Bayesian Maximum Entropy (BME) is increasingly used in predicting and mapping spatio-temporal data. However, studies that have fully evaluated its robustness empirically are rare. Therefore, this research examined empirically the effect of skewness, sample size and spatial dependency level using simulated data. We considered symmetric data, data positively skewed by 1, 3, 6 and 9, data with weak, moderate, and strong spatial dependency levels, and sample sizes from 100 to 500 at the interval length of 50. The results showed that the variation of sample sizes and spatial dependency levels do not affect the Mean Square Error (MSE) and bias of BME prediction. However, skewness affects the MSE of prediction but does not affect the bias. This result indicates that BME is robust to sample size and is unbiased. Despite the significant difference due to skewness, a graphical plot showed values of MSE close to zero, suggesting that BME can be considered robust to skewness.

PrEP uptake in the Netherlands is growing but remains at suboptimal levels. Hence, the analysis of hurdles is paramount. Given the initial focus of PrEP provision among men-who-have-sex-with-men (MSM) via a demonstration project that was launched in June 2015, AmPrEP in Amsterdam, and pharmacies in the main urban areas (so called “Randstad”, entailing Amsterdam, Utrecht, Leiden, The Hague and Rotterdam), investigating regional differences is necessary. This study seeks to unravel regional differences jointly with psycho-social determinants of PrEP uptake. This cross-sectional study included 3,232 HIV-negative Dutch MSM recruited via the EMIS survey in late 2017. Prevalence and standardized prevalence ratio (SPR) of PrEP awareness, intention and uptake were measured on a regional level (Randstad vs. the rest of the country). Multilevel logistic modelling was conducted to identify the association of PrEP uptake with PrEP awareness and intention, sociodemographic, psycho-social determinants, and random effects from regional differences. MSM from the Randstad used more PrEP (SPR=1.4 vs. 0.7) compared to the rest of the country, but there were minor differences for awareness and intention. The regional distinction was estimated to explain 4.6% of the PrEP use variance. We observed a greater influence from PrEP intention (OR=4.5, 95%CI 2.0-10.1), while there was limited influence from the awareness of PrEP (OR=0.4, 95%CI 0.04-4.4). Lower education (OR=0.4, 95%CI 0.2-0.9) was negatively associated with PrEP uptake, however, no significant difference was found between middle and high education (OR=1.2, 95%CI 0.7-2.0). We showed that regional differences – MSM in non-urban regions – and other psycho-social determinants account for lower PrEP uptake. Based on these findings, more fine-tuned PrEP access with a focus on non-urban regions can be implemented, and tailored campaigns increasing intention/use can be conducted among target populations.

3D city models integrate heterogeneous urban data from multiple sources in a unified geospatial representation, combining both semantics and geometry. Although in the last decades, they are predominantly used for visualization, today they are used in a large range of tasks related to exploration, analysis, and management across multiple domains. The complexity of urban processes and the diversity of urban environment bring challenges to the implementation of 3D city models. To address such challenges, this paper presents the development process of a 3D city model of a single neighborhood in Sofia city based on CityGML 2.0 standard. The model represents the buildings in LOD1 with a focus on CityGML features of related to the buildings like building part, terrain intersection curve and address. Similar building models of 18 cities provided as open datasets are explored and compared in order to extract good modeling practices. As a result, workflows for generation of 3D building models in LOD1 are elaborated and improvements in the feature modeling are proposed. Two options of building model are examined: modeling of a building as a single solid and modeling of a building with separate building parts. Finally, the possibilities for visualization of the model in popular platforms such as ArcGIS Pro and Cesium Ion are explored.

A class of models for non-Gaussian spatial random fields is explored for spatial field reconstruction in environmental and sensor network monitoring. The family of models explored utilises a class of transformation functions known as the Tukey g-and-h transformations to create a family of warped spatial Gaussian process models which can support various desirable features such as flexible marginal distributions, which can be skewed, leptokurtic and/or heavy-tailed. The resulting model is widely applicable in a range of spatial field reconstruction applications. To utilise the model in applications in practice, it is important to carefully characterise the statistical properties of the Tukey g-and-h random fields. In this work, we both study the properties of the resulting warped Gaussian processes as well as using the characterising statistical properties of the warped processes to obtain flexible spatial field reconstructions. In this regard, we derive five different estimators for various important quantities often considered in spatial field reconstruction problems. These include the multi-point Minimum Mean Squared Error (MMSE) estimators; the multiple point Maximum A-Posteriori (MAP) estimators; an efficient class of multiple-point linear estimators based on the Spatial-Best Linear Unbiased (S-BLUE) estimators; and two multi-point threshold exceedance based estimators, namely the Spatial Regional and Level Exceedance estimators. Simulation results and real data examples show the benefits of using the Tukey g-and-h transformation as opposed to standard Gaussian spatial random fields in a real data application for environmental monitoring.

Abstract: Service polycentricism, in the face of the pandemic, took on a new significance because of sanitary requirements. Population concentrations in large service centres face a high risk of infection both at their destination and during the trip. Higher-tier services were largely closed during the threat’s peak. In this paper, the focus of the study has been placed on the urban scale of a large city’s polycentricism as seen through the prism of service concentrations, which, to a degree, are commuter destinations, but primarily act as attractors that are not associated with work—they satisfy higher-tier service needs. To formulate the investigative apparatus used to study the functio-spatial structure of Cracow. The current threat, as well as the high probability of similar situations happening in the future, will probably lead to changes in behaviour patterns of consumers and service providers in reference to siting. Local service centres that are accessible to pedestrians, due to safety-related considerations, can gain significant popularity.

The reality of people’s lives has shifted from rural to urban areas, where an ever-increasing proportion of the world’s population lives. Providing infrastructure to serve these areas, especially in the Global South, is a key task of sustainable development. A deep understanding of the spatial arrangement and scales of these urban structures and their temporal evolution can help to develop innovative solutions to issues of energy, water, or transportation infrastructures. For this purpose, in this work we study the temporal evolution of urban built-up structures (Global Artificial Impervious Area) and population distributions (Global Human Settlement Population) in four regions of the Global South (Argentina, India, Egypt, and Nigeria). We qualitatively analyze regularity through the pair correlation function and subsequently identify typical scales within the different interurban systems. In doing so, we identify that especially the large settlement objects arrange themselves in a regular way and thus typical scales exist in urban systems. Thus, settlement objects are usually located about 20 to 40 km apart from each other. This information can be used to develop sustainable infrastructure concepts, for example for passenger transport between settlements.

The interest in spatial analysis has been growing in recent years, mainly due to communication technology advance, economic globalization, and the development of new statistical and econometric methods. The main aim of this article is to contribute to the dissemination of spatial econometric applications by presenting some basic theoretical aspects and a literature review of articles that address the socio-economic drivers that lead to environmental pollution. Three spatial regression models are reviewed here: the spatial lag model (SLM), the spatial error model (SEM), and the spatial Durbin model (SDM). A literature search was conducted using specific terms of interest in eight databases, from 1996 to February 2021, where 22 articles were considered for analysis. The results showed that most articles studied environmental problems in China. The most used exploratory spatial analysis model was Moran Index and the most used explanatory spatial analysis models were SDM and SLM.

Spatial light modulators (SLMs) have been widely used to achieve dynamic control of optical traps. Often, holographic optical tweezers have been presumed to provide nanometer or sub-nanometer positioning accuracy. It is known that some features concerning the digitalized structure of SLMs cause a loss in steering efficiency of the optical trap, but their effect on trap positioning accuracy has been scarcely analyzed. On the one hand, the SLM look-up-table, which we found to depend on laser power, produces positioning deviations when the trap is moved at the micron scale. On the other hand, phase quantization, which makes linear phase gratings become phase staircase profiles, leads to unexpected local errors in the steering angle. We have tracked optically-trapped microspheres with sub-nanometer accuracy to study the effects on trap positioning, which can be as high as 2 nm in certain cases. We have also implemented a correction strategy that enabled the reduction of errors down to 0.3 nm.

Sediment accumulation in a dam reservoir is a common happening environmental problem throughout the world. Topographic conditions, land use land cover change, the intensity of rainfall, and the soil characteristics are the major driving factors for sedimentation to occur. The effect of sedimentation in a dam reservoir is very visible in the watershed as a result of hilly topographic conditions, high rainfall intensity, thin land cover, and less soil infiltration capacity. In this paper, an integrated RUSLE and GIS technique was implemented to estimate a mean annual sediment yield based on spatial and temporal variations in Nashe dam reservoir situated in Fincha catchment, Abaya River basin, Ethiopia. Spatial and temporal estimation of mean annual sediment yield was estimated using the Revised Universal Soil Loss Equation (RUSLE) model and GIS. Historical 6-year (2014-2019) rainfall for the temporal variations and other physical factors such as soil erodibility, slope and length steepness, management and land used land cover, and support practice for spatial variations were used as sediment driving factors. The mean annual sediment yield ranges from 0 to 2712.65 t ha-1 year-1 was seen. Spatially, Very high, high, moderate, low, and very low sediment yield severity with total area coverage with 25%, 10%, 30%, 15%, and 20% in 2017, 2015, 2019, 2014, and 2018 respectively. The information about the spatial and temporal variations of the severity of sediment yield in RUSLE model has a paramount role to control the entry of sediment into the dam reservoir in this watershed. The results of the RUSLE model can also be further considered along with the watershed for planning strategies for dam reservoirs in the catchment.

This paper discusses Green Infrastructure, which can be considered a useful tool in the process of ensuring the sustainable development of urban structures in the Carpathian region. It allows for achieving a better quality of the environment of human life and healthy wildlife linkages. The element that supports defining information about the existing state of Green Infrastructure and its resources is the Green Infrastructure fragmentation coefficient based on edge effect calculations, which is the relation between the edge of the patch (circumference) to its surface area [1, 2]. With the use of model analysis of Green Infrastructure, it is possible to implement the provisions of the Carpathian Convention and coordinate planning documents that facilitate the sustainable development of spatial structures. Our study on the state of Green Infrastructure in rural areas of the Polish Carpathian Mountains is a source of knowledge about the quality of this area, its natural environment and fragmentation. Determining the territory’s Green Infrastructure fragmentation coefficient provides an opportunity for higher-precision studies and the detection of threats and integration of GI fragments and addressing proper solutions in conflict areas.

Numerous cities in our modern world are unfortunately encountering the negative effects of urban sprawl: this includes unrestricted settlement, degradation in the quality of their environment, traffic congestion, sub-standard buildings, and air pollution as well as flooding, swampy areas, landslides, and settlement zones with dilapidated utilities and infrastructures that are not safe for living. The Ulaanbaatar City land management master plan defined the settlement zone area suitable for living as 33,698 hectares. However, due to unrestricted urban sprawl caused by exponential growth of the city’s population, the settlement zone area reached 39,235 hectares, which exceeds the limit by 5,537 hectares. In order to tackle this issue, several urban planning concepts were developed to be implemented within the Ulaanbaatar City urban planning framework. It is, in any case, problematic to choose a single planning concept due to the fact that neither measurements nor analyses are being made of the respective spatial quantitative indicators in urban planning assumptions that are taking the current situation into consideration. One of the prerequisites for identifying an optimal concept in urban planning is an assessment of the current situation, and measuring the impacts against its quantitative data. In the current research, when defining Ulaanbaatar city sprawl, the base year was selected as 1990, the time when the city started to sprawl. Research analyses were made using geographic information systems based on the satellite data 1990, 1995, 2000, 2005 Landsat ТМ-5, 2010 Quickbird, 2015 World View and 2020 Sentinel2, respectively. Based on the results of determining the city sprawl using spatial indicators, the urban planning concepts applied thus far have been analyzed in relation to land use efficiency and land use structural changes. This research paper addresses the issue of reducing unrestricted urban sprawl by increasing the internal density of the city. The research results show that, by applying the concept of a compact city in urban redevelopment planning for 4,604 hectares, and by allocating the settlements in 12,479 hectares, it is possible to reduce the urban expansion threefold and increase land use efficiency accordingly.

A descriptive epidemiological study has been conducted using hospital database of Teaching Veterinary Hospital (TVH) at Jhenaidah Government Veterinary College (JGVC) from July 2018 to June 2019. The study aimed to estimate the proportionate prevalence of different livestock and poultry diseases along with their spatiotemporal distribution and drug prescribing pattern. A total of 960 clinical cases were recorded during the study period. Ectoparasitic cases were proportionately higher in cattle (25.2%), whereas Peste des Petits Ruminants (PPR) cases were more frequent in goat (53.4%). The proportionate prevalence of other cases in cattle was Fascioliasis (14.3%), Myiasis (11.2%) and Foot and Mouth Disease (FMD) (7.2%). The proportionate prevalence of other cases in goats were vitamin and mineral deficiency (12.3%), bloat (5.2%), abscess (4.7%), and dog bite (1.2%). Again, the proportionate prevalence of poultry diseases was Infectious Bursal Disease (41.2%), salmonellosis (33.4%), fowl cholera (13.7%) and pox (7.8%). Most of the cattle cases were highly prevalent during the summer season except fascioliasis. In goat, PPR was predominated in the rainy season whereas myiasis was in the winter. Around 92% of disease cases were spatially located within the 2.5 km radius of the TVH of JGVC where only 0.9% of disease cases came from >10km away from TVH of JGVC. Simple linear regression identified a significant relation (p=0.01) with the distance and number of diseased animals came to the hospital. Antimicrobials belonging to b-Lactam group were most frequently prescribed for both poultry (48.6%), cattle (32.5%) followed by goat (9.2%), however sulfar drugs (34.8%) were commonly prescribed for goat cases. This type of study is very novel in Jhenaidah region of Bangladesh that might contribute to the researchers for further investigation.

Spatial modeling in Geographic Information Systems (GIS) always involves choices. The existence of constraints, either of a financial nature or related to the specifics of the software itself, to the algorithms, the uncertainty and even the reliability of the data, the purposes and the applications of the studies, make this a kind of guiding compass for GIS analysts. Building on a previous exercise of data acquisition (check-ins) based on two Digital Social Networks (DSN—Facebook and Foursquare) and on the awareness of the use of voluntary geographic information generated by tourists sharing their topophilic ties through DSN, the present analysis aims to evaluate the contribution of modern techniques of spatial analysis applied to tourism in the “Alta and University of Coimbra” area. Concepts and procedural tasks related to density determination, cluster analysis and identification of patterns associated with regionalized variables have thus been implemented with the purpose of evaluating and comparing the results obtained through the application of two techniques of spatial analysis, Kernel Density Estimation (KDE) and Optimized Hot-Spot Analysis (OHSA) & Inverse Distance Weighting (IDW) Interpolation.

The present study examines inner and outer suburban ring attainment outcomes among racial and ethnic groups residing in the nation’s metropolitan areas. The main objective is to evaluate the extent to which the relationship between racial and ethnic group’s socioeconomic status characteristics and residence between inner and outer suburban rings conforms to the tenets of the spatial assimilation model. Using micro-level data from the 5-year 2012-2016 American Community Survey, the author calculates multinomial logistic regression models to determine the effects of SES and other relevant predictors on residence within the nation’s metropolitan area’s suburban inner and outer rings. The results both confirm and contradict the main tenets of the spatial assimilation model. To the extent that income, education, and homeownership are positively related to residence in both suburban rings, the findings also suggest that access to inner and outer rings is hierarchically stratified by race and ethnicity.

This study was designed to assess variation of possible phytochemical compounds in the ethanolic extracts of Gigantochloa scortechinii rhizome. Destructive sampling was done by using selective random sampling method on four consecutive rhizomes from healthy clumps and was conducted at two natural forests and one secondary forest (planted). Homogenized sample were extracted using solvent extraction (70% ethanol) method. Ethanolic extracts of G. scortechinii rhizome were qualitatively analyzed using GC/MS GC2010 Plus, Shimadzu to determine the composition of phytochemical compounds and identified using FFNSC 1.3, NIST11, PMW_tox2, and Wiley229 spectral library. A qualitative variation was observed with a total of 56 compounds were identified and differentiated between study site and rhizome age. Results revealed that G. scortechinii rhizome contains various phytochemical compounds with potential as a plant of phytopharmaceutical importance. This is the first finding on the spatial and age-related effects of phytochemical compounds in a consecutive rhizome and of G. scortechinii rhizome specifically.

A basic pattern in the body plan architecture of many animals, plants and some molecular and cellular systems is five-part units. This pattern has been understood as a result of genetic blueprints in development and as a widely conserved evolutionary character. Despite some efforts, a definitive explanation of the abundance of pentagonal symmetry at so many levels of complexity is still missing. Based on both, a computational platform and a statistical spatial organization argument, we show that five-fold morphology is substantially different from other abundant symmetries like three-fold, four-fold and six-fold symmetries in terms of spatial interacting elements. We develop a measuring system to determine levels of spatial organization in 2D polygons (homogeneous or heterogeneous partition of defined areas) based in principles of regularity in a morphospace. We found that spatial organization of five-fold symmetry is statistically higher than all other symmetries studied here (three to ten-fold symmetries) in terms of spatial homogeneity. The significance of our findings is based on the statistical constancy of geometrical constraints derived from spatial organization of shapes, beyond the material or complexity level of the many different systems where pentagonal symmetry occurs.

We show that an experimentally plausible system consisting of a modulated wire medium hosted in a metal cavity can preserve the longitudinal field profile shaping predicted by Boyd et al. (2018) on the basis of a perfectly periodic wire-only structure. These new frequency domain numerical results are a significant step towards justifying the construction of an experimental apparatus to test the field profile shaping in practise.

An adaptive spatial clustering (ASC) algorithm is proposed in this present study, which employs sweep-circle techniques and a dynamic threshold setting based on the Gestalt theory to detect spatial clusters. The proposed algorithm can automatically discover clusters in one pass, rather than through the modification of the initial model (for example, a minimal spanning tree, Delaunay triangulation or Voronoi diagram). It can quickly identify arbitrarily-shaped clusters while adapting efficiently to non-homogeneous density characteristics of spatial data, without the need of prior knowledge or parameters. The proposed algorithm is also ideal for use in data streaming technology with dynamic characteristics flowing in the form of spatial clustering in large data sets.

Loss of function of the hippocampus or frontal cortex is associated with reduced performance on memory tasks in which subjects are incidentally exposed to cues at specific places in the environment and are subsequently asked to recollect the location at which the cue was experienced. Here, we examined the roles of the rodent hippocampus and frontal cortex in cue-directed attention during encoding of memory for the location of a single incidentally experienced cue. During a spatial sensory preconditioning task, rats explored an elevated platform while an auditory cue was incidentally presented at one corner. The opposite corner acted as an unpaired control location. The rats demonstrated recollection of location by avoiding the paired corner after the auditory cue was in turn paired with shock. Damage to either the dorsal hippocampus or the frontal cortex impaired this memory ability. However, we also found that hippocampal lesions enhanced attention directed towards the cue during the encoding phase while frontal cortical lesions reduced cue-directed attention. These results suggest that the deficit in spatial sensory preconditioning caused by frontal cortical damage may be mediated by inattention to the location of cues during the latent encoding phase, while deficits following hippocampal damage must be related to other mechanisms such as generation of neural plasticity.

Visualizing geo-spatial data embedded into a three-dimensional terrain is challenging. The problem becomes even more complex when uncertainty information needs to be presented as well. This paper addresses the question of how to visually communicate all three aspects: the 3D terrain, the geo-spatial data, and the data-associated uncertainty. We argue that visualizing all aspects with a high degree of detail will likely exceed the visual budget. Therefore, we propose a visualization strategy based on prioritizing a selected aspect and presenting the remaining two with less detail. We discuss various design options that allow us to obtain differently prioritized visual representations. Our approach has been implemented as a tool for rapid visualization prototyping in the context of avionics applications. Practical solutions are described for a use case related to the visualization of 3D terrain and uncertain weather data.

Moso bamboo is famous for fast growing and biomass accumulation, as well as high annual output for timber and bamboo shoots. These high outputs require high nutrient inputs to maintain and improve stand productivity. Soil nitrogen (N), phosphorus (P) and potassium (K) are important micronutrients for plant growth and productivity. Due to high variability of soils, analysing spatial patterns of soil N, P and K stocks is necessary for scientific nutrient management in Moso bamboo forests. In this study, soils were sampled from 138 locations across Yong’an City and ordinary kriging was applied for spatial interpolation of soil N, P and K stocks. Soil N stock showed a strong spatial dependence while soil N and P stocks presented a moderate spatial dependence, indicating soil N was mainly controlled by intrinsic factors while soil N and P stocks were controlled by both intrinsic and extrinsic factors. Different spatial patterns were observed for soil N, P and K stocks across the whole study area, indicating that fertilizations with different ratios of N:P:K should be applied for different sites to maintain and improve stand productivity. The total soil N, P and K stocks within 0-60 cm were 0.624, 0.020 and 0.583 Tg, respectively.

Ecosystem physical structure, defined by the quantity and spatial distribution of biomass, influences a range of ecosystem functions. Remote sensing tools permit the non-destructive characterization of canopy and root features, potentially providing opportunities to link above- and belowground structure at fine spatial resolution in functionally meaningful ways. To test this possibility, we employed ground-based portable canopy lidar (PCL) and ground penetrating radar (GPR) along co-located transects in forested sites spanning multiple stages of ecosystem development and, consequently, of structural complexity. We examined canopy and root structural data for coherence at multiple spatial scales ≤ 10 m within each site using wavelet analysis. Forest sites varied substantially in vertical canopy and root structure, with leaf area index and root mass more evenly distributed by height and depth, respectively, as forests aged. In all sites, above- and belowground structure, characterized as mean maximum canopy height and root mass, exhibited significant coherence at a scale of 3.5-4 meters, and results suggest that the scale of coherence may increase with stand age. Our findings demonstrate that canopy and root structure are linked at characteristic spatial scales, which provides the basis to optimize scales of observation. Our study highlights the potential, and limitations, for fusing lidar and radar technologies to quantitatively couple above- and belowground ecosystem structure.

As part of the Copernicus programme of the European Union (EU), the European Space Agency (ESA) has developed and is currently operating the Sentinel-2 mission that is acquiring high spatial resolution optical imagery. This paper provides a description of the calibration activities and the current status of the mission products validation activities. Measured performances, from the validation activities, cover both Top-Of-Atmosphere (TOA) and Bottom-Of-Atmosphere (BOA) products. Results presented in this paper show the good quality of the mission products both in terms of radiometry and geometry and provide an overview on next mission steps related to data quality aspects.

Distributed/semi-distributed models are considered to be sensitive to the spatial resolution of the data input. In this paper, we take a small catchment in high urbanized Yangtze River Delta, Qinhuai catchment as study area, to analyze the impact of spatial resolution of precipitation and the potential evapotranspiration (PET) on the long-term runoff and flood runoff process. The data source includes the TRMM precipitation data, FEWS download PET data, and the interpolated metrological station data. GIS/RS technique was used to collect and pre-process the geographical, precipitation and PET series, which were then served as the input of CREST (Coupled Routing and Excess Storage) model to simulate the runoff process. The results clearly showed that, the CREST model is applicable to the Qinhuai catchment; the spatial resolution of precipitation had strong influence on the modelled runoff results and the metrological precipitation data cannot be substituted by the TRMM data in small catchment; the CREST model was not sensitive to the spatial resolution of the PET data, while the estimation fourmula of the PET data was correlated with the model quality. This paper focused on the small urbanized catchment, suggesting the influential explanatory variables for the model performance, and providing reliable reference for the study in similar area.

Categorical variables are common in spatial data analysis. Traditional analytical methods for deriving probabilities of class occurrence, such as kriging-family algorithms, have been hindered by the discrete characteristics of categorical fields. This study introduces the theoretical backgrounds of linear Bayesian updating (LBU) approach for spatial classification through expert system. Transition probabilities are interpreted as expert opinions for updating the prior marginal probabilities of categorical response variables. The main objective of this paper is to present the solid theoretical foundations of LBU and provide a categorical random field prediction method which yields relatively higher classification accuracy compared with conventional Markov chain random field (MCRF) approach. A real-world case study has also been carried out to demonstrate the superiority of our method. Since the LBU idea is originated from aggregating expert opinions and not restricted to conditional independent assumption (CIA), it may prove to be reasonably adequate for analyzing complex geospatial data sets, like remote sensing images or area-class maps.

This article attempts to identify factors impacting on the quantity of municipal waste in Polish 2478 communes (LAU-2), taking into account the variability of particular determinants influence depending on their regional diversification. The analysis covers the years 2005-2018. The dependent variable is the volume of municipal waste in kg per capita, whereas the group of determinants include: economic and human development, uncontrolled dumping sites, population density, population at the working age, migration, tourism, urbanization, dwellings and housing, retail sales, entities, education and investments in waste management; The geographically weighted regression with spatial error term (GWR-SEM) was employed in this study. The model enabled not only the specification of the waste production determinants, but also the analysis of the variability in the strength and direction of dependencies occurring between the examined variables in individual communes. The results proved that the higher the level of education the less waste is generated (in the north-central Poland); the business entities and working-age population are crucial for the waste quantity in communes of eastern Poland; the factors most considerably as to regional range affecting the waste quantity are urban and business development; as to strength–higher education and the share of working age individuals;

After the launch of a high-resolution remote sensing satellite, representative spatial quality estimators (RER, FWHM, MTF50, MTFA) are measured from images taken of ground Edge targets. In this work, the best spatial quality estimator is proposed by quantitatively comparing and analyzing the precision between Relative Edge Response (RER), Full Width at Half Maximum (FWHM), MTF value at Nyquist frequency (MTF50), and MTF Area between 0 and Nyquist frequency (MTFA). While the basic method for the measurement of spatial quality estimators on Edge targets is already well established, this work summarizes and explains the uncertain factors and problems in the measurement procedure that affect the accuracy and precision of spatial quality estimators. It also considers how to improve the precision of spatial quality estimators during the measurement procedure. The contents and results of this work were discussed by various satellite development organizations in the Geo-Spatial Working Group within CEOS WGCV IVOS from 2012 to 2019, and the Edge target Spatial quality Measurement Python code (ESMP) was developed in 2019 to reflect the findings of this workshop. Using 483 Edge targets from worldwide images taken by KOMPSAT-3A, which has been in operation since 2017, the results obtained via ESMP show that the precision of RER, FWHM, and MTFA are approximately three to four times higher than that of MTF50 when comparing the Coefficient of Variance (CV) statistics.

This study uses Historical Geographic Information Systemsand spatial analysis methods like Space Syntax and Kernel Density Estimation to examine the urban spatial evolution of Xi'an from the Five Dynasties (907 CE) to the early People's Republic of China (1949 CE). By focusing on six historical periods, it reveals the city's transition from a centralized layout to a decentralized, multi-center form. The research shows that Xi'an's spatial structure was shaped by political, economic, cultural, and environmental forces. The Ming and Qing dynasties, in particular, saw economic diversification and the influence of the Silk Road, driving the city's outward expansion and the formation of multiple functional zones. The study also discusses how these historical changes offer valuable lessons for modern urban planning, particularly in balancing cultural heritage preservation with modernization. Overall, the findings provide fresh insights into the long-term urban development of historic cities and offer practical strategies for modern urban planning.

In recent years, the "3-30-300" concept by Professor Cecil Konijnendijk has been gaining popularity, outlining what makes a city "green" and what we should strive for. This concept suggests that from every apartment, at least 3 trees should be visible, 30% of the city's surface should be covered with greenery, and the nearest park or forest should be no more than 300 meters away. However, the lack of detailed guidelines makes the implementation of this concept a significant challenge. The goal of the research presented here was to adapt this concept for Warsaw (Poland) and assess whether it can be considered a green city. I defined parameters such as the maximum distance for visible trees as 20 meters and determined what counts toward the 30% green areas. The results showed that 57.82% of apartments in Warsaw have a view of at least 3 trees, while only 5.44% do not meet this criterion, and the rest meet it only partially. Parks and forests cover 19.95% of the city, while all green areas combined cover 42.01%. Additionally, 45% of buildings are located within 300 meters from a park or forest. Ultimately, full compliance with the "3-30-300" concept applies to 22.19% of buildings, while only 12.66% meet the criteria when considering only parks and forests. This indicates that, while Warsaw is relatively green, not all of its areas fulfill these criteria.

A study was conducted to determine the estimated parasitic infestation and associated risk factors (Water quality, Farm management practices and External factors such as Intermediate hosts and Wild fish entry) of farmed Nile tilapia in Pond grow-out, Cage grow-out (lake), Cage grow-out (reservoir) farms and Hatcheries in the Lake Victoria Crescent, Uganda. Sixteen parasite genera and 65% (418/640) infestation rate were obtained. However, Pond grow-out farms and Hatcheries did not only constitute 81% and 63% of the parasite genera, but also had the highest mean number of parasite genera per farm of 1 to 7 and 4 to 8 respectively. Cage grow-out (reservoir) farms and Hatcheries had the highest mean prevalence of >0.7. Water quality parameters, farm management practices and external factors varied across the 4 farming systems, with Cage grow-out farms (lake) with the best water quality parameters, farm management practices and control over intermediate hosts and wild fish entry. Using Spatial Areal Unit Modelling with Conditional Autoregressive Priors, out of 16 risk factors, only Intermediate hosts had a significant effect on estimated parasitic infestation. Estimated parasitic infestation of 0.28 (low) or 0.55 (high) prevalence was obtained. All Cage grow-out (reservoir) farms and Hatcheries, and 78% (14/18) of Pond grow-out farms had high estimated parasitic infestation despite their locations, while 75% (3/4) of Pond grow-out farms that had low estimated parasitic infestation were located in Masaka (an area with many minor and less polluted rivers). Fifty-six percent (5/9) of Cage grow-out (lake) had low estimated parasitic infestation and were located in the Southern part of Wakiso, Southern part of Mukono and Southern part of Jinja but far from the Napoleon Gulf. The Cage grow-out farms with high estimated parasitic infestation were found in the Southeastern part of Buikwe (an area with sugar cane plantations and factories) and Southern part of Jinja in the polluted Napoleon Gulf. Therefore, the type of farming system and its location (spatial component) need to be given at-most importance when coming up with management and sanitary control strategies to encounter parasite infestation.

Transport systems are essential in the path towards sustainable urbanisation and the transition to more sustainable living. Recently, European cities have undergone substantial changes, owing to which suburbanisation is posing new challenges. Suburban areas are often more affordable in terms of housing, but their neighbourhoods tend to be car-oriented. This leads to higher commuter costs, immobility, transport and time poverty, pollution, and accident rates and a lack of social interaction. To offer sustainable mobility options to citizens, we must comprehensively understand their specific mobility practices, together with their personal characteristics, and the built environment. This study centred on the Barcelona Metropolitan Region, which has a public transport network that covers the entire area. The aim of this study was to examine the relationship between travel behaviour, transport mode use, individual characteristics, and place of residence, using detailed information sources. Herein, we used data from the annual travel survey conducted in the Barcelona region from 2018 to 2021, together with land-use and other information sources. Our findings suggest that transport policies have encouraged sustainable mobility practices, particularly in the centre of Barcelona. Despite the positive results, considerable disparities exist between the inner and outer city, with notably decreased sustainable mobility practices in the latter. Our results indicate that promoting sustainable mobility policies requires further changes in transport, city, and land-use planning that consider equity, the socioeconomic profile of citizens, and mixed urban planning.

This study was conducted to identify the actual degree of underdevelopment and spatial characteristics of rural areas in Korea, which are expected to face extinction due to population decline and aging. For this purpose, the spatial scope was set to Eup and Myeon areas classified as rural regions. To assess the degree of underdevelopment, previous studies were reviewed and analyzed to develop a comprehensive underdevelopment index, which was then applied to Eup and Myeon areas to classify them into growth regions, general regions, and depressed regions. Mapping the classified regions revealed that both growth and general regions were concentrated in the metropolitan area and southeastern regions, where Korea's population and resources are densely clustered. In contrast, depressed regions were found primarily in areas where agriculture is the main industry and in regions with unfavorable geographical conditions. This study is significant as it covers the entire nation of Korea, providing a method for identifying depressed regions and highlighting the regions that are developing. The findings can serve as fundamental data for the development of policies to address population decline in the future.

The flooding phenomenon in Pekanbaru City is an interrelated problem between socio-economic and environmental problems. Changes or conversion of land from non-built areas to residential areas or other uses result in the area of water catchment areas being reduced. Another no less important factor is socio-economic factors. One of the socio-economic problems that causes flooding every year in Pekanbaru City is waste management. Garbage thrown carelessly by Pekanbaru people obstructs drainage channels and river flows and causes flooding. Another socio-economic problem is water use behavior. The majority of water used for daily needs, bathing and washing toilets comes from groundwater. The use of groundwater in large and continuous quantities results in land subsidence and contributes to an increase in flood inundation. Analysis of spatial planning strategies and community behavior towards flood control efforts using Participatory Prospective Analysis (PPA). Prospective analysis in this study was used to determine the dominant variables that influence strategy determination. This method is a tool designed to find out or investigate and anticipate changes with the participation of experts or stakeholders. The prospective method is a tool used for policy analysis, especially in research involving sustainable development, because it can facilitate the anticipation of change in an unstable environment. The results of the research show that spatial planning strategies and community behavior in flood control are carried out with the coordination of technical stakeholders related to flood control, flood management includes before a disaster occurs, during a disaster and after a disaster, flood plain management, includes determining flood boundaries, determining zoning, supervision and monitoring as well as community involvement.

Greenhouse gas (GHG) emission-induced climate change, particularly occurring since the mid-20th century has been considerably affecting short-term weather conditions, such as increasing weather variability, and incidence of extreme weather-related events. Milk production is sensitive to such changes. In this study, we use spatial panel econometric models, Spatial Error Model (SEM) and Spatial Durbin Model (SDM), with a panel dataset at the state-level varying over seasons, to estimate the relationship between weather indicators and milk productivity, in an effort to reduce the bias of omitted climatic variables that can be time-varying and spatially correlated and cannot be directly captured by conventional panel data models. We find an inverse U-shaped effect of summer heat stress on milk production per cow (MPC), indicating that milk production reacts positively to a low-level increase in summer heat stress, and then MPC declines as heat stress continues increasing beyond a threshold value of 72. Additionally, fall precipitation exhibits an inverse U-shaped effect on MPC, showing that milk yield increases at a decreasing rate until fall precipitation rises to 14 inches and then over that threshold milk yield declines at an increasing rate. We also find that, relative to conventional panel data models, spatial panel econometric models could improve prediction performance by leading to smaller in-sample and out-sample root mean squared errors. Our study contributes to the literature by exploring the feasibility of promising spatial panel models and resulting in estimating weather influences on milk productivity with high model predicting performance.

The exponential growth of user-contributed data provides a comprehensive basis for assessing collective perceptions of landscape change. A variety of possible public data sources exist, such as geospatial data from social media or Volunteered Geographic Information (VGI). Key challenges with such ‘opportunistic’ data sampling are variability in platform popularity and bias due to changing user groups and contribution rules. In this study, we use five case studies to demonstrate how intra- and inter-dataset comparisons can help to assess the temporality of landscape scenic resources, such as identifying seasonal characteristics for a given area, or testing hypotheses about shifting popularity trends observed in the field. By focusing on the consistency and reproducibility of temporal patterns for selected scenic resources and comparisons across different dimensions of data, we aim to contribute to the development of systematic methods for disentangling the perceived impact of events and trends from other technological and social phenomena included in the data. The proposed techniques may help to draw attention to overlooked or underestimated patterns of landscape change, fill in missing data between periodic surveys, or corroborate and support field observations. Despite limitations, the results already provide a comprehensive basis for developing indicators with a high degree of timeliness for monitoring perceived landscape change over time.

Although the influence of climate on the fire regime is unanimously recognized, most publications are studies on this influence are at the global scale. Therefore, this study aims to demonstrate the role of climate in the incidence of wildfires at the country and regional scale using statistical analysis and machine learning methods. Mainland Portugal was chosen as a case study due to its climate and because it is the European region most affected by wildfires. Results demonstrate the climate signature in the spatial and temporal distribution of the wildfire incidence. The conclusions of the study include (i) the existence of two pyro-regions, with different types of climate (Csb and Csa) composed of NUTS II regions: the northern region composed of the Norte and Centro regions and the southern region composed of Alentejo and Algarve; (ii) the intra-annual variability in the incidence of wildfires, characterized by two peaks, one in spring and the other in summer, are a consequence of the country's type of climate; and, (iii) how the annual cycle of wildfire incidence varies over the years depends on the weather conditions throughout each year. These results are of fundamental importance for wildfire managers, especially in the context of climate change.

This study presents a computational simulation to explore the distinctive patterns of neural connectivity in Autism Spectrum Disorder (ASD), focusing on the concepts of spatial and topological proximities. ASD is characterized by unique cognitive and behavioral profiles, often attributed to atypical neural development and connectivity. Our simulation employs a two-pronged approach: first, by modeling spatial proximity through the generation of clustered 3D node positions, we mimic the observed over-connectivity in localized brain regions in ASD. Second, we construct a topological network to represent the complex interplay of under-connectivity and over-connectivity in functional brain networks, a hallmark of ASD's neural landscape. The spatial model demonstrates pronounced clustering, reflecting the structural brain differences and atypical growth patterns reported in neuroimaging studies of individuals with ASD. In contrast, the topological model reveals a network with a mix of sparse and dense connections, simulating the diverse and often contradictory findings of functional connectivity in ASD, such as reduced long-range connections and increased local connections. Our simulation provides a visual and conceptual framework for understanding the altered neural connectivity in ASD. It underscores the importance of considering both physical (spatial) and functional (topological) aspects of brain connectivity to grasp the full extent of neurodevelopmental deviations in ASD. This approach not only aids in elucidating the neural underpinnings of ASD's core symptoms but also offers a foundation for developing targeted interventions. Future research, integrating more complex and individual-specific data, could further refine this model, enhancing our understanding of ASD's neural dynamics and contributing to personalized therapeutic strategies.

To study the spatial accessibility of healthcare services in cities on the Tibetan Plateau, this paper proposes an improved 3SFCA method in the main urban area of Xining City. The improved 3SFCA method incorporates both selection weights from the Huff model and the M2SFCA approach to addressing suboptimal allocation of supply resources. Results show that the accessibility of healthcare for the main urban area of Xining City decreases in a circle to four axes, with good accessibility in the east and center but poor accessibility in the north. And there is a positive spatial relationship between population and accessibility. Besides, this paper proposes a framework for studying the spatial accessibility of healthcare on the Tibetan Plateau.

During the last few years, advancement in the area of biochemistry, science of the material world, engineering and computer-aided testing has directed towards advancement of high-throughput tools for the sake of profiling information encoded in a gene. Single-cell RNA-sequencing (scRNA-seq) tools capable to examine the sequence data from each individual cells and it shows within population variety and permit exploring of cell conditions and transformation by extreme resolution, possibly give out cell sub-types or gene expression fluctuations that are shaded in mass sequencing processes, which shows population-averaged evaluations. Yet, the major disadvantage for this tool is the lack of success to pick out location related details of the RNA transcriptome, as this needs tissue detachment and cell isolation. Location based transcript determination is among the advancements in the area of medical biotechnology as this can find out the molecules like RNA dataset in their intact physical placement in tissue segment with spatial context at the scale of a single-cell, which is very advantageous as compare to single-cell sequencing techniques. These approaches give key observation in the areas of biomedical field sub-disciplines like neurology, embryology, carcinoma study, immune cell investigation and histological activities. Generally, this review mainly focused towards single-cell sequencing methods, technology development, its challenges observed, different expression data analysis mechanisms and their applications in different areas, such as in cancer research, microbes, central nervous system, sex organs and immune-biology, intensifying the essentiality of sequencing tools at single-cell level for the characterization of extremely dynamic individual cells.

Urbanization triggers the emergence of slums in urban areas. Palembang is one of the Indonesian cities with slum settlements. This research aimed to analyze the interpolation of slum positions in Palembang and their distribution pattern using kernel density analysis and by modeling spatial data on accuracy values. It employed a quantitative method with a survey approach. Samples were selected using proportional random sampling from families in 64 slum areas across the 13 districts in the city, and their positions were recorded with a GPS device and then processed and mapped in ArcGIS. The data were analyzed with inverse distance weighted, kernel density, and spatial data modeling. Results showed that slum areas near the riverbanks had high population density, while those located further were lower. The values obtained from the kernel density analysis varied from 0 to 58.1123, while the inverse distance weighted showed a value range of 2.26745–380.991. The spatial data modelling analysis demonstrated that the distribution of slum areas was widely spread along the Musi River, with an accuracy value of 96.8%, which falls within the range of >0.9-1.

Mangrove forests play a vital role in maintaining the ecological balance of coastal regions. Ac-curately assessing changes in their ecosystem service value (ESV) requires more than precise mangrove distribution data, an appropriate set of evaluation methods is also needed. In this study, we obtain the spatial distribution and structural changes of mangrove forests based on Sentinel-2 imagery and build a mangrove forest ESV evaluation system by combining spatial pattern and the millennium ecosystem services. The results showed that: (1) the area of mangroves in Guangxi increased from 6245.15 ha in 2016 to 6750.01 ha to 2020, with a net increase of 504.81 ha which mainly concentrated in Lianzhou Bay, Tieshan Harbour and Dandou Bay; (2) the ESV of mangrove forests was 379.85 million U.S. dollars (USD) in 2016 and 413.13 million USD in 2020; (3) the value of fishery, soil conservation, wave absorption, and pollution purification comprise the largest proportion of the ESV of mangrove forests. From 2016 to 2020, mangroves in Guangxi had demonstrated a positive development trend and was undergoing a process of recovery. Results and conclusions of this study provide valuable insights and information to facilitate a more profound comprehension of the relationship between the mangrove forest and its ecological significance.

The main focus of this paper is to propose a comprehensive framework for the cognitive meas-urement and modelling of the built environment. This will involve exploring and measuring neural mechanisms. The aim is to create a foundation for further studies in this field that are consistent and rigorous. Additionally, this framework will facilitate collaboration with cognitive neuroscientists by establishing a shared conceptual basis. The goal of this research is to develop a human-centric approach for urban design that is scientific and measurable, producing a set of urban design guidelines that incorporate cognitive measurement and modelling. By doing so, the broader intention is to design urban spaces that prioritize human needs and well-being, mak-ing them more liveable.

The popularity of online retail is followed by decreasing transactions at shopping centres. Leisure and companionship trips to restaurants and cafés are replacing the frequency of intra-urban shopping trips. Several studies demonstrated a notable trend in urban commercial areas, wherein functionality has shifted from shopping centres to primarily focused on restaurants and cafes. Leisure and companionship places in the urban south have greater diversity than formal restaurants and cafés. This article employs a comprehensive framework to examine the categorization of restaurants and cafés in the urban south, referred to as food services. This study aims to investigate the spatial pattern of food service in Surabaya and ascertain whether there is a concentration of all food service variants around existing shopping centres. This research employs the POI of food service units gathered through web scraping from Google Maps. Applying the k-means cluster analysis demonstrates that the spatial pattern of food services is categorized into several distinct clusters. This article discloses five types of urban spaces characterized by varying compositions of food service variants within them. These spaces include the city centre, sub-city centre, areas along main roads, residential neighbourhoods, and peri-urban areas.

Data on the temporal and spatial evolution of Sars-Cov-2, local control measures and their effects on morbidity and mortality patterns in rural Brazil are scarce. We analyzed data from case notification systems, epidemiological investigation reports, and municipal decrees in a small municipality in northeast Brazil. For spatial analysis, cases and deaths in the urban area were mapped. There were a total of 3,020 cases of COVID-19 from April 2020 to December 2021; 135 (4.5%) died. The cumulative incidence and mortality rates were 5,650.3 cases and 252.6 deaths per 100,000 population, respectively. The index case of Sars-Cov-2 in the city was diagnosed in March 2020. The first peak of cases and deaths occurred in May 2020. The second wave of infection peaked in May 2021, with the highest number of deaths in March 2021. In spatial analysis, the highest density of cases and deaths occurred in the urban area. The municipal government issued 69 decrees on restriction measures, surveillance, and maintenance of social isolation to address the spread of SarsCov-2. The spread of the Sars-Cov-2 pandemic in Itapajé mirrored the behavior observed in large metropolitan regions, from central neighborhoods to the periphery.

The rapid development of the regional economy in China has led to the rise of local ecological risks. It is very important to provide enjoyable ecosystem services to residents while reducing ecological risks. In order to understand spatial relationships between ecosystem services and ecological risks, we took Chongqing as an example in this study to assess the spatial relationship between ecosystem services and ecological risks at the county scale based on the ES-DPSIR system. The main findings include: (1) significant variation in the spatial distribution of the comprehensive ecosystem service index, where the lowest ecosystem service index (0.013) was found in the main urban area of Chongqing and the scores gradually increased outward from this center, reaching 0.689 in the outermost areas, (2) the increase of the comprehensive ecological risk index from east to west, ranging from -0.134 to 0.333, (3) the spatial relationship between ecosystem services and ecological risks was prominent, with 52.63% of the districts and counties being imbalanced or mild imbalanced, and (4) the significant differences between development trends of ecosystem services-ecological risks, including 60.53% being imbalanced districts and 30.47% mildly balanced. Overall, it was necessary to improve the relationship between ecosystem services and ecological risks in Chongqing by reducing ecological risks., and these research results could provide effective approaches and technical support for improving regional ecological security and enhancing ecosystem service capabilities.

This article deals with the governance of common goods within regeneration projects of UNESCO heritage sites. The main objective is to evaluate the existence of conditions that prove the con-temporary function of common goods as drivers of strategic regeneration of complex territories with high cultural value. The research identifies some of the strategic planning policies implemented for the historical centre of Naples and analyses their proactive function in the enhancement, conser-vation and reuse of built commons. The expected result is to contribute to the debate on the cultural planning of the territory, aimed at the diversification of services and the fight against the process of touristisation. Through desk research and consultation of primary sources, a brief outline is given of the historical process that led to the birth of the "Great UNESCO Project" in the historical centre of Naples. The paper then analyses the "Discipline of the common goods - Principles for the govern-ance and management of the commons in the city of Naples", defining its addresses, opportunities and limitations as a contribution to relational, oriented and sustainable strategic territorial planning.

This manuscript presents a study on the spatial relationships between bike accidents, the built environment, land use, and transportation network characteristics in Budapest, Hungary using Geographic Weighted Regression (GWR). The sample period included bike crash data between 2017 and 2022. The findings provide insights into the spatial distribution of bike crashes and their severity, which can be useful for designing targeted interventions to improve bike safety in Budapest and be useful for policymakers and city planners in developing effective strategies to reduce the severity of bike crashes in urban areas. The study reveals that the built environment features, such as traffic signals, road crossings, and bus stops, are positively correlated with the bike crashes index, particularly in the inner areas of the city. However, traffic signals have a negative correlation with the bike crash index in the suburbs, where they may contribute to making roads safer for cyclists. The study also shows that commercial activity and PT stops have a higher impact on bike crashes in the northern and western districts. The GWR analysis further suggests that one-way roads and higher speed limits are associated with more severe bike crashes, while green and recreational areas are generally safer for cyclists. Future research should be focused on the traffic volume and bikes trips’ effects on the severity index.

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In the context of the rural revitalization strategy, it is urgent to accurately grasp the spatial differentiation characteristics and influencing factors of rural settlements in underdeveloped arid inland river basins. Taking the Shiyang River Basin as an example, the rural settlements from 2000 to 2019 were obtained by visual interpretation using satellite remote sensing data and official statistical data. Following the logical of "state characteristics - evolutionary patterns - influence factors - layout optimization", the average nearest index, gravity-center migration model, spatial statistical analysis and other methods were used combine with GIS. The spatiotemporal pattern evolution characteristics of rural settlements in the past 20 years have been analyzed. The results revealed the following: 1) The distribution pattern of rural settlements in the study area is sheet-like and strip-shaped. The projects in the southeast are mostly distributed in a patchy pattern with high density, while the characteristics in the west and north are exactly opposite. The objects in the south are distributed in the alluvial area of rivers, while settlements in the north are located in the oasis area. 2) From 2000 to 2019, the scale of rural settlements in the Shiyang River Basin, where simultaneous occurrence of new-built and disappearing phenomena, shows an expansion trend that fast firstly and then slow. Spatially, rural settlements in the basin show a trend of clustering towards the southwest. 3) The distribution characteristics of rural settlements are close to water and roads, greatly influenced by urban-rural integration and ecological migration. The results will provide scientific basis for accelerating the modernization of rural areas, and the construction of new rural areas according to local conditions.

This article investigates the potential for carbon reduction in urban parks in Shangqiu City using high-resolution remote sensing imagery. The aim is to guide modern urban carbon neutrality strategies. The carbon reduction potential is estimated based on the mitigation of the urban heat island effect by park greenery, which reduces energy consumption. The parks are regarded as cool island centers, and 100 cooling gradients are gradually formed outward until the temperature reaches that of impermeable urban surfaces. The energy saved by the parks in mitigating the heat island effect is statistically calculated as the carbon reduction potential of urban park greenery. Additionally, this article classifies the sample parks into different categories and selects 26 landscape metrics to analyze their relationship with carbon-saving potential and driving factors.

By using various skill scores and spatial characteristics of spatial verification methods and tradi-tional techniques of the model evaluation tool (MET, V10.0.0), the gridded precipitation obser-vation CMPAV (V2.0) and three datasets that derived from local (LOC), Shanghai (SHA), and Grapes (GRA) model respectively are conducted to assess the 3 lead day rainfall forecast with 0.5-day intervals during summer of 2020 over central east China. Results have shown that LOC generally outperforms the other two for most skill scores but usually with relatively larger un-certainties than SHA, and it has the least displacement errors for moderate rainfall among the three datasets. However, the rainfall of GRA has been heavily underestimated and accompanied with large displacement error. Both LOC and SHA have shown almost equitable abilities in forecasting convection and rainstorms of the large area but with a slightly over-forecast of local convection, while LOC likely over-forecasts the local rainstorms. In addition, SHA slightly favors over-forecast on a broad scale range and a broad threshold range, and LOC slightly misses the rainfall exceeding 100 mm. Generally, for a broadly comparative evaluation on rainfall, the popular dichotomous methods should be recommended under considering reasonable classifi-cation of thresholds if the accuracy is highly demanded. And most spatial methods should be suggested to conduct with proper pre-handling of non-rainfall event cases. Especially, the veri-fications including spatial characteristic difference information could be recommended in a computationally sufficient environment.

(1) Background: tuberculosis (TB) is considered one of the leading causes of death by a single in-fectious agent worldwide. This study aimed to identify hotspots of people diagnosed with tu-berculosis and abusive use of alcohol, tobacco and other drugs in communities through a geo-spatial intelligence application; (2) Methods: ecological study with a spatio-temporal approach. We considered tuberculosis cases diagnosed and registered in the Notifiable Diseases Information System, which presented information on alcoholism, smoking, and other drugs. Spatial Variations in Temporal Trends (SVTT) and scan statistics were applied for the identification of Hotspots; (3) Results: of 29.499 cases of tuberculosis were identified. And when we applied the STTT for al-coholism, three Hotspots were detected, one of which was protective (RR: 0.08 – CI95%: 0.02 – 0.32) and two at risk (RR:1.42 – CI95%: 1.11 – 1.73; RR: 1.39 - CI95%: 1.28 – 1.50). Regarding smoking, two risk clusters were identified (RR: 1.15 - CI95%: 1.01 – 1.30; RR: 1.68 - CI95%: 1.54 – 1.83). For other drugs, a risk cluster was found (RR: 1.13 - CI95%: 0.99 – 1.29) and two protections (RR: 0.70 - CI95%: 0.63 – 0.77; RR: 0.76 – CI95%: 0.65 – 0.89); (4) Conclusions: it was evidenced in the communities a problem of TB and drug addiction. Use disorder perhaps in a person's brain and behavior and leads to an inability to continue their treatment, putting the community at risk for TB.

In this article, the evolution in space and in time of the coronavirus pandemic is studied by utilizing a neural network with a self-organizing nature for the spatial analysis of data, and a fuzzy fractal method for capturing the temporal trends of the time series of the countries. Self-organizing neural networks possess the capability for clustering countries in the space domain based on their similar characteristics with respect to their coronavirus cases. In this form enabling finding the countries that are having similar behavior and thus can benefit from utilizing the same methods in fighting the virus propagation. To validate the approach, publicly available datasets of coronavirus cases worldwide have been used. In addition, a fuzzy fractal approach is utilized for the temporal analysis of time series of the countries. Then, a hybrid combination of both the self-organizing maps and the fuzzy fractal approach is proposed for efficient COVID-19 forecasting of the countries. Relevant conclusions have emerged from this study, that may be of great help in putting forward the best possible strategies in fighting the virus pandemic. A lot of the existing works concerned with the Coronavirus have look at the problem mostly from the temporal viewpoint that is of course relevant, but we strongly believe that the combination of both aspects of the problem is relevant to improve the forecasting ability. The most relevant contribution of this article is the proposal of combining neural networks with a self-organizing nature for clustering countries with high similarity and the fuzzy fractal approach for being able to forecast the times series and help in planning control actions for the Coronavirus pandemic.

Understanding the relationships between land cover/urban structure patterns and air pollutants is key to sustainable urban planning and development. In this study, we employ a mobile monitoring method to collect PM_2.5 and BC data in the city of Philadelphia, PA during the summer of 2019 and apply the Structure of Urban Landscapes (STURLA) methodology to examine relationships between urban structure and atmospheric pollution. We find that, while PM_2.5 and BC vary by STURLA class, many of the differences in pollutant concentrations between classes are not significant. However, we also find that the proportions in which STURLA components are present throughout the urban landscape can be used to predict urban air pollution. Among frequently sampled STURLA classes, gpl hosted the highest PM_2.5 concentrations on average (16.60 _± 4.29 µg/m³), while tgbwp hosted the highest BC concentrations (2.31 _± 1.94 µg/m³). Furthermore, STURLA combined with machine learning modeling was able to correlate PM_2.5 (R²= 0.68, RMSE 2.82 µg/m³) and BC (R² = 0.64, RMSE 0.75 µg/m³) concentrations with the urban landscape and spatially interpolate concentrations where sampling did not take place. These results demonstrate the efficacy of the STURLA methodology in modeling relationships between air pollution and land cover/urban structure patterns.

This paper presents a two-way factor design incorporating both spatial and temporal variation in the prediction of COVID 19 in Africa. In line with this, the impact of COVID-19on the GDP in Africa is well scrutinized. In contrast to the existing works [1–3], this work also extends the two-factor design into the one-way factor design through incorporating covariates into spatial effects. The data rely on the spatial and temporal obtained from WHO datasets [4, 5]. The one-factor design with more covariates is taken into consideration to identify the major potential predictor variables responsible for the deaths and confirmed cases due to COVID 19 in Africa. The MANCOVA considered population density, temperature, humidity; perception, and wind are all considered as co-variates. Simulations show that the two-way analysis of variance has shown that there is a statistically significant difference between the spatial (Fcal= 8.2704, Pvalue= 3.099∗10−6)and temporal (Fcal= 48.7964, Pvalue= 9.147∗10−16) effects. South Africa and Nigeria are highly influencing due to the pandemic where their GDP also relatively mostly declined. A significant economic change is observed before the pandemic and after the outbreak of the pandemic(tcal= 2.9548, Pvalue= 0.01805). COVID 19 negatively influenced the economy of1 most of the African countries. The population density, temperature, and wind are found to be statistically significantly associated with COVID 19 cases and deaths.

Hox gene collinearity (HGC) is a multiscalar property of many animal phyla particularly important in embryogenesis. It relates entities and events occurring in Hox clusters inside the chromosome DNA and in embryonic tissues. These two entities differ in linear size by more than four orders of magnitude. HGC is observed as spatial collinearity (SC) where the Hox genes are located in the order (Hox1, Hox2, Hox3 …) along the 3’ to 5’ direction of DNA in the genome and a corresponding sequence of ontogenetic units (E1, E2, E3, …) located along the Anterior – Posterior axis of the embyo. Expression of Hox1 occurs in E1. Hox2 in E2, Hox3 in E3… Besides SC, a temporal collinearity (TC) has been also observed in many vertebrates. According to TC first is Hox1 expressed in E1, later is Hox2 expressed in E2, followed by Hox3 in E3,… Lately doubt has been raised whether TC really exists. A biophysical model (BM) was formulated and tested during the last twenty years. According to BM, physical forces are created which pull the Hox genes one after the other driving them to a transcription factory domain where they are transcribed. The existing experiments support this BM description. Symmetry is a physical-mathematical property of Matter that was explored in depth by Noether who formulated a ground-breaking theory that applies to all sizes of Matter. This theory applied to Biology can explain the origin of HGC as applied not only to animals developing along the A/P axis but also to animals with circular symmetry.

Single cell RNA-seq (scRNA-seq) profiles conceal temporal and spatial tissue developmental information. De novo reconstruction of single cell temporal trajectory has been fairly addressed, but reverse engineering single cell 3D spatial tissue localization is hitherto landmark based, and de novo spatial reconstruction is a compelling computational open problem. Here we show that a new algorithm - named D-CE - for coalescent embedding of single cell transcriptomic networks can address this open problem. We rely merely on the spatial information encoded in the expression patterns of developmental signal transcription factor (DST) genes, and we find that D-CE of cell-cell association DST-transcriptomic networks reliably reconstructs the Geo-seq or single cell samples’ 3D spatial tissue distribution. Comparison to the novoSpaRC and CSOmap (recent and only available de novo 3D spatial reconstruction methods) on 16 datasets and 681 reconstructions, reveals a significantly distinctive superior performance of D-CE.

Boreal forests are one of the largest stores of carbon on Earth, and two-thirds of them are located in Siberia. Despite the fact that these forests have a significant influence on the global climate, they continue to remain understudied. Chernevaya taiga is a unique example of a highly productive Siberian boreal ecosystem. This type of forest is characterized by a series of unique ecological traits, the most notable of which are the gigantism of the perennial herbaceous plants and bushes, complete lack of moss cover on soil surface, and the type of soil it grows on, notable for its particularly high rate of decomposition of vegetative remains and low humic acid content. Abundant rainfall actively washes out nutrients from the top layers of the soil, but its fertility level remains very high. In fact, based on the existing data, it is twice as high as that of fertilized agricultural lands. In some ways the conditions within this type of forest closely resemble those observed in tropical rainforests. Microbiota associated with soil and plants represent an integral part of an interconnected system and contribute significantly to productive processes in soils. Its impacts on the environment require further study, since it could lead to discoveries that will help improve soil fertility without harming the natural environment.

The pandemic outbreak of COVID-19 had begun in March of 2020 on a global scale. This outbreak has originated numerous questions for society and many challenges for public managers. The disease is worrying because it has a high propagation velocity, high lethality levels, and there is no cure. Some groups are considered more vulnerable due to pre-existing disease conditions, age-range, and living conditions. In Latin American countries, people live in different conditions than those who live in countries located in the North hemisphere, such as climatic conditions, less favorable socioeconomic conditions, different educational levels, inequality, precarious urban infrastructure, etc. These factors generate even more concern and uncertainty about the pandemic than in developed countries. This study aimed, although preliminarily, to identify areas of great socio-spatial vulnerability and susceptibility of infection of people over 60 years old of COVID-19 in Brasília, Brazil. In this research, publicly available data and information about the population characteristics and social aspects were used, all connected directly to the census sectors. With the support of Geographic Information System (GIS), a matrix was used to cross-check the data and, thereby, achieve the objective of identifying the most vulnerable sectors for people aged over 60 years old. The results point out to more than 400 census sectors classified as Extremely Vulnerable in Brasília and it should be the object of special attention for public managers to do specific health care actions.

Bovine tuberculosis surveillance in Northern Ireland includes Multiple-Locus Variable number tandem repeat Analysis (MLVA) to determine the Mycobacterium bovis genetic type present in both cattle, and the predominant wildlife host, the European badger (Meles meles). These data are informative for investigating clusters of infection and understanding the scale at which interspecific transmission may occur. We utilised a comprehensive dataset of routinely sampled isolates from infected cattle and from badgers killed in road-traffic accidents to investigate the spatial co-location of MLVA types in, and between, the badger and cattle populations. Furthermore, we investigate the hypothesis that the farming enterprise type might explain some variation in this relationship. MLVA types were spatially co-localised in cattle and RTA badger hosts, indicative of a shared epidemic. Dairy herds were more likely to have at least one MLVA type in common with nearby RTA badgers, compared to non-dairy herd types. Marginally more MLVA spatial clustering was observed in non-dairy herds, which may be a consequence relatively more between-herd movements. For the cattle population, local transmission mechanisms such as infection from contiguous herds, infectious wildlife and short-range between-herd cattle movements appear primarily to drive the epidemic: there appears to be a more limited role for long-range movements. Animal management practices are likely the driving force behind this observation, as beef rearing is associated with elevated numbers of animal movements compared to dairy herds.

(1) Background: Most of the existing studies focus on the evaluation of technology finance; the relationship between technology finance and technology innovation. But there are few studies on the development of technology finance and the quality of economic development in our country; (2) Methods: Based on the panel data of 30 provinces in China, this paper constructs an index system to measure the development of technology finance through the improved entropy method, and tests the spatial correlation of the development of technology finance in China by Moran'I index. According to the test results, this paper constructs a spatial econometric model to empirically analyze the promoting effect of scientific, technological and financial development on high-quality economic development, and analyzes its promoting effect in different regions and different time periods; (3) Results: The results show that the quality of China's economic growth is spatially dependent, and the development of science, technology and finance can significantly promote the quality economic development in China. And the promotion coefficient of the central region is the largest, as well as the coefficient of the eastern region is the smallest. The promotion coefficient was small and not significant before 2015, and was significantly positive after 2015; (4) Conclusions: this paper puts forward the corresponding policy recommendations according to the research results.

The main scope of the presented paper is an assessment of the potential repellent effect of selected essential oils (EOs) against Alphitobius diaperinus, which can cause economic losses in storages and poultry industry. Due to development of pesticide resistance in A. diaperinus populations, as well as an attempt to limit extensive usage of potentially harmful pesticides in food-related industries, there is a strong need for development of alternative methods of management of A. diaperinus infestations. Because of cost-effectiveness, availability and low vertebrate toxicity EOs are promising agents in pest management. In presented paper four of-the-shelf EOs: mint, vanilla, lemon and citronella (and their mixtures) were tested as a potential repellents. Moreover, novel preference assay providing an extended analysis of preference and the locomotor response was used. The most effective EOs were, respectively: citronella and lemon. EOs mixtures were generally more repellent than single EOs, with lemon and vanilla 1:1 mixture acting as the strongest repellent. Few of tested EOs caused significant alterations in locomotor activity, although direct relation wasn’t observed. In conclusion, EOs can be potentially used as a repellent agents in A. diaperinus management. Additionally, data on locomotor activity may lead to better design of pull-push strategies in pest management.

Background: Underreporting of Visceral Leishmaniasis (VL) in India remains a problem to public health controls. Effective and reliable surveillance systems are critical for monitoring disease outbreaks and public health control programs. However, in India, government surveillance systems are affected by levels of scarcity in resources and therefore, uncertainty surrounds the true incidence of asymptomatic and clinical cases, affecting morbidity and mortality rates. The State of Bihar alone contributes up to the 40\% of the worldwide VL cases. The inefficiency of surveillance systems occurs because of multiple reasons including delay in seeking health care, accessing non-authentic health care clinics, and existence of significant asymptomatic self healing infectious cases. This results in a failure of the system to adequately report true transmission rates and number of symptomatic cases that have sought medical advice (thus, high underreporting of cases). Objectives and Methods: There are several methods to estimate the extent of underreporting in the surveillance system. In this research, we use a mathematical dynamic model and two different types of data sets, namely, monthly incidence for 2003-2005 and yearly incidence from 2006-2012 from the Bihar's 21 most VL affected districts out of its 38 districts. The goals of the study are to estimate critical metrics to measure level of transmission and to evaluate the estimation process between the two data sets and 21 districts. In particularly, our focus is on (i) estimating infection transmission potential, underreporting level in incidence and proportion of self-healing cases, (ii) quantifying reproduction number of the$R_0$, and (iii) comparing underreporting incidence levels and proportion of self-healing cases between the two periods 2003-2005 and 2006-2012 and between 21 districts. Results: Our research suggests that the number of asymptomatic individuals in the population who eventually self-heal may have a significant effect on the dynamics of VL spread. The estimated mean self-healing proportion (out of all infected) is found to be $\sim 0.6$ with only 7 out of 21 affected districts having self-healing proportion less than $0.5$ for both data sets. The estimated mean underreporting level is at least $64$\% for the state of Bihar. The estimates of the basic reproduction numbers obtained are similar in magnitude for most of the districts, being in the range of (0.88, 2.79) and (0.98, 1.01) for 2003-2005 and 2006-2012, respectively. Conclusions: The estimates for the two types (monthly and yearly) of temporal data suggest that monthly data are better for estimation if less number of data points are available, however, in general, using such data set results in larger variances in parameters as compared to estimates obtained through aggregated yearly data. Estimated values of transmission related metrics are lower than those obtained from earlier analyses in the literature, and the implications of this for VL control are discussed. The spatial heterogeneity in these control metrics increases the risk of epidemics and makes the control strategies more complex.

Geologists employ high-spatial-resolution (HR) remote sensing (RS) data for many diverse applications as they effectively reflect detailed geological information, enabling high-quality and efficient geological surveys. Applications of HR RS data to geological and related fields have grown recently. By analyzing these applications, we can better understand the results of previous studies and more effectively use the latest data and methods to efficiently extract key geological information. HR optical remote sensing data are widely used in geological hazard assessment, seismic monitoring, mineral exploitation, glacier monitoring, and mineral information extraction due to high accuracy and clear object features. Compared with optical satellite images, synthetic-aperture radar (SAR) images are stereoscopic and exhibit clear relief, strong performance, and good detection of terrain, landforms, and other information. SAR images have been applied to seismic mechanism research, volcanic monitoring, topographic deformation, and fault analysis. Furthermore, a multi-standard maturity analysis of the geological applications of HR images using literature from the Science Citation Index reveals that optical remote sensing data are superior to radar data for mining, geological disaster, lithologic, and volcanic applications, but inferior for earthquake, glacial, and fault applications. Therefore, geological remote sensing research needs to be truly multidisciplinary or interdisciplinary, ensuring more detailed and efficient surveys through cross-linking with other disciplines. Moreover, the recent application of deep learning technology to remote sensing data extraction has improved automatic processing and data analysis capabilities.

The city of Barcelona, ​​like other cities in the world, suffers strong internal socio-economic inequalities. Numerous works have sought to detect, quantify, characterize and / or map existing intra-urban differences, almost always based on quantitative methodologies. With this contribution, we intend to illuminate the complementary role that qualitative methodologies can play in studies on urban socio-environmental vulnerability. We consider aspects that are not quantifiable but that may be inherent to many such vulnerable spaces, both in the constructed environment and in the social ambit. These questions are considered through selected neighborhoods of Barcelona which have been shown (in prior works, mainly studies of quantitative manufacturing) to possess elements of vulnerability including a high presence of immigrants from less-developed countries, low per capita income, aging populations, or low educational levels. The results reveal the multidimensionality of vulnerability in the neighborhoods analyzed, as well as the essential complementarity among methodologies that detect and support possible public actions aimed at reducing or eliminating intra-urban inequalities.

This study aims to open a discussion on the concept “cultural sustainability” in architectural design. We asked the question if spatial planning has a role in cultural sustainability and in which terms cultural sustainability could be considered or discussed in design process. We started with a presupposition of an example which achieved cultural sustainability in time. We exemplified a holiday resort village designed in 1970 and is still in use with inconsiderable transformations. As being a social engineering was a necessity for the architects, Altuğ and Behruz Çinici [1], it can be said that their design approach was to achieve a sustainable living considering the financial, ecological, environmental and cultural dimensions. Behruz Çinici and his wife Altuğ Çinici are influential and proclaimed architects of their time. For understanding Çinici’s design concepts, we first looked at his inspiration sources as he verbalized in his conferences. After studying on their village projects, we suggested four spatial concepts for reading the projects from the perspective of cultural sustainability. We analyzed their three resort villages designed in the same decade through the criteria we have suggested. In evaluation; the distinguished features of Çinici’s resort projects are discussed in relation with the concept cultural sustainability. In conclusion, we intended to open a discussion for the criteria we proposed for cultural sustainability in spatial planning and put the importance of cultural practices for housing policies for regional identity in global world.

Crowd counting is of significant importance for numerous applications, e.g., urban security, intelligent surveillance and crowd management. Existing crowd counting methods typically require specialized hardware deployment and strict operating conditions, thereby hindering their widespread deployment. To acquire a more effective crowd counting approach, a device-free counting method based on Channel Status Information (CSI) is proposed, which could mitigate environment noise through wavelet transform and extract the amplitude or phase covariance matrix as the feature vector. Moreover, both the spatial diversity and frequency diversity are leveraged to improve detection robustness. The accuracy of the proposed CSI-based method is compared with a renowned crowd counting one, i.e., Electronic Frog Eye: Counting Crowd Using WiFi (FCC). The experimental results reveal an accuracy improvement of 30% over FCC.

Urban cycling is a sustainable transport mode that cities currently try to expand. However, cities still do not take advantage of geospatial technologies to understand cycling mobility based on the behavioural patterns and difficulties faced by cyclists. This study analyses a geospatial dataset crowdsourced by urban cyclists using an experimental geo-game. About 20 participants per city recorded bicycle trips during one week and, by aggregating them, we found not only the cyclists’ preferred streets but also the frictions faced during cycling. We successfully identified 284 places potentially having frictions: 71 in Münster, Germany; 70 in Castelló, Spain; and 143 in Valletta, Malta. At such places, Participants not only recorded bicycle trips but also segments at lower speeds indicating a deviation from an ideal cycling scenario. We described the potential frictions preventing from bicycle commuting based on the distance to bicycle paths, surrounding infrastructure, and location in the urban area.

Horses’ ability to adapt to new environments and to acquire new information plays an important role in handling and training. Social learning in particular would be very adaptive for horses as it enables them to flexibly adapt to new environments. In the context of horse handling, social learning from humans has been rarely investigated but could help to facilitate management practices. We assessed the impact of human demonstration on spatial problem-solving abilities in horses using a detour task. In this task, a bucket with a food reward was placed behind a double-detour barrier and horses (n = 16) received a human demonstration or no demonstration. Horses were allocated to two test groups of 8 horses each, which experienced the two treatments in a counterbalanced order. We found that horses did not solve the detour task faster with human demonstration. However, both test groups improved rapidly over trials. Our results suggest that horses prefer to use individual rather than social information when being confronted with a spatial problem-solving task.

Outdoor lighting is becoming increasingly widespread, and residents are suffering from serious light pollution as a result. Residents’ awareness of their rights to protection has gradually increased. However, due to the sometimes-inaccessible nature of residential vertical light incidence intensity data and the high cost of obtaining specific measurements, there is no appropriate hierarchic compensation for residents suffering from different degrees of light pollution. It is therefore important to measure light pollution levels and their damage at the neighborhood scale to provide residents with basic materials for proper protection and to create more politically suitable solutions. This article presents a light pollution assessment method that is easy to perform, is low-cost, and has a short data-processing cycle. This method can be used to monitor residential zone light pollution in other cities. We chose three open areas to test the spatial variation pattern of light intensity. The results are in accordance with spatial interpolation patterns and can be fit, with high precision, using the IDW method. This approach can also be used in 3 dimensions to quantitatively evaluate the distribution of light intensity distribution. We use a mixed-use zone in Beijing known as The Place as our case study area. The vertical illumination at the windows of residential buildings ranges from 2 lux to 23 lux; the illumination in some areas is far higher than the value recommended by CIE. Such severe light pollution can seriously interfere with people's daily lives and has a serious influence on their rest and health. The results of this survey will serve as an important database to assess whether the planning of night-time lighting is scientific and whether it provides residents with a basis for the protection of their rights.

The Tibetan Plateau(TP) is known as ‘the water tower of Asian’, its precipitation variation play an important role in the eco-hydrological processes and water resources regimes. based on the monthly mean precipitation data of 65 meteorological stations over the Tibetan Plateau and the surrounding areas from 1961-2015,variations, trends and temporal-spatial distribution were analyzed, furthermore, the possible reasons were also discussed preliminarily. The main results are summarized as follows: the annual mean precipitation in the TP is 465.54mm during 1961-2015, among four seasons, the precipitation in summer accounts for 60.1% of the annual precipitation, the precipitation in summer half year (May.- Oct.) accounts for 91.0% while that in winter half year (Nov.- Apr.) only accounts for 9.0%; During 1961-2015, the annual precipitation variability is 0.45mm/a and the seasonal precipitation variability is 0.31mm/a, 0.13mm/a, -0.04mm/a and 0.04mm/a in spring, summer, autumn and winter respectively on the TP; The spatial distribution of precipitation can be summarized as decreasing from southeast to northwest in the TP, the trend of precipitation is decreasing with the increase of altitude, but the correlation is not significant. The rising of air temperature and land cover changes may cause the precipitation by changing the hydrologic cycle and energy budget, furthermore, different pattern of atmospheric circulation can also influence on precipitation variability in different regions.

Urban indicators characterize a specific urban space through calculated values, such as building coverage, building height, floor area ratio, and many others. Urban indicators established in spatial development plans should ensure the preservation of spatial order when introducing new construction investments. They should also harmonize with the existing urban structure, and even modernize it towards sustainable development. When determining these indicators, surrounding space is analyzed. Conventionally, building indicators in the existing space are determined based on available documents, usually 2D spatial data such as large-scale maps or cadastral maps. The aim of this research is to investigate the method of calculating building indicators using 3D urban building models that will be created from airborne Light Detection and Ranging (LiDAR) measurements. In the discussion of the results, indicators calculated based on LiDAR data are compared with calculated from 2D cadastral data. The calculated 3D indicators correlate with the classically calculated indicators. The accuracy of computed building area, volume, and other indicators depends on the LiDAR point cloud density and accuracy. The indicators calculated from 3D data align with new trends in defining Building Morphology Indicators (BMI).

Extracting the economic value by integrated utilization of space in old communities is crucial for encouraging independent participation from enterprises and residents, reducing reliance on government leadership and fiscal investment. This study starts from the active perspective of spatial assets, constructs a value activation framework for old community spatial assets by balancing "endogenous demand" and "exogenous opportunities." By enhancing "economic value" through "use value," five methods for value extraction and overall project utilization paths are proposed, guided by a dynamic "cost-revenue" balance. Using multi-source data, we identify the spatial assets of 1,096 old communities in central Guangzhou and apply a market comparison method for economic value assessment. Additionally, the study offers recommendations on timing and project portfolios for regeneration efforts, along with strategies for establishing a coordinating implementation entity and fund account. This research provides strategic insights for advancing the regeneration of old communities by tapping into their macro-level economic potential.

Tianjin is currently in the phase of urban stock renewal, yet its industrial heritage encounters significant challenges, including constrained spatial development, a misalignment between industrial progress and the reuse of industrial heritage, and an understated cultural representation. This study applies geospatial analysis to delineate the distribution characteristics of Tianjin's industrial heritage across time, space, and typology. Moreover, the geographic detector is employed to assess the impact of various factors on the development of Tianjin's industrial heritage, ultimately elucidating the correlations between existing challenges and objective patterns. The conclusion of this paper presents three strategic approaches for the renewal of industrial heritage, emphasizing historical culture, environmental space, and sustainable development. These strategies are designed to provide a solid research foundation for protecting and revitalizing Tianjin's industrial heritage resources.

This paper proposes a novel facial expression recognition network called the Lightweight Facial Network with Spatial Bias (LFNSB). The LFNSB model balances model complexity and recognition accuracy. It has two key components: a lightweight feature extraction network (LFN) and a Spatial Bias (SB) module for aggregating global information. The LFN introduces combined channel operations and depthwise convolution techniques, effectively reducing the number of parameters while enhancing feature representation capability. The Spatial Bias module enables the model to focus on local facial features while also capturing the dependencies between different facial regions. Additionally, a novel loss function called Cosine-Harmony Loss is designed. This function optimizes the relative positions of feature vectors in high-dimensional space, resulting in better feature separation and clustering. Experimental results on the AffectNet and RAF-DB datasets show that the proposed LFNSB model performs excellently in facial expression recognition tasks. It achieves high recognition accuracy while significantly reducing the number of parameters, thus substantially lowering model complexity.

Indications of the passage of time are indirect reminders of our mortality. Humans have often responded to this discomforting thought by attempting, temporally at least, to escape its relentless march through the notion of a timeless present. This essay explores how architecture can assist us in that objective. It suggests that there are at least three different ways that buildings can offer us temporary psychological refuge from the progress of time. They can include the fascination of seemingly timeless natural movement patterns. They can recall archetypal environments, and they can isolate us from the external world of perpetual temporal progress.

The paper focuses on the application of a multilevel, integrative and territorial approach to the protection of cultural heritage through planning instruments in Serbia. It points out the problems of the sectoral approach and discusses international recommendations that can be integrated into the planning methodology. The case study is the area of Djerdap National Park, which has an already established international and national importance. The research suggests that the cultural heritage in the area, with the exception of the Roman limes, is still recognized at the level of individual localities. The study emphasizes the importance of aligning all levels of governance to safeguard the whole spatial entity rather than focusing solely on its particular areas. The interna-tional and national importance of the planning area contributes to the strengthening of awareness of heritage and its institutional protection. However, the vitality of the area, which is key to its sustainable development, comes from the local level. Planning documents contribute to the inte-gration of local values and local communities into development programs and projects.

Revealing the spatial distribution mechanism of the coupling effect between soil water and salt is essential for managing saline-alkali farmland. This study utilized the multifractal method to analyze soil water and salt under varying water content levels in arid saline-alkali farmland. Soil samples were collected on the second (S1), fifth (S2), eighth (S3), eleventh (S4), and fourteenth (S5) days after a rainfall event. The findings showed that a significant decrease in soil water content, contrasting with an increase in soil salt content throughout the soil layers post-rainfall. As water content decreased, the spatial variability of soil water initially increased from S1-3 and then decreased, while the spatial variability of soil salt decreased. The spatial distribution between soil water and salt exhibited a high correlation at S3-4 (with relative water content of soil ranging from 0.52 to 0.75) due to their coupling effect. However, soil salt was unevenly leached by rainfall at high water content levels (S1-2) and precipitated at low water content levels (S5), resulting in low spatial variability correlations between soil water and salt. This study elucidated the coupling process of soil water and salt, identifying their spatiotemporal distribution mechanism in dryland agricultural areas.

Xinjiang, as a climate-sensitive region in northwest China, holds significant importance in studying extreme weather events for agricultural production and socioeconomic development. Based on data from 52 meteorological stations and 23 extreme climate indicators in Xinjiang from 1960 to 2021, the 5a moving average, linear trend estimation, climate tendency rate, and GIS spatial analysis methods are used to analyze the distribution and change characteristics of extreme climate. The results revealed that from 1960 to 2021, TXx, TNx, TXn, TNn, FD, ID, TN10p, TX10p, and WSDI showed a significant upward trend, while SU, TR, TN90p, TX90p, and CSDI showed a significant downward trend. What is more, the range of changes in the extreme temperature indexes from 1990 to 2021 is higher than that of 1960 to 1989. The high-value areas of extreme indexes are primarily concentrated in the northern part, while the high-value areas of cold event indexes are mainly distributed in the southern part. The upward/downward trends all accounting for over 80.00% of the entire region. The Rx1day, Rx5day, PRCPTOT, R95p, R99p, R10, R20, and R25 showed a significant growth trend from 1960 to 2021, and the range of change in the extreme precipitation indexes from 1990 to 2021 was lower than that from 1960 to 1989. Moreover, their high-value areas and high climate tendency areas are mainly concentrated in the northern and western parts of Xinjiang, with upward trends all accounting for over 71.00% of the entire region. The research results provide theoretical foundations for formulating climate risk strategies in the northwest region of China.

To assess the impact of a deep learning (DL) denoising reconstruction algorithm applied to identical patient scans acquired with two different voxel dimensions, representing distinct spatial resolutions. This IRB approved prospective study was conducted at a tertiary pediatric center in compliance with the Health Insurance Portability and Accountability Act. We used a General Electric Signa Premier unit (GE Medical Systems, Milwaukee, WI) to acquire two DTI sequences of the left knee on each child at 3T: an in-plane 2.0 x 2.0 mm2 with section thickness of 3.0 mm and a 2 mm3 isovolumetric voxel, neither had an inter-section gap. We used a multi-band DTI acquisi-tion with fat-suppressed single-shot spin-echo echo-planar sequence (20 non-collinear directions; b-values of 0 and 600 sec/mm2). The MR vendor-provided a commercially available DL model applied with 75% noise reduction settings to same subject DTI sequences at different spatial res-olutions. We compared DTI tract metrics from both DL-reconstructed scans and non-denoised scans for femur and tibia, at each spatial resolution. Differences were evaluated using Wilcox-on-signed ranked test and Bland-Altman plots. When comparing DL versus non-denoised diffu-sion metrics in femur and tibia using the 2 mm x 2mm x 3 mm voxel dimension there were no significant differences between tract count (p = 0.1, p =0.14) tract volume (p = 0.14, p = 0.29), or tibial tract length (p=0.16); femur tract length exhibited a significant difference (p

Land surface temperature (LST) is a critical parameter for land surface and atmospheric interactions. However, the applicability of current land surface temperature estimates for field-level hydrological, agricultural, and ecological operations is still challenging due to their coarse spatiotemporal resolution. In the current article, we have downscaled 100 m LST to 10 m by using high-resolution Sentinel-1,2 optical-microwave data and three different models- 1) Thermal Sharpening (TsHARP); 2) Thin Plate Spline (TPS); and 3) Random Forest (RF). The extensive analysis was performed at agricultural farms in the semi-arid (IARI) region of India during the winter and summer seasons of 2020-21 and 2021-22, arid (CAZRI) and per-humid (UBKV) region (2021-2022). The calibration accuracy of the RF model was shown to better agreement with the coefficient of determination (R2) of 0.982, root means square error (RMSE) 0.181 and normalized root means square error (RMSE) 0.061 with their lower values of standard errors over three diverse agro-climatic zones. The findings demonstrate that the validation accuracy of models' varied according to the agroclimatic zones, although RF and TPS consistently outperformed as compared to the TsHARP models.

In this paper, we shall introduce the interval-valued spatial error model. Based on the idea of least square method of single-valued case, we give the parameter estimator for interval-valued spatial error model. The theoretical properties of the proposed estimator are proved. Finally, we give the numerical analysis and a real example.

Spatial regression models have garnered significant attention across several disciplines, including functional magnetic resonance imaging analysis, econometrics, home price analysis, and many other domains. The phenomenon of sparsity is often found in nature, when a limited number of factors contribute significantly to the overall variation. Spatial regression models frequently use sparsity to indicate less complex computational and more superficial covariance structures. The spatial error model is a significant spatial regression model that focuses on the geographical dependence present in the error terms rather than the response variable. This study proposes an effective approach for estimating the vector of regression coefficients in the spatial error model, taking into consideration of the prior knowledge that some coefficients are insignificant and there is multicollinearity among the regressors. It also introduces pretest and shrinkage ridge estimators for spatial error regression models, evaluating their performance compared to traditional maximum likelihood estimators. It also assesses their efficacy using real-world data and bootstrapping techniques for comparison purposes.

Due to the lack of theoretical research on the amount of spatio-temporal information in the high-speed photography technologies, obtaining an optimized system with a best amount of spatio-temporal information remains a challenge, resulting in insufficient effective information and observation accuracy for ultrafast events. The paper presents an ultrafast raster imaging (URI) system with a large amount of spatio-temporal information based on the all-optical raster principle in single-shot. Specifically, we derive the optimal equation of spatial resolution and the expression for the maximum amount of spatio-temporal information that can achieve excellent performance for an URI system. It serves as a general guideline for getting a large amount of information design in the URI system. Compared to the existing URI systems, the advanced URI system exhibits an improvement of nearly 1 order of magnitude in the amount of spatio-temporal information, and more than twofold in spatial resolution. It shows a great potential for capturing intricate and non-repetitive ultrafast events on the femtosecond time scale.

This research deals with risk assessment for human life, man-made infrastructure, and agriculture in a landslide-prone area using GIS-based spatial methods. The study area landslide inventory map was prepared based on previous landslide information, aerial photograph analysis, and several field observations. A total of 550 landslides have been included with 182 debris flow and 368 soil slides. All included landslides were classified into two groups by random selection; half were used for model calibration and the rest were used for cross-validation. In the analysis, fourteen causative factors were vastly used, such as aspect, slope, curvature, elevation, topographic wetness index, forest timber diameter, forest type, forest crown density, forest age, land-use, geology, soil drainage, soil depth, and soil texture. Moreover, to identify the interaction between occurred landslides and causative factors, the affected pixels were divided into different sub-classes using a frequency ratio method. Based on the total dataset, three landslide susceptibility maps were constructed using Bayesian prediction, likelihood ratio, and fuzzy set method. By evaluating cross-validation and success rate curve, model susceptibility results were plotted with a receiver operating characteristic (ROC) curve and the area under the curve (AUC) was estimated. In addition, for risk assessment, each social data layer such as agriculture, house, industry, business, road, river, population intensity, monetary value, and vulnerability level was added based on the local standard and incident time and was converted into US dollars. During the analysis, each method hazard map was used with a specific group of thematic data layers. Subsequently, for preparing the probability table, study area total pixels and predictive landslide affected pixels were considered. Matching with the affected pixels, a standard of 5000 pixels was selected to run the final evaluation. Based on the results, the agricultural field showed the highest vulnerability and estimated risk of US $ 16.3 million. Further, the man-made infrastructure map showed a risk of US $ 31.3 million. The total estimated population casualties were 6.77, which was relatively similar to the published data.

The agricultural sector is currently confronting multifaceted challenges such as an increased food demand, a slow adoption of sustainable farming, a need for climate-resilient food systems, resource inequity, and protection of the small-scale farmers’ practices, all issues integral to food security and environmental health. Remote sensing technologies can assist precision agriculture to effectively address these complex problems, by providing farmers with a high-resolution lens. The use of vegetation indices (VIs) is an essential component of remote sensing, which combine the variability of spectral reflectance value (derived from remote sensing data) with the growth stage of crops. Currently a wide array of VIs is available that could be used to provide a classification and an evaluation of the state and health of crops. However precisely this high number leads to difficulties in selecting the best VI and combination of VIs for a specific objective. Without a thorough documentation and analysis of appropriate VIs, users might be confronted with difficulties in using remote sensing data or even with a very low accuracy of the results. Thus, the objective of this review is to conduct a critical analysis of the existing state of the art on the most important features related to the effective use of VIs for the discrimination and monitoring of the most important agricultural crops (wheat, corn, sunflower, soybean, rape, potatoes, and forage crops), grasslands and meadows. This data could be highly useful for all the stakeholders involved in agricultural activities (from farmers, researchers up to institutions dealing with the centralization and monitoring of agricultural crops).

Ethiopia has been known for its recurrent droughts and poverty due to extreme climate change and variability affecting agricultural production. This study was, therefore, conducted to analyze the seasonal and inter-annual variability in rainfall and temperature v over the past three decades in western Tigray. The study consisted of 34 years of temperature and rainfall data from nine stations in two administrative zones in the western part of Tigray. The climate data was obtained from the National Aeronautic Space Administration (NASA) from 1983 to 2016. Total rainfall, simple daily intensity index, Rx1day and Rx5day, and maximum and minimum temperature, tropical nights, diurnal temperature range, cold spell and warm spell duration indicator values, cold days and cold nights, warm days, and warm nights were analyzed. RClimDex software was employed to analyze the trends and quality control of the extreme rainfall and temperature indices; in addition, the Mann-Kendall test was also used to detect the changes and variability of the variables. The trend analysis showed that there was significant (p<0.05) variability in extreme temperatures, with maximum and minimum temperatures varying from 0.04 to 0.051 0C, and 0.022 to 0.031 0C, respectively. The results also revealed that there was a negative trend in extreme rainfall, with no uniform pattern. The highest and smallest reduction in the annual rainfall was 14.6 mm and 8.4 mm, which were observed at Maygaba and Adigoshu, respectively. More than 60% of the total rainfall was received in July and August throughout the study areas, with the maximum monthly rainfall of 294.2 mm observed at Adiremets. Finally, this study demonstrated that areas in the northwestern and western zones of Tigray were significantly affected by extreme rainfall and temperature variability, which subsequently affected crop yield, natural resources, food security, and the livelihood of the community during the last three decades.

The spatial and temporal changes in social vulnerability to natural hazards in Mexico are analyzed. To this end, using census data from 2000, 2010, and 2020 and a statistical method, different indices were computed, and with a GIS-based approach, patterns of social vulnerability are examined. In addition, a risk assessment test for severe weather (thunderstorms, hailstorms, and tornadoes) is made out. The results show different common social vulnerability driving factors in the three analyzed years, with root causes that have not been addressed since the beginning of the century. Likewise, a wider gap between Mexico's most and least vulnerable populations is identified. The changes in spatial patterns respond to different historical situations, such as migration, urbanization, and increased population. Also, poverty, ethnicity, and marginalization factors located in very particular regions in Mexico have remained relatively the same in the last few years. These situations have strongly influenced the spatial-temporal distribution of vulnerability in the country. The role of social vulnerability in the disaster risk to extreme events such as thunderstorms, hailstorms, and tornadoes in Mexico is fundamental to understanding changes in disaster distribution at the national level, and it is the first step to generating improvements in integrated risk management.

White oak mortality is a significant concern in forest ecosystems due to its impact on biodiversity and ecosystem functions. Understanding the factors influencing white oak mortality, particularly the soil properties, is crucial for effective forest management and conservation efforts. In this study, we aimed to investigate the spatial pattern of white oak mortality and examine the influence of soil properties on mortality rates. Multicycle Forest Inventory and Analysis data were compiled to capture white oak plots across the eastern US. White oak mortality data were collected across plot systems that utilized Diameter at Breast Height between two periods. Soil variables were analyzed to assess soil properties. Spatial analysis techniques, including geostatistics and regression modeling, were used to analyze the relationship between white oak mortality and soil characteristics. Results found clustered spatial patterns of white oak mortality across a broad scale depicting the significant effects of coarser soil textures, nutrient-deficient sites, and extreme soil moisture levels. Our findings demonstrated the importance of soil properties in shaping the spatial pattern of the white oak mortality rate. This idea can inform forest management practices for the conservation of white oak populations. Future research is needed for comprehensive soil assessment including biotic and abiotic factors for forest management strategies at a broader scale aimed at mitigating white oak mortality.

Landscape restoration activities must be conducted through a transdisciplinary process, integrating social, economic, environmental, and governance aspects. Combining visions from the natural and social sciences is a challenge in highly complex territories, where unique ecosystem characteristics, economic processes, stakeholders of diverse nature, and different normativity converge. The harmonization of multiple techniques, such as multicriteria spatial analysis, expert knowledge elicitation, and social mapping, allows for an approach to defining landscape restoration areas in complex regions. This paper employs multiple techniques to define ecosystem restoration areas in a complex Colombian Andes landscape, integrating ecological and social components for sustainable development. We observed that areas of high and very high feasibility for ecological restoration, encompassing 179.5 hectares (4.84% of the study area), are predominantly located near primary forests. Although some areas have low feasibility for conservation processes, they should not be disregarded as they still require protection. Landowners prioritize watershed and soil restoration as the most important landscape restoration activity due to their interest in improving water-related ecosystem services. This proposal enables the identification of areas with higher restoration potential at the property level, facilitating prioritization and investment allocation for future implementation.

Using the Super-SBM method, this study calculates the carbon emission efficiency (CEE) of 30 provinces in mainland China from 2011 to 2019. Then, using the systematic GMM model, spatial Durbin model, and mediating effect model, it examines the direct effect, spatial effect, and influence mechanism of the digital economy (DE) on CEE. It was found that (1) the DE significantly promoted regional CEE, but had a negative effect on CEE in provinces with high economic correlation; (2) mechanism studies showed that the DE improved CEE by reducing energy intensity, promoting industrial upgrading and green technology innovation; (3) regional heterogeneity analysis found that the DE significantly improved CEE in eastern provinces, but not in central and western provinces. DE improves CEE in provinces with high level of economic development, but decreases CEE in provinces with low level of economic development.This paper provides some empirical and theoretical references for the development of DE to improve CEE.

White oak mortality is a significant concern in forest ecosystems due to its impact on biodiversity and ecosystem functions. Understanding the factors influencing white oak mortality, particularly the soil properties, is crucial for effective forest management and conservation efforts. In this study, we aimed to investigate the spatial pattern of white oak mortality and examine the influence of soil properties on mortality rates. Multicycle Forest Inventory and Analysis data were compiled to capture all white oaks across the eastern US. White oak tree mortality data were collected across plot systems that utilized Diameter at Breast Height between two periods. Soil variables were analyzed to assess soil properties. Spatial analysis techniques, including geostatistics and regression modeling, were used to analyze the relationship between white oak mortality and soil characteristics. Results found clustered spatial patterns of white oak mortality across a broad scale depicting the significant effects of coarser soil textures (p = 0.00), nutrient-deficient sites, and extreme soil moisture levels (p <0.1). Our findings demonstrated the importance of soil properties in shaping the spatial pattern of white oak mortality rate. This idea can inform forest management practices for the conservation of white oak populations. Future research is needed for comprehensive soil assessment including biotic and abiotic factors for forest management strategies at a broader scale aimed at mitigating white oak mortality.

This article, inspired by Dr. Jürgen Essletzbichler’s chapter namely “Locating Location Models” in The SAGE Handbook of Economic Geography (2011), delves into the legacy and significance of location theory. By examining its integration of historical analysis and theoretical abstraction, the study highlights the crucial role played by historical and geographical contexts in shaping spatial patterns. It emphasizes the importance of complementing abstract models with historical analysis to gain a deeper understanding of real-world spatial phenomena. Acknowledging the delay in the development of location theory attributed to the dominance of the German historical school, the article recognizes its enduring value and resurgence in the realm of modern evolutionary economic geography. Ultimately, the abstract underscores the contributions of location theory to our comprehension of spatial dynamics and its potential for further advancements in the field of economic geography.

This study’s primary goal was to conduct an analysis on the flood propensity of the Tabua (Ribeira Brava) drainage basin’s main watercourse. In addition to that, this study also recommends two different methodologies in order to mitigate flood’s impacts, namely by dimensioning a detention basin and adjusting the riverbed roughness coefficient. Regarding the study on the flood propensity, it was necessary to resort to geomorphological data, which was obtained when characterizing the watershed; that data was crucial to determine the expected peak flow rate, according to the Gumbel Distribution methodology and considering a 100 years return period, and to perform necessary tasks in the SIG ArcGIS software. Lastly, it was also analyzed the drainage capacity of this drainage basin’s river mouth, in order to conclude whether it would have the capacity to drain the total volume of rainwater if an extreme flood event was to happen. Indeed, the main results point out that this watershed’s river mouth doesn’t have the necessary drainage capacity to cope with an extreme event, for the return period that was considered. As a consequence of that, the two mitigation measures aforementioned were developed considering Tabua (Ribeira Brava) drainage basin’s specific features: the sizing of the detention basin was estimated through the Dutch Method and the Simplified Triangular Hydrograph Method, while the adjustment of the roughness coefficient was considered a valid solution to enhance the drainage capacity of this river mouth.

Multimodal user interfaces promise a natural and intuitive human machine interactions. But is the extra effort for the development of a complex multi-sensor system justified, or can users also be satisfied with one input modality already? This study investigates interactions in an industrial weld inspection workstation. Three unimodal interfaces, including spatial interaction with buttons augmented on a workpiece or a worktable, and speech commands, were tested individually and in a multimodal combination. Within the unimodal conditions, users preferred the augmented worktable, but overall, the interindividual usage of all input technologies in the multimodal condition was ranked best. Our findings indicate that the implementation and use of multiple input modalities is valuable, and that it is difficult to predict the usability of individual input modalities for complex systems.

Stream chemistry effectively integrates watershed ecosystem processes on both spatial and temporal scales. Streams of coastal areas integrate a more homogeneous, flat topography wherein there can be interactions between the stream and the body of water into which it drains, especially where tidal fluxes occur. The present study assessed water quality of Thompson Bayou, which comes to the campus of the University of West Florida in a wetland after flowing through 4 km of commercial and private property with associated impacts on water quality. Sampling was carried out for one year at eight discrete sites along Thompson Bayou from the UWF campus to the Escambia River. We used a portable field meter to measure temperature, pH, dissolved O2 (DO), and specific conductivity (SC). Except for temperature, all variables exhibited a spatial pattern of significant variation with distance, with consistent increases in DO and SC as the stream approached the river. These variables also exhibited a temporal pattern of significant seasonal variation, including—and especially—temperature. Data suggest that spatial and temporal patterns of water quality of Thompson Bayou are determined by (1) processing of water by the wetland, (2) interactions of the stream channel with upland forest stands, and (3) the tidal hydrology of the Escambia River.

Objectives: COVID-19 pandemic interrupted the Spanish professional football competition until May 2020, when it was restarted following a surveillance protocol established by LaLiga. The aims were to describe the infective and serological status of professional football players (PLY) and staff (STF) between May 5th 2020 until April 22nd 2021, to analyze the spatial-temporal distribution of the COVID-19 disease in this cohort and its comparison to the Spanish population. Methods: a prospective observational cohort study was carried out. Differences between PLY and STF were assessed by Chi-squared test and test of equality of proportions. Pearson correlation test was used to measure the presence of an association between the percentages of positivity in population and LaLiga cohort. Results: 137,420 RT-PCR and 20,376 IgG serology tests were performed in 7,112 professionals. Positive baseline serology was detected in 10.57% of PLY and 6.38% of STF. Among those who started the follow-up as not infected and before STF vaccination, 11.87% of PLY and 5.03% of STF became positive. Before summer 2020 the prevalence of infection was similar than the observed at national level. The percentage of positivity in the Spanish population was higher than in LaLiga cohort, but both series showed a similar decreasing trend.

Working memory refers to the capability of the nervous system to selectively retain short-term memories in an active state. The long-standing viewpoint is that neurons play an indispensable role and working memory is encoded by synaptic plasticity. Furthermore, some recent studies have shown that calcium signaling assists the memory processes and the working memory might be affected by the astrocyte density. Over the last few decades, growing evidence has also revealed that astrocytes exhibit diverse coverage of synapses which are considered to participate in neuronal activities. However, very little effort has yet been made to attempt to shed light on the potential correlations between these observations. Hence, in this article we will leverage a computational neuron-astrocyte model to study the short-term memory performance subject to various astrocytic coverage and we will demonstrate that the short-term memory is susceptible to this factor. Our model may also provide plausible hypotheses for the various sizes of calcium events as they are reckoned to be correlated with the astrocytic coverage.

This study seeks to measure the degree of agglomeration of educational quality in Colombia, based on the non-socialization of the population that exhibits low educational quality, with the population that exhibits high educational quality, and thus determine how such agglomeration affects the phenomenon of academic segregation. To this end, we perform a spatial analysis of the educational quality in Colombia and of variables that may influence to the phenomenon of educational agglomeration. The level of agglomeration in educational quality in Colombia is demonstrated by the calculation of the Moran’s Index, in which a result of 0.62 was obtained. High educational quality is concentrated in the Andean region, while low educational quality is agglomerated in the periphery of the country, in areas such as the Pacific region. A spatial regression model was carried out to measure the dependence of municipalities on their neighbors, and to determine the main socio-economic factors affecting the phenomenon of educational agglomeration in Colombia, finding that living conditions, unsatisfied basic needs and fiscal transparency have all an impact on the educational quality of the municipalities. It is also found that the number of homicides in the municipalities does not seem to have a significant relationship with education.

If one considers chemical-biology toolsets that have had the greatest impact on numerous fields of life sciences over the most recent years, proximity-labeling tools, such as APEX, and Bio-ID arguably lead the way. This article reflects upon the current state-of-the-art and discusses key limitations underlying these emerging approaches, in particular, the limited functional knowledge they provide in understanding local proteomes / interactomes. This limitation is directly linked to the use of non-biologically- or non-pharmaceutically-relevant reactive intermediates in the course of covalently labeling the local proteomes. As such, these methods cannot report on specific functions of localized protein players, nor can they scrutinize whether the specific functions of such proteins/interactomes can be directly manipulated by pharmacologically-relevant small-molecule ligands. The latest data hint that precision localized electrophile delivery concept ushers a means to address this limitation with high spatiotemporal resolution, and ultimately, in relevant live animals.