ABSTRACT The article connects several concepts that were previously used and evaluated separately in functional regional taxonomy and in the delineation of functional regions. Typically, functional regions are not defined based on absolute values of population flows. Instead, they are delineated using interaction measures, which are normalizations of population flows. In the resulting system of functional regions, it is possible to determine a membership index for each basic spatial unit, expressing the degree to which that unit belongs to the functional region in which it is located. The membership index used should be in congruence with the interaction measure applied. This means they should be based on the same normalization method. Using the example of two different states, two different datasets, and two different interaction measures with their corresponding membership indices, we demonstrate the necessity of congruence: interaction measure—functional region boundaries—membership indices. An important part of the article's outputs is the visual representation of this congruence through map outputs. The results confirm that different interaction measures yield significantly different delineations of functional regions. Only when the interaction measure and the membership index are in congruence are the procedures' logical correctness and the results' compatibility maintained.
Accurately mapping spatial phenomena with limited observations hinges on selecting sampling sites that minimize predictive uncertainty. We model this task by quantifying unsampled-location uncertainty with ordinary Kriging and framing site selection as an optimization problem. Because the resulting Kriging prediction-variance objective is nonlinear, we derive an integer program approximation called Kriging-informed coverage sampling that bounds the Kriging variance with a set of linear constraints. We prove that Kriging-informed coverage sampling is isomorphic to the classical Maximal Coverage Location Problem, thereby linking geostatistical uncertainty reduction to a well-studied family of location problems and enabling the use of well-established solution techniques. Computational experiments on synthetic landscapes and a remote-sensing case study show that Kriging-informed coverage sampling attains 90% of the information gain achieved by exact non-linear solution methods while reducing solution times by up to two orders of magnitude.
Smell is a crucial yet understudied sensory dimension in urban environments, bridging tangible elements (e.g., exhaust, flowers) with intangible impacts on emotions, social interactions, and well-being. While geographical and urban research increasingly acknowledges multisensory experiences, much of geospatial analysis still emphasized the visual dimension. This research advances spatial thinking by examining cross-modal associations between smell and vision in urban environments. Specifically, we utilize advanced image processing techniques to extract visual cues from street view imagery (SVI) (i.e., Mapillary) and apply causal analysis to examine their effects on smell expectations recorded from participants. The results show that visual cues can predict smells in straightforward urban settings (e.g., parks or less densely populated areas). However, in complex urban environments, the predictive power of visual cues diminishes as diverse and overlapping scents obscure specific smells, even in visually distinct areas. These findings underscore the importance of a multisensory approach in urban analytics, enhancing our understanding of the interplay between sensory experiences and informing urban design strategies that integrate multiple senses to create engaging and inclusive environments. This is especially important for individuals with sensory impairments, such as anosmia or visual impairments, who rely on other senses to compensate for their perception of urban environments.
This study introduces a novel methodology in which a composite indicator derived using the Entropy Weight Method is decomposed into a location-specific component and a spatial component. Although EWM is widely recognized for its robustness in constructing data-driven composite indicators, standard applications typically overlook spatial information. To address this limitation, we combine EWM with spatial filtering techniques to disentangle location-specific effects from spatial spillovers, thereby improving the ability of the composite indicator to capture regional dynamics. This decomposition makes it possible to assess the relative contribution of local characteristics and spillover effects to the overall magnitude of the indicator in each region. In addition, we propose a random permutation test to evaluate the statistical significance of the spatial component. The methodology is applied to the construction of a composite indicator for education and training across 107 Italian provinces, using data from the Benessere Equo e Sostenibile (BES) dataset. The results show that the proposed approach provides policy-relevant insights that support targeted interventions and efficient resource allocation.
This study proposes a geographically and temporally weighted random forest (GTWRF) model for explanatory spatiotemporal analysis of COVID-19 outcomes. GTWRF extends the conventional geographically weighted random forest by incorporating spatial and temporal dependencies through an adaptive Gaussian weighting scheme. The model integrates three spatiotemporal distance (STD) functions, including a novel formulation that prioritizes observations closer in space and time. To capture major shifts in transmission dynamics, the analysis was structured around three data-driven temporal periods derived from observed COVID-19 waves between 2020 and 2023. GTWRF was then applied to US county-level COVID-19 incidence using four composite indicators (epidemiological, demographic, socioeconomic, and environmental) to identify region- and period-specific drivers of COVID-19. GTWRF using the new STD function achieved the lowest out-of-bag mean absolute error and root mean square error. Across periods, the epidemiological indicator was the leading driver of incidence, while secondary drivers shifted over time, with demographics being most influential in Period 1, environment in Period 2, and socioeconomic factors in Period 3. Regional vulnerabilities persisted, particularly in the South and West. These results show that GTWRF can characterize geographically varying, period-specific drivers of incidence and can support targeted interventions, surveillance prioritization, and resource allocation in future outbreaks.
This paper discusses the relationship between two types of points with a focus on spatial proportionality. The spatial proportionality referred to in this paper indicates the relationship in which one type of points is evenly distributed in relation to the other type of points. Examples include the spatial relationship between supermarkets and individuals, crimes and low-income residents, nursery schools and pupils, and so forth. Though analysis of spatial proportionality permits us to understand its underlying factors, analytical methods have not yet been fully developed. To fill the research gap, we propose a new method for evaluating the spatial proportionality between two types of points. We propose two statistics that measure the degree of spatial proportionality. We test the validity of the statistics through the applications to hypothetical and real datasets. The results indicate the effectiveness of the statistics and provide empirical findings.
While area-level inequalities in life expectancy are well documented within individual countries, cross-national comparisons remain rare. We estimated and compared the magnitude of area-level life expectancy inequalities across districts in the UK and Germany, representing two distinct welfare regimes. We used mortality data from national statistical offices to estimate life expectancy for all districts between 2003 and 2021. Based on employment data, we assigned a deprivation decile to each district within both countries and calculated life expectancy for each decile. Using the slope index of inequality, we compared temporal trends in life expectancy inequalities across deciles in both countries. We found that, although life expectancy was similar in both countries, the UK consistently showed higher levels of area-level inequality than Germany. While district-level life expectancy inequalities increased in both countries over time, they rose more sharply in the UK, particularly during the first years of the Covid-19 pandemic. These findings are likely to reflect deeply rooted differences in governmental approaches to ensuring equitable living conditions across two distinct welfare regimes.
Amid China's accelerating urbanization, the impact of migrants on carbon emissions has emerged as a critical yet underexplored dimension of environmental challenges. Drawing on data from 275 prefecture-level cities between 2010 and 2017, this study employs spatial econometric models to investigate the complex relationship between migrants and carbon emissions in China. The results reveal significant positive spatial autocorrelation in both carbon emissions and migrants. Over time, the autocorrelation of emissions has intensified, whereas that of migrants has gradually weakened. High-high clusters of both emissions and migrants are predominantly concentrated in the eastern coastal regions. Empirically, migrants exhibit an inverted U-shaped relationship with carbon emissions, with the optimization of industrial structure exerting a significant mediating effect. The analysis also highlights substantial interregional spillover effects, where the impact of migrants extends beyond city boundaries. Furthermore, environmental regulation is identified as a key factor that helps reduce carbon emissions. Heterogeneity analysis shows that migrants with higher educational attainment tend to generate lower carbon emissions, whereas older migrants are associated with higher carbon emissions. This study provides new theoretical perspectives and policy insights for understanding the environmental footprint of migrants on carbon emissions.
Spatial interpolation is a crucial task in geography. As perhaps the most widely used interpolation methods, geostatistical models-such as Ordinary Kriging (OK)-assume spatial stationarity, which makes it difficult to capture the nonstationary characteristics of geographic variables. A common solution is trend surface modeling (e.g., Regression Kriging, RK), which relies on external explanatory variables to model the trend and then applies geostatistical interpolation to the residuals. However, this approach requires high-quality and readily available explanatory variables, which are often lacking in many spatial interpolation scenarios-such as estimating heavy metal concentrations underground. This study proposes a Focal Feature Regression Kriging (FFRK) method, which automatically extracts geospatial features to construct a regression-based trend surface without requiring external explanatory variables. We conducted experiments on the spatial prediction of three heavy metals in a mining area in Australia. In comparison with 17 classical interpolation methods, the results indicate that FFRK, which relies solely on extracted geospatial features, consistently outperforms both conventional Kriging techniques and machine learning models that depend on explanatory variables. This approach effectively addresses spatial nonstationarity while reducing the cost of acquiring explanatory variables, improving both prediction accuracy and generalization ability.
In this article we reframe the massive location choice problem for retail chains by proposing an optimization model that integrates human mobility. Traditional methods of massive location choice encounter limitations rooted in assumptions such as power-law distance decay and oversimplified travel patterns. In response, we present a spatial operations research model aimed at maximizing customer coverage, using massive individual trajectories as a robust "sampling" of human flows. Using a deduplication-based greedy algorithm, we maximize customer coverage within a predefined number of stores while maintaining computational efficiency. Through a case study in Shenzhen, China, we demonstrate that our model significantly improves population coverage compared to existing retail locations. Additionally, the optimized coverage follows a power-law distribution, providing implications for the scaling effects and robustness of retail location potential.
This paper investigates the interplay between population dynamics and transport connectivity in African municipalities from 1880 to 2020. Using an original historical dataset spanning African countries, we examine how proximity to railways and ports has shaped population dynamics over time. Through the application of Granger causality and Vector Autoregression (VAR) models—accounting for structural breaks and regime changes—we demonstrate that transport infrastructure development has a statistically significant and directional influence on urban population growth. Our findings reveal that city size and growth are not randomly distributed across space; rather, they are systematically linked to historical patterns of transport connectivity. In particular, the largest and fastest-growing cities consistently correspond to strategic nodes at the intersection of railway and maritime networks. These results, reinforced by hierarchical clustering and regime analysis, highlight the enduring impact of colonial infrastructure and institutional path dependence. The study also points to broader implications for contemporary urban planning in the context of containerization and modern global trade flows.
This study proposes coarse-to-fine spatial modeling (CFSM) as a scalable and machine learning-compatible alternative to conventional spatial process models. Unlike conventional covariance-based spatial models, CFSM represents spatial processes using a multiscale ensemble of local models. To ensure stable model training, larger-scale patterns that are easier to learn are modeled first, followed by smaller-scale patterns, with training terminated once the validation score stops improving. The training procedure, which is based on holdout validation, can be easily integrated with other machine learning algorithms, including random forests and neural networks. CFSM training is computationally efficient because it avoids explicit matrix inversion, which is a major computational bottleneck in conventional spatial Gaussian processes. Comparative Monte Carlo experiments demonstrated that the CFSM, as well as its integration with random forests, achieved superior predictive performance compared to existing models. Finally, we applied the proposed methods to an analysis of residential land prices in the Tokyo metropolitan area, Japan. The CFSM is implemented in an R package spCF (https://cran.r-project.org/web/packages/spCF/).
Traditional measures of urban accessibility often rely on static models or survey data. However, location information from mobile networks enables large-scale, dynamic analyses of how people navigate cities. In this study, we employ eXtended Detail Records (XDRs) from mobile phone activity to analyze commuting patterns and accessibility inequalities in Santiago, Chile. We identify residential and work locations and model commuting routes by public transport and walking using the R5 multimodal routing engine. Spatial patterns are examined using bivariate local indicators of spatial association (LISA) alongside regression techniques to identify distinct commuting behaviors and their alignment with vulnerable population groups. Our results show that while average public transport commuting times do not differ significantly across socioeconomic groups, marked inequalities emerge when accessibility is considered. High-income neighborhoods consistently exhibit high accessibility, whereas low-income areas show substantially lower levels. Importantly, these disparities do not translate into longer commuting times for lower-income groups, indicating a weak relationship between proximity to opportunities and observed travel times. The analysis also reveals significant disparities across sociodemographic groups, particularly in relation to Indigenous populations and gender. The proposed approach is readily scalable and can support evaluations of changes in commuting patterns and the impacts of urban interventions.
Human mobility is a fundamental aspect of social behavior, with broad applications in transportation, urban planning, and epidemic modeling. Represented by the gravity model and the radiation model, established analytical models for mobility phenomena are often discovered by analogy to physical processes. Such discoveries can be challenging and rely on intuition, while the potential of emerging social observation data in model discovery is largely unexploited. Here, we propose a systematic approach that leverages symbolic regression to automatically discover interpretable models from human mobility data. Our approach finds several well-known formulas, such as the distance decay effect and classical gravity models, as well as previously unknown ones, such as an exponential-power-law decay that can be explained by the maximum entropy principle. By relaxing the constraints on the complexity of model expressions, we further show how key variables of human mobility are progressively incorporated into the model, making this framework a powerful tool for revealing the underlying mathematical structures of complex social phenomena directly from observational data.
An Arriaga decomposition partitions differences in life expectancy into contributions from mortality rate differences in each age. A Kitagawa decomposition partitions a difference between two weighted means into effects from differences in structure and from differences in each element of the weighted value. Life expectancy differences between like-defined subpopulations can be decomposed using the Arriaga method, or a different suitable decomposition method. If combined (or total) life expectancy is treated as a weighted average of the subpopulations, then the results of subgroup-specific decompositions can substitute the rate component from a Kitagawa decomposition of combined life expectancy. This is valid as long as the relative weight of each subpopulation is part of the initial conditions of the combined lifetable, and group prevalence in later ages is determined only by mortality. The composition component of the same Kitagawa decomposition gives the effect of differing subgroup composition on total life expectancy differences. Notable properties of the method include: (i) it accommodates any number of subpopulations, (ii) it easily incorporates cause-of-death information, and (iii) composition is considered only in the initial conditions. We apply the method to Spanish cause- and education-specific data. This method can further disentangle the effects of mortality and composition differences, helping to explain or clarify paradoxes and contemporary or forthcoming life expectancy changes as partly driven by shifts in cohort composition. We give both R code and spreadsheet implementations of the method.
Natural springs are the primary source of water for the rural households in the Himalayan region. For many people, springs are the sole source of water. For example, a major proportion of drinking water supply in the mountainous parts of Himachal Pradesh is spring based. This research paper is based on the role of local community in the conservation of natural water springs in Kharahal region of Kullu district of Himachal Pradesh. The main objectives of this research paper are to identify the natural water springs, to study the significance of natural water springs and study the role of the local community in the conservation of natural water springs in the study area. This research paper utilizes both primary and secondary data sources. Primary data was meticulously gathered through an extensive field survey encompassing the entire study region, while secondary data was sourced from authoritative reports such as the Niti Aayog report, Indian Census, and Panchayat office documents. Following data acquisition, a rigorous process of tabulation, compilation, and analysis was conducted using advanced statistical methods, complemented by the creation of maps and diagrams. The maps were developed using Q-GIS software, and diagrams were generated with Microsoft Excel. The study's findings reveal the existence of 71 natural water springs across 10 panchayats within the study area. These springs are integral to the region’s economic activities, cultural practices, and essential for drinking and domestic purposes. As such, local communities are pivotal in the conservation, maintenance, and revitalization of these natural springs. Keywords: Local community, Kharahal region, Himalaya, Natural springs, Conservation, Q-GIS
Indian coastal zones have diverse ecological and geomorphological features; however, they are recurrently influenced by anthropogenic activities and natural disturbances. Therefore, understanding coastal land use and land cover features is essential for protecting these zones. Remote sensing is a crucial tool for identifying and quantifying coastal features with synoptic coverage. The present study focuses on land use and land cover mapping along the Tirunelveli coast using QGIS and LISS III datasets. The results revealed the following land cover classifications in the Tirunelveli coastal area: barren land (17.083 km²), built-up area (26.096 km²), dense vegetation (24.167 km²), industrial discharge (3.186 km²), industrial waste (1.716 km²), low-density vegetation (36.818 km²), sand dune plants (18.568 km²), sandy beach area (32.710 km²), scrub area (14.426 km²), Teri sand area (20.553 km²), and water bodies (including freshwater and seawater), covering 305.068 km². The overall classification accuracy was 86.85%. This study provides valuable insights into the coastal regulation zone in the Tirunelveli coastal area and may contribute to future coastal conservation research. Keywords: Tirunelveli coast, Land use land cover, QGIS, LISS III, Conservation
The rapid urbanization of Srinagar city, located in the Himalayan region, has led to significant changes in land use and land cover (LULC), resulting in an increase in the Urban Heat Island (UHI) phenomenon. This study investigates the relationship between Land Surface Temperature (LST) and LULC changes over a decade, from 2010 to 2022, focusing on four key categories: built-up areas, agricultural land, water bodies, and natural vegetation. Using Landsat satellite data, we analysed seasonal variations in LST across these categories and calculated the Urban Thermal Field Variance Index (UTFVI) to assess the ecological impact. The findings reveal a substantial increase in LST, particularly in built-up areas, where maximum temperature increased from 34.6°C in 2010 to 37.19°C in 2022. Additionally, the UTFVI analysis showed a decline in areas with "Excellent" ecological conditions, dropping from 57.91% in December 2010 to 47.24% in December 2022, while areas categorized as "Worst" ecological conditions increased, indicating a worsening UHI effect. These results highlight the growing environmental challenges posed by urbanization in Srinagar city, necessitating urgent sustainable urban planning interventions. Keywords: Land surface temperature (LST), Urban heat island (UHI), Urban thermal field variance index (UTFVI), Land use land cover (LULC), Urbanisation
Mosquito-borne diseases are those that are transmitted by the bite of an infected mosquito. Stagnant bodies of water are frequently preferred as mosquito breeding places. However, from producing eggs to the final stage, several elements contribute to its incubation, maturity, and growth to the point where it is capable of biting and transmitting diseases. The primary goal of this research is to focus on connected environmental determinants that provide optimal breeding locations and vulnerability mapping of mosquito-borne diseases using geospatial techniques and a decision-making approach. The analytical hierarchy process was combined with a geographic information system to create a map of mosquito-borne diseases in Muktsar district of Punjab state. The weights of selected variables were determined using a choice-based varied ranking method, which involved building a pair-wise comparison matrix. Initially, ten important environmental parameters were selected to determine their weight using a pair-wise comparison matrix. At the same time, the weight of each related element was employed as a geo-database to aid with overlay analysis. The consistency ratio was derived to evaluate the decision-making process and significance measurement. The consistency ratio of choice factors was found to be 0.0470, which is less than 0.1 and regarded consistent and acceptable. According to the study's findings, proximity to water bodies is a major influence, followed by moisture content, water index, availability of shade area, and the presence of vegetation in mosquito-borne disease prevalence. The current findings demonstrate the wide range of uses of satellites data and spatial techniques in epidemic diseases zonation. Keywords: Mosquito-borne diseases, Geospatial analysis, Analytic Hierarchy Process, Public health
Kozhikode is a bustling city, located on the southwest coast of India. Fast paced urbanization, industrialization and population growth have drastically altered the land use and land cover in the study area. The main objective of present study is to examine LULC change in Kozhikode city and suburbs during the year 1993 to 2023. For this purpose, supervised classification has been carried out using Landsat images and LULC change analysed for the years 1993, 2003, 2013 and 2023. The result spatio temporal changes of LULC revealed that Kozhikode city and suburbs witnessed drastic changes. Built up land boomed from 1371.12 hectares in 1993 to 13211.38 hectares in 2023. The area under agriculture, vegetation, barren land and water bodies were declined. As Kozhikode city has grown, agricultural lands are replaced by commercial complexes, residential areas and infrastructural developments. Vegetation has also been cleared to make path for urban development. As cities expand, the demand for land rises, agricultural land and vegetative area become prime target for real estate and illegal construction. Thus, the findings of this study are significant in implementing sustainable planning interventions in Kozhikode City and suburbs. Keywords: Land Use Land Cover, Urbanization, Built up, Landsat
