Local indicator of spatial association-LISA

Efek Limpahan Pertumbuhan Antar-Kabupaten/Kota di Provinsi Jawa Timur Tahun 2001–2013

Article

Jul 2015

Penelitian ini bertujuan untuk mengidentifikasi tipologi kabupaten/kota di Provinsi Jawa Timur dan menganalisis efek limpahan pertumbuhan antar-kabupaten/kota. Alat analisis yang dipergunakan adalah Tipologi Klaassen, identifikasi kutub pertumbuhan berdasarkan definisi yang dikemukakan oleh Richardson, perhitungan efek limpahan pertumbuhan, serta deteksi autokorelasi spasial dengan indeks lokal Moran dan Local Indicators of Spatial Association (LISA). Hasil penelitian ini menunjukkan bahwa kabupaten/kota yang tergolong maju dan cepat tumbuh pada tahun 2001 hingga 2013 terpusat di kawasan tengah Provinsi Jawa Timur. Konsistensi sebagai daerah cepat tumbuh dan maju/kaya yang merupakan indikator kutub pertumbuhan, ditunjukkan oleh Kota Surabaya.

Spatiotemporal analysis of mosquito-borne infections and mosquito vectors in mainland Portugal

Article

Full-text available

Jan 2025
BMC INFECT DIS

Background The incidence of mosquito-borne infections has increased worldwide. Mainland Portugal’s characteristics might favour the (re)emergence of mosquito-borne diseases. This study aimed to characterize the spatial distribution of vectors and notification rates of imported cases of mosquito-borne infections in mainland Portugal and demarcate the areas where these geographies overlap. Methods We used data from imported cases of malaria, dengue and Zika from 2009 to 2019, alongside data on the presence of mosquitoes capable of potentially transmitting these diseases at the municipality level (2009–2018). This data was provided by the National Epidemiological Surveillance System and Regional Health Administrations, based on reports from the Vector Surveillance Network. While the mosquitoes in question do not currently transmit these diseases, they have the potential to do so if there is a significant increase in pathogen circulation. A spatial cluster analysis was performed using the univariate Local Moran Index, the Bivariate Moran Local Index and the Mann-Kendall method. Results We found significant spatial variability in both notification rates of imported mosquito-borne infections and the distribution of competent mosquito species. We identified clusters of simultaneous high concentrations of vectors and imported cases of malaria in Condeixa-a-Nova (Coimbra), Cuba (Beja), Santiago do Cacém (Setúbal), Albufeira and São Brás de Alportel (Faro), most located on the Southern coast of Portugal. For dengue, we detected clusters of simultaneous high concentrations of vectors and imported cases in Paredes, in the Northern region, and Faro, on the southern coast. For Zika, no clusters were identified. Conclusion This study identified areas with high notification rates of imported cases and the presence of competent vectors. Surveillance, control, and awareness efforts are essential, as these areas may present higher risks for local transmission in the future if ecological conditions remain or become suitable, potentially evolving into foci for disease transmission.

Comparative evaluation of spatiotemporal methods for effective dengue cluster detection with a case study of national surveillance data in Thailand

Article

Full-text available

Dec 2024

Dengue fever poses a significant public health burden in tropical regions, including Thailand, where periodic epidemics strain healthcare resources. Effective disease surveillance is essential for timely intervention and resource allocation. Various methods exist for spatiotemporal cluster detection, but their comparative performance remains unclear. This study compared spatiotemporal cluster detection methods using simulated and real dengue surveillance data from Thailand. A simulation study explored diverse disease scenarios, characterized by varying magnitudes and spatial-temporal patterns, while real data analysis utilized monthly national dengue surveillance data from 2018 to 2020. Evaluation metrics included accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. Bayesian models and FlexScan emerged as top performers, demonstrating superior accuracy and sensitivity. Traditional methods such as Getis Ord and Moran’s I showed poorer performance, while other scanning-based approaches like spatial SaTScan exhibited limitations in positive predictive value and tended to identify large clusters due to the inflexibility of its scanning window shape. Bayesian modeling with a space–time interaction term outperformed testing-based cluster detection methods, emphasizing the importance of incorporating spatiotemporal components. Our study highlights the superior performance of Bayesian models and FlexScan in spatiotemporal cluster detection for dengue surveillance. These findings offer valuable guidance for policymakers and public health authorities in refining disease surveillance strategies and resource allocation. Moreover, the insights gained from this research could be valuable for other diseases sharing similar characteristics and settings, broadening the applicability of our findings beyond dengue surveillance. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-024-82212-1.

Análise espacial da concessão de benefícios previdenciários a pessoas vivendo com HIV/aids no Brasil

Article

Full-text available

Dec 2024

OBJETIVO: Analisar os padrões de associação espacial da concessão de benefícios previdenciários a pessoas vivendo com HIV/aids no Brasil entre 2004 e 2016. MATERIAIS E MÉTODOS: Estudo do tipo ecológico, utilizando dados secundários do Ministério do Trabalho e Previdência Social. As análises foram realizadas com o uso de técnicas de autocorrelação espacial, nomeadamente os índices I de Moran Global e Local. RESULTADOS: Foram concedidos 73.066 benefícios. Macrorregiões de saúde das Regiões Norte e Nordeste do país se destacaram com a formação de aglomerados de padrão baixo-baixo quando analisadas as concessões de benefícios de forma geral, em relação ao sexo e nas áreas urbanas, enquanto o estado de Santa Catarina apresentou aglomerado de padrão alto-alto para essas mesmas variáveis. Apenas nas Regiões Norte e Nordeste foi observado aglomerado de padrão alto-alto para concessão de benefícios em áreas rurais. Em macrorregiões de saúde da Região Norte, observou-se principalmente a formação de aglomerados de padrão baixo-baixo quando analisada a idade no início da doença e da incapacidade laboral. Quando analisado o tempo médio decorrido entre o início da doença e da incapacidade, um extenso aglomerado de padrão baixo-baixo cobriu grande parte das Regiões Norte e Nordeste, e outro extenso aglomerado de padrão alto-alto formou-se nas Regiões Sul e Sudeste do país. CONCLUSÃO: Os dados apresentados refletem a vulnerabilidade social que as populações das Regiões Norte e Nordeste do Brasil estão submetidas em relação à aids, particularmente nos aspectos de informalidade do trabalho e dificuldade de acesso aos serviços de saúde e previdência social.

On Farm Non-Agricultural Activities: Recent Evolution and Dynamics in Portugal

Article

Jan 2024

An increasing share of farmer revenues derives from on-farm non-agricultural activities (OFNAA), which constitute a complement to the farmer’s income and can function as a factor for the development of farms, enhancing the endogenous resources of the territories and contributing to the multifunctionality of rural areas. Therefore, it is important to understand the importance of these non-agriculture activities in the territory, their diversification, spatial trends at local level and the relation with farm’s orientation. This paper intends to analyse the OFNAA, using as object of study the Portuguese municipalities. In order to analyse the diversification of the OFNAA, a diversification index based on entropy is proposed. The relationships between OFNAA diversification and the farms’ technical-economic orientation (TEO) are also analysed using correlation matrixes, while the spatial patterns are studied, using the global Moran I and local Moran-LISA. The results provide important insights of the OFNAA dynamics and diversification. Therefore, this study provides an important tool for policy management and implementation.

A local indicator of stratified power

Article

Full-text available

Dec 2024
INT J GEOGR INF SCI

Spatial stratified heterogeneity measures spatial association by power of determinant (PD) that compares variations within strata and across space. Models based on stratified heterogeneity have been extensively used across various fields to analyze PD from a spatial perspective. However, stratified heterogeneity in the local regions has not been investigated, although it can significantly influence the overall PD measurements in large-scale studies. This study proposes a local indicator of stratified power (LISP) to analyze local spatial stratified association and demonstrate how spatial stratified association changes spatially and in local regions. The LISP model was implemented to examine the local potential determinants of the thickness variations in lake-terminating glaciers of the Greater Himalayas. The results indicate that LISP can reveal spatial association at various local positions, effectively mitigating the underestimation or overestimation of PD values in local regions that are probably missed in global spatial stratified association models. The developed LISP model provides a reliable methodological framework for exploring local spatial associations and identifying local determinants in broad fields.

Surface Morphology and Strength of Chemically Treated Saccharum Officinarum Bagasse Fibres Cement Composites of Low Carbon Concrete

Article

Full-text available

Dec 2024

This study explores the potential of Saccharum Officinarum Bagasse Fibres (SOBF) as a sustainable reinforcement in cement-based composites for affordable construction in developing countries. Focusing on locally sourced SOBF, known for its sustainability and low cost, this research aims to enhance the mechanical properties of cement composites. The study examines the impact of SOBF on compressive strength, comparing 1%, 2%, and 3% fibre content at 7 and 28 days, both treated and untreated. Scanning Electron Microscopy (SEM) analysis revealed that SOBF inclusion mitigates surface cracking through a bridging effect within the hardened cement. Results indicate that compressive strength increases up to an optimal fibre content, with 1 wt.% providing the best outcomes for both treated and untreated samples. Interestingly, untreated composites displayed higher compressive strength than their treated counterparts, likely due to the natural lignin enhancing fibre-matrix bonding. The findings demonstrate that SOBF significantly improves the mechanical properties and durability of cement composites, ideal for sustainable construction in regions prioritizing cost effectiveness and local resource utilization. This study not only highlights the benefits of integrating natural fibres like SOBF in cementitious applications but also emphasizes the importance of optimizing fibre content and treatment to enhance composite performance.

A Majority Theorem for the Uncapacitated p = 2 Median Problem and Local Spatial Autocorrelation

Article

Full-text available

Jan 2025

The existing quantitative geography literature contains a dearth of articles that span spatial autocorrelation (SA), a fundamental property of georeferenced data, and spatial optimization, a popular form of geographic analysis. The well-known location–allocation problem illustrates this state of affairs, although its empirical geographic distribution of demand virtually always exhibits positive SA. This latent redundant attribute information alludes to other tools that may well help to solve such spatial optimization problems in an improved, if not better than, heuristic way. Within a proof-of-concept perspective, this paper articulates connections between extensions of the renowned Majority Theorem of the minisum problem and especially the local indices of SA (LISA). The relationship articulation outlined here extends to the p = 2 setting linkages already established for the p = 1 spatial median problem. In addition, this paper presents the foundation for a novel extremely efficient p = 2 algorithm whose formulation demonstratively exploits spatial autocorrelation.

Crime Hotspot Analysis and Mapping Using Geospatial Technology in Dessie City, Ethiopia

Preprint

Full-text available

Dec 2024

Over the past few decades, crime and delinquency rates have increased drastically in many countries; nevertheless, it is important to note that crime trends can differ significantly by geographic region. This study's primary goal was to use geographic technology to map and analyze Dessie City's crime patterns. To investigate the geographic clustering of crime, the researchers used semivariogram modeling and spatial autocorrelation analysis with Moran'sI. The neighborhoods of Hote, Arada, and Segno in Dessie's central city were found to be crime-prone "hot spot" locations, as evidenced by statistically significant high Z-scores ranging from 0.037 to 4.608. On the other hand, low negative Z-scores ranging from -3.231 to -0.116 indicated "cold spot" concentrations of crime in the city's north-central sub-cities of Menafesha and Bounbouwha. With an index of 0.027492 and a Z-score of 3.297616 (p<0.01), the analysis overall showed a substantial positive spatial autocorrelation, suggesting a clustered pattern of crime in Dessie. The majority of crimes showed a north-south directionality, except for murder, which trended from northeast to southwest. The mean center of all crime types was found in the central Hote area. To address the complicated problem of rising crime rates in Dessie and other developing metropolitan areas, more focused and efficient enforcement techniques, and resource deployment can be informed through the knowledge acquired from the geospatial analysis.

Why are some places developed and other places lagging behind? An analysis of 295 Chinese cities

Article

Full-text available

Jan 2025
ANN REGIONAL SCI

There are huge differences in the levels of social and economic development among the world’s regions, and the gaps between regions have not narrowed despite rapid economic development. Understanding the impact of factors other than political factors on socioeconomic development can help all countries to find solutions that go beyond ideology. To test this hypothesis, we selected 295 Chinese cities at the same administrative level and used the entropy weighting method, a spatial econometric model, and a geographical and temporal weighted regression model to identify the reasons for regional development differences. Unlike the methods used in previous studies, this approach let us identify the relative importance of many forces that are simultaneously driving development and their variation among regions. We found that industrial upgrading, industrialization, fiscal decentralization, marketization, and education were important factors to promote regional economic growth, whereas economic growth decreased with increasing elevation and environmental pollution. These were the primary factors that explained why some places were more highly developed and other places lagged behind. To narrow the regional development gap, it will be necessary to upgrade a region’s industrial structure, improve market mechanisms, improve the education system, and implement targeted and differentiated development policies that account for each region’s unique needs. Our results will provide a reference not only for China but also for the world to support efforts to eradicate poverty and achieve more regionally balanced development.

Identifying critical road sections using the weighted periodicity mined from trajectory data for efficient urban transportation

Article

Full-text available

Dec 2024

Critical road sections (CRS) are part of links of the road network, which have an obvious influence on urban transportation systems. Identifying CRS would contribute to improving efficient traffic management. However, existing studies pay less attention to the influence of traffic temporal dynamism on CRS and the spatial disparity of CRS in different temporal scenarios. We propose a method to identify CRS in urban road network. The new method takes sparse tensor decomposition and reconstruction for the imputation of driving speed that is calculated from trajectory data. Then, empirical mode decomposition is applied to calculate the weighted periodicity for each time series of driving speed. Finally, CRS are determined according to local spatial autocorrelation of the weighted periodicity. Taking the urban area of Xi’an City, China, as a case study, the result show that the new method could effectively achieve the imputation of speed information (R² >0.67). The weighted periodicity could characterize the temporal dynamism of driving speed with considering the aliasing effect of traffic modes. The CRS reflect the multi-center characteristics of urban transportation systems, and show obvious spatial disparity in holiday and workday. The CRS identified by the proposed method could be applied to improving urban traffic management and maintaining efficient urban transportation.

Second-order and local characteristics of network intensity functions

Preprint

Dec 2017

The last decade has witnessed an increase of interest in the spatial analysis of structured point patterns over networks whose analysis is challenging because of geometrical complexities and unique methodological problems. In this context, it is essential to incorporate the network specificity into the analysis as the locations of events are restricted to areas covered by line segments. Relying on concepts originating from graph theory, we extend the notions of first-order network intensity functions to second-order and local network intensity functions. We consider two types of local indicators of network association functions which can be understood as adaptations of the primary ideas of local analysis on the plane. We develop the node-wise and cross-hierarchical type of local functions. A real dataset on urban disturbances is also presented.

STEP_WISE: Spatiotemporal Epidemiology of Primary Waterborne Infections -Cryptosporidium and VTEC

Technical Report

Full-text available

Jan 2025

Climate, environment, geography, and spatial education inequality in sub-Saharan Africa

Article

Full-text available

Jan 2025
Prospects

Space, beyond standard urban/rural divisions, plays a leading role in the diffusion of educational inequalities. In this paper, using geo-localization and Demographic and Health Surveys (DHS), we analyze educational access (never-been-to-school and out-of-school rates at primary and secondary levels) and the impact of climate variables (aridity, temperature, rainfall, enhanced vegetation index) for 5,279 communities from 10 countries in sub-Saharan Africa. We find that space matters for educational access and negative impacts of climate variables on access after accounting for communities' contextual backgrounds in spatial econometric models, with climate and spatial educational inequality operating more powerfully in marginalized communities. Educational policies aimed at boosting educational access should consider space-based interventions and mitigating strategies against climate change.

Correlation Analysis of the Spatial Characteristics and Influencing Factors of the Block-Scale Thermal Environment: A Case Study in Jinan, China

Article

Jan 2025

Using Spatial Analysis to Assess Cohesion For Sustainable Development Goals

Article

Full-text available

Jan 2025

Our study explores the integration of spatial analysis methodologies within the context of the Sustainable Development Goals (SDGs) at the regional level in European Union countries. The application of spatial analysis allows for a nuanced understanding of SDGs progress and obstacles within specifi c territorial units. Adopted by the United Nations, the Sustainable Development Goals (SDGs) provide an all-encompassing framework for tackling global issues in the social, economic , and environmental spheres. Through the examination of geographic data, spatial analysis techniques give a distinct perspective and can provide important insights into regional inequities and localised diffi culties. Spatial analysis-which includes spatial autocorrelation tools-provides a visual narrative of regional cohesion and off ers a deep understanding of the distribution and connectivity of SDGs accomplishments across various territorial units. For planners, stakeholders, and politicians, this visual aid is invaluable as it facilitates informed decision-making that leads to more integrated policies. Furthermore, the application of spatial analysis methods makes it easier to allocate resources effi ciently and track progress towards the SDGs. It allows for authorities , decision-makers, and interested parties to plan resources eff ectively, rank interventions in order of importance, and monitor the eff ects of programmes locally. Our study extensively employs available data at the NUTS 3 level, providing a comprehensive measurement of SDGs achievements. Through a close examina-Romanian Statistical Review nr. 4 / 2024 37 tion of data at the NUTS 3 level, our research off ers a comprehensive evaluation of SDGs accomplishments, facilitating an in-depth comprehension of advancements and discrepancies within certain territorial units. Additionally, this strategy is in line with the cohesion process strategically, making it possible to assess these goals' contributions to fair development and regional cohesion more thoroughly. Our research emphasises the relationship between achieving the Sustainable Development Goals and regional cohesion by focusing on the cohesion process. This emphasises how important it is to accomplish not just the individual goals but also to promote inclusivity, lessen inequality, and unite people in various geographical contexts. Thus, our research contributes to a more inclusive and unifi ed approach to sustainable development within the European Union by enhancing our understanding of SDGs progress at a granular level and highlighting the signifi cance of these accomplishments in fostering cohesion. Furthermore, we stress the signifi cance of utilising spatial analytic techniques to improve the execution, observation, and assessment of plans intended to accomplish the SDGs.

Using Spatial Analysis to Assess Cohesion For Sustainable Development Goals

Article

Jan 2025

Our study explores the integration of spatial analysis methodologies within the context of the Sustainable Development Goals (SDGs) at the regional level in European Union countries. The application of spatial analysis allows for a nuanced understanding of SDGs progress and obstacles within specific territorial units. Adopted by the United Nations, the Sustainable Development Goals (SDGs) provide an all-encompassing framework for tackling global issues in the social, eco- nomic, and environmental spheres. Through the examination of geographic data, spa- tial analysis techniques give a distinct perspective and can provide important insights into regional inequities and localised difficulties. Spatial analysis—which includes spa- tial autocorrelation tools—provides a visual narrative of regional cohesion and offers a deep understanding of the distribution and connectivity of SDGs accomplishments across various territorial units. For planners, stakeholders, and politicians, this visual aid is invaluable as it facilitates informed decision-making that leads to more integrated policies. Furthermore, the application of spatial analysis methods makes it easier to allocate resources efficiently and track progress towards the SDGs. It allows for au- thorities, decision-makers, and interested parties to plan resources effectively, rank interventions in order of importance, and monitor the effects of programmes locally. Our study extensively employs available data at the NUTS 3 level, providing a comprehensive measurement of SDGs achievements. Through a close examination of data at the NUTS 3 level, our research offers a comprehensive evaluation of SDGs accomplishments, facilitating an in-depth comprehension of advancements and discrepancies within certain territorial units. Additionally, this strategy is in line with the cohesion process strategically, making it possible to assess these goals’ contributions to fair development and regional cohesion more thoroughly. Our research emphasises the relationship between achieving the Sustain- able Development Goals and regional cohesion by focusing on the cohesion process. This emphasises how important it is to accomplish not just the individual goals but also to promote inclusivity, lessen inequality, and unite people in various geographical contexts. Thus, our research contributes to a more inclusive and unified approach to sustainable development within the European Union by enhancing our understanding of SDGs progress at a granular level and highlighting the significance of these accom- plishments in fostering cohesion. Furthermore, we stress the significance of utilising spatial analytic techniques to improve the execution, observation, and assessment of plans intended to accomplish the SDGs.

Spatial-temporal distribution and evolution of medical and health talents in China

Article

Full-text available

Jan 2025
BMC PUBLIC HEALTH

Background In the context of public health emergencies, the presence of medical and health talents (MHT) is critically important for support in any country or region. This study aims to analyze the spatial and temporal distributions and evolution of MHT in China and propose strategies and recommendations for promoting a balanced distribution. Methods This research used data from 31 provinces in China to construct a multidimensional index system for measuring the agglomeration level of MHT. The indices include talent agglomeration density (TAD), talent agglomeration scale (TAS), talent agglomeration intensity (TAI), and talent agglomeration equilibrium (TAE). Using provincial data from the years 1982, 1990, 2000, 2010, and 2020, a spatiotemporal analysis of the MHT agglomeration levels was conducted. Furthermore, the regional dynamic distribution of MHT was analyzed using kernel density estimation diagrams. The spatial autocorrelation of MHT was assessed through global and local Moran’s I, and the spatial gap and decomposition of MHT were analyzed using the Dagum Gini coefficient. Results From the temporal level, the TAD and TAI of MHT showed an increasing trend over the studied period, whereas TAS decreased and TAE first increased and then decreased from 1982 to 2020. At the spatial level, the TAD, TAS, TAI, and TAE of MHT exhibited varied patterns among the eastern, central, and western regions of China, showing significant geographical disparities, generally demarcated by the Hu Huanyong Line. The regional dynamic distribution level of MHT in the country and the three regions were expanding. Spatial autocorrelation analysis using global and local Moran’s I for TAD, TAS, TAI, and TAE demonstrated significant regional differences. The Dagum Gini coefficient of TAD, TAS, TAI, and TAE revealed divergent trends in regional disparities, with overall declines in disparities for TAD and TAI, a slight increase for TAS, and fluctuating patterns for TAE. Conclusions From a temporal perspective, the overall number of MHT in China has been increasing annually at the national and provincial levels. From the spatial perspective, TAD, TAS, TAI, and TAE exhibit significant differences among the three regions. Kernel analysis reveals that the distribution differences are gradually expanding in national level and varying in regional level. Moreover, the global and local Moran’s I indices reveal varying spatial autocorrelation for TAD, TAS, TAI, and TAE. The Dagum Gini coefficients of TAD, TAS, TAI, and TAE show different patterns of decomposition.

Opportunities and Agglomeration Forces in the Automotive and Auto Parts Industry in Mexico in the Presence of Chinese Companies

Article

May 2025

Abstract Mexico has emerged as one of the world's leading producers and exporters of vehicles. With the entry into force of the USMCA, an increase in North American regional content in the automotive industry is expected, presenting an opportunity to boost technology transfer and the development of local capabilities. The automotive and auto parts industry (AAI) in Mexico exhibits spatial concentration patterns, generating agglomeration economies linked to knowledge spillovers, specialized labor, local inputs, and urbanization economies. However, few studies have analyzed these forces with a high level of territorial disaggregation, particularly regarding the presence of Chinese companies in the sector. This study examines the existence of agglomeration economies in the AAI across nine micro-regions in Mexico, home to 13 Chinese companies. Using kernel density maps and global and local autocorrelation indices, the analysis explores their local and regional impact. The findings provide evidence of agglomeration economies in five regions, with a trend toward further growth. Keywords: Economic density, Localization economies, Urbanization economies, Sectorial analysis, Spatial analysis. JEL codes: C81, R12, L60, O18. Resumen México se ha posicionado como uno de los principales productores y exportadores de vehículos a nivel mundial. Con la entrada en vigor del T-MEC, se espera un aumento en el contenido regional norteamericano en la industria automotriz, lo que representa una oportunidad para impulsar la transferencia de tecnología y el desarrollo de capacidades locales. La industria automotriz y de autopartes (IAA) en México muestra patrones de concentración espacial, generando economías de aglomeración vinculadas a externalidades de conocimiento, trabajadores especializados, insumos locales y economías de urbanización. Sin embargo, existen pocos estudios que analicen estas fuerzas con alto nivel de detalle territorial, especialmente en relación con empresas chinas en el sector. Este trabajo examina la presencia de economías de aglomeración en la IAA en nueve micro-regiones mexicanas, donde operan 13 empresas chinas. A través de mapas de densidad kernel e índices de autocorrelación global y local, se verifica su impacto local y regional. Los resultados evidencian economías de aglomeración en cinco regiones, con una tendencia creciente. Palabras clave: Densidad económica, Economías de localización, Economías de urbanización, Análisis sectorial, Análisis espacial. Códigos JEL: C81, R12, L60, O18

Patriarchy, Fertility and Excess Female Child Mortality in India

Article

Full-text available

Jan 2025

Research investigating association between patriarchy and demographic behavior is limited in India. The only study on this subject utilized 1981 Indian Census data to examine associations between patriarchy and fertility. We examined the association of patriarchy, measured using India Patriarchy Index (IPI), with total fertility rate (TFR) and excess female child mortality in India. Additionally, we examined independent associations of the 5 dimensions included in the IPI with the two outcomes. We used univariate and bivariate Local Indicators of Spatial Autocorrelation, multivariable ordinary least squares and spatial error- regressions to examine the associations. Spatial heterogeneity beyond the north–south divide was evident in the spatial association of IPI with TFR and excess female child mortality. Results show positive association of IPI with TFR and excess female child mortality. While son preference and socio-economic domination were positively associated with TFR, domination of men over women and son preference were positively associated with excess female child mortality. This study is the first of its kind to examine the association of a novel measure of patriarchy with TFR and excess female child mortality. As patriarchy is deep-rooted in Indian society, a great deal of effort is needed to shift these traditionally held social norms and practices.

Upscaling ecosystem studies on coral reefs

Thesis

Full-text available

Jan 2024

Lucas Lutzenkirchen

Using Machine Learning Approach to Cluster Marine Environmental Features of Lesser Sunda Island

Article

Dec 2024

Evellin Lusiana

Mapping marine ecosystems is acknowledged as a vital tool for implementing ecosystem services in practical situations. It provides a framework for effective marine spatial planning, enabling the designation of marine protected areas (MPAs) that consider ecological connectivity and habitat requirements. It also helps pinpoint areas of high biodiversity or ecological significance, allowing conservationists to prioritize these regions for protection and management. Numerous studies over decades have produced a vast amount of data that illustrates the features of the marine ecosystem. Therefore, the unsupervised learning is a promising technique to map marine ecosystem based on its environmental features. This study aims to compare unsupervised learning techniques to analyze marine environmental features in order to map marine ecosystem in Lesser Sunda waters. Eleven global environmental variables were accessed from global databases. The Lesser Sunda waters were delineated into groups according to their environmental characteristics using four unsupervised learning techniques: k-mean, fuzzy c-mean, self-organizing map (SOM), and density-based spatial clustering of applications with noise (DBSCAN). According to the findings, the Lesser Sunda waters can be divided into five to nine clusters, each with distinct environmental features. Moreover, the fuzzy c-mean method's clustering result outperformed the others based on the highest Silhouette (0.2204478) and Calinski-Harabasz (1741.099) Index. As an unsupervised learning technique, fuzzy c-mean clustering offered good performance in delineating Lesser Sunda Island marine waters with five clusters. The clustering results mostly consistent with existing conservation programs, even though there are several areas which needed international and multinational organization collaboration to effectively accomplish marine conservation objectives.

Transforming CCTV cameras into NO

_2

sensors at city scale for adaptive policymaking

Preprint

Full-text available

Dec 2024

Air pollution in cities, especially NO\textsubscript{2}, is linked to numerous health problems, ranging from mortality to mental health challenges and attention deficits in children. While cities globally have initiated policies to curtail emissions, real-time monitoring remains challenging due to limited environmental sensors and their inconsistent distribution. This gap hinders the creation of adaptive urban policies that respond to the sequence of events and daily activities affecting pollution in cities. Here, we demonstrate how city CCTV cameras can act as a pseudo-NO\textsubscript{2} sensors. Using a predictive graph deep model, we utilised traffic flow from London's cameras in addition to environmental and spatial factors, generating NO\textsubscript{2} predictions from over 133 million frames. Our analysis of London's mobility patterns unveiled critical spatiotemporal connections, showing how specific traffic patterns affect NO\textsubscript{2} levels, sometimes with temporal lags of up to 6 hours. For instance, if trucks only drive at night, their effects on NO\textsubscript{2} levels are most likely to be seen in the morning when people commute. These findings cast doubt on the efficacy of some of the urban policies currently being implemented to reduce pollution. By leveraging existing camera infrastructure and our introduced methods, city planners and policymakers could cost-effectively monitor and mitigate the impact of NO\textsubscript{2} and other pollutants.

Spatio-temporal dynamics of building carbon emissions via a GIS-LCA integrated framework

Article

Dec 2024

Building carbon emissions (BCEs) from the construction industry are crucial to realizing the ‘Dual-Carbon’ Goals of China, but their spatio-temporal and stage differences are not fully understood. Here, we explored the spatial and temporal differences of building carbon emissions in the Yangtze River Delta urban agglomeration by combining life cycle assessment and spatial analysis, and further compared the contribution rates of each influencing factor to BCEs based on the LMDI-STIRPAT model. The results revealed that from 2005 to 2020, the growth rate of building carbon emissions in the Yangtze River Delta urban agglomeration slowed after 2013, with a spatial trend of ‘slow in the east and fast in the west’. With respect to the whole process, the contribution of building materials production and transportation to BCEs is the most significant. Additionally, the types of building materials and energy used in each stage have changed dramatically over the 16 years. Furthermore, GDP per capita and carbon intensity are the strongest positive and negative drivers, respectively. These findings provide a complete understanding for a regional exploration of BCEs and contribute to formulating low-carbon building policies within urban agglomerations.

Descriptive spatial analysis of LSD outbreaks in Uttar Pradesh (2022-2023)

Article

Jan 2024

Research on the evolutionary characteristics and mechanism of production-living-ecological space in Shanxi Province, China

Article

Full-text available

Dec 2024

An investigation of the evolutionary characteristics and internal driving mechanisms of territorial space since the reform and opening up is essential. The study will guide the orderly development and rational layout of territorial space, as well as achievement transformation and high-quality development in Shanxi Province. We used land use data from 1980 to 2020, which was divided into four periods, to examine the changes in production-living-ecological spatial pattern in Shanxi Province. Various methods, including the territorial spatial transfer matrix, standard deviation ellipse and spatial autocorrelation, were employed to analyse the evolution of the territorial spatial pattern. Applying GeoDetector as the primary tool, we conduct research on the mechanisms underlying the evolution of this spatial pattern. The results indicated that Shanxi Province exhibits distinct differentiation characteristics in both the horizontal and vertical spatial dimensions. Over the 40-year period from 1980 to 2020, the territorial spatial pattern of Shanxi Province transitioned from gradual change to drastic change to moderate change. The production space (PS) and ecological space (ES) decreased, while the living space (LS) significantly increased. The territorial spatial pattern of Shanxi Province exhibited a northeast‒southwest distribution pattern, and the changes in the centre of gravity of the production-living-ecological spaces varied in direction. The spatial distribution of land in Shanxi Province is influenced by both natural factors and human activities, leading to changes in its territorial pattern over time. The primary catalyst for the development of production space (PS) is grain production, while the major determinants of the development of living space (LS) are the overall gross domestic product (GDP) and public financial expenditure. Thus, topography greatly influences ecological space (ES).

Spatial function-on-function regression

Preprint

Full-text available

Dec 2024

We introduce a spatial function-on-function regression model to capture spatial dependencies in functional data by integrating spatial autoregressive techniques with functional principal component analysis. The proposed model addresses a critical gap in functional regression by enabling the analysis of functional responses influenced by spatially correlated functional predictors, a common scenario in fields such as environmental sciences, epidemiology, and socio-economic studies. The model employs a spatial functional principal component decomposition on the response and a classical functional principal component decomposition on the predictor, transforming the functional data into a finite-dimensional multivariate spatial autoregressive framework. This transformation allows efficient estimation and robust handling of spatial dependencies through least squares methods. In a series of extensive simulations, the proposed model consistently demonstrated superior performance in estimating both spatial autocorrelation and regression coefficient functions compared to some favorably existing traditional approaches, particularly under moderate to strong spatial effects. Application of the proposed model to Brazilian COVID-19 data further underscored its practical utility, revealing critical spatial patterns in confirmed cases and death rates that align with known geographic and social interactions. An R package provides a comprehensive implementation of the proposed estimation method, offering a user-friendly and efficient tool for researchers and practitioners to apply the methodology in real-world scenarios.

Framework for Monitoring the Spatiotemporal Distribution and Clustering of the Digital Society Index of Indonesia

Article

Full-text available

Dec 2024

Digital disparities remain a significant challenge in Indonesia, particularly across its diverse regions, with uneven access to digital infrastructure, skills, and economic opportunities. This study aims to map these digital disparities at the district level, analyze the spatial distribution and clustering of digital transformation using the Digital Society Index of Indonesia (IMDI), and investigate the key drivers of digital inequality across four core pillars: Infrastructure and Ecosystem, Digital Skills, Empowerment, and Jobs. To measure the IMDI, primary data were collected from the industrial sector and the general population over three years (2022–2024), combined with secondary data on internet usage and service standards. A multistage random sampling approach ensured representativeness, considering demographic variations and industrial segments. The analysis employed spatiotemporal methods to capture temporal trends and spatial clustering. The results revealed a significant IMDI increase from 37.80 in 2022 to 43.18 in 2023, followed by stability at 43.34 in 2024. The hotspots of digital transformation remain concentrated on Java Island, while low spots persist in eastern Indonesia. This study provides critical insights into Indonesia’s digital readiness, identifying priority areas for targeted interventions to bridge the digital divide and foster equitable digital development.

How Can Geostatistics Help Us Understand Deep Learning?

Conference Paper

Full-text available

Dec 2024

Fluxos e padrões de deslocamento de idosos no sistema de transporte coletivo da Região Metropolitana de Belo Horizonte (MG)

Article

Dec 2024

Com o aumento no volume e proporção de idosos na população brasileira, reflexo do processo de transição demográfica, torna-se crucial a elaboração e implementação de políticas públicas que priorizem a qualidade de vida, o bem-estar coletivo e a inserção desse grupo nas diversas dimensões da vida urbana, incluindo o acesso e a mobilidade no território. Embora existam muitos estudos sobre o envelhecimento populacional no Brasil, especialmente nas áreas de saúde e economia, é importante expandir a abordagem temática, de modo a incluir o debate sobre o acesso, mobilidade e a oferta de transporte público coletivo, considerados como fundamentais ao pleno exercício da cidadania e de garantia do direito à cidade. Nesse sentido, o presente artigo tem como objetivo principal analisar os fluxos e os padrões de deslocamento por transporte coletivo da população idosa na Região Metropolitana de Belo Horizonte, dado o recente e acentuado processo de envelhecimento da população. Para tanto, foram utilizados os dados da Pesquisa Origem Destino de 2002 e 2012 e do estoque de população (total e idosos) dos Censos Demográficos de 2000 e 2010 para a proposição de indicadores de mobilidade e dos padrões espaciais relacionados ao transporte coletivo por ônibus. Em geral, os resultados confirmaram o esperado aumento no total de viagens diárias realizadas por idosos, acima do esperado pela mudança na estrutura etária. Observou-se, ainda, um crescimento menos acentuado na utilização do sistema de transporte coletivo por ônibus pelos idosos, o que é coincidente com o padrão geral da população. Trata-se, pelo menos aparentemente, de um caminho oposto à busca da mobilidade urbana sustentável, em prol do uso do transporte coletivo e/ou ativos, tal como preconizado pelos programas de mobilidade sustentável das políticas públicas federal e municipais.

Spatiotemporal patterns of online food delivery services before the Covid-19 pandemic

Article

Full-text available

Dec 2024

Public health concerns of the Covid-19 pandemic and the popularity of on-demand mobility services have led to a recent and prominent increase in online food delivery (OFD) service adoption. While app-based services that offer restaurant customers the convenience of a freshly prepared meal delivered to any location have existed, their present and future impacts to urban transportation networks and landscapes have become ever more apparent since the pandemic’s onset and subsequent restrictions on restaurant dining. By analyzing route-level data collected by a ridehailing driver assistant app between October 2015 and October 2019, this study informs a baseline understanding of where and when these on-demand food delivery services were used within the Phoenix metro area prior to the pandemic. This identification of the spatiotemporal patterns of OFD service is accompanied by the estimation of traditional and spatially lagged negative binomial models of delivery counts using a robust set of predictors of the built environment and socioeconomic context found at the trip destination. Study results indicate that on-demand food delivery services were popular during dinner hours corresponding with evening peak travel and in neighborhoods characterized by higher activity density, greater drinking establishment access, and increased shares of residents under 45 years old.

Space–Time Analysis of Peste des Petits Ruminants in Mali and Identification of Risk Factors

Article

Full-text available

Dec 2024
TRANSBOUND EMERG DIS

Livestock farming is an important part of Mali’s economy and a major source of income for the rural population especially women. One of the major constraints to this activity is high burden of animal disease, in particular peste des petits ruminants (PPR), which hinder the productivity of small ruminants and thus reduces the income of livestock farmers. This disease that has an effective vaccine is subjected to a worldwide eradication program. The aim of this study is therefore to develop risk maps and identify the disease’s risk factors to inform national vaccination strategy in Mali. This tool will help decisions-makers rationalize the limited resources available for disease control. A compilation of retrospective cases of PPR from 2011 to 2023 was used to generate risk maps using multivariable regression models and geographically weighted regression. Results show that the southern regions of Mali are more at risk than the northern. PRR cases occur more during rainy and hot dry seasons. Parameters such as railroads length, rainfall, and watering points were identified as risk factors for the spread of the disease. These results point out high priority areas during a risk-based vaccination campaign against PPR in Mali.

The Spatial and Temporal Characteristics of Ecosystem Service Trade-Offs and Synergistic Relationships in a Mountainous Region of Northern China

Article

Full-text available

Dec 2024
ISPRS

Understanding ecosystem service trade-offs and synergies is the foundation for achieving the efficient management of the ecosystem and improving human well-being. Therefore, in this paper, multi-scale trade-offs and synergies among eleven secondary ecosystem service (ES) types of four ecosystem service categories in the mountainous areas of North China in 2015 are assessed using statistical methods and spatial analysis, and their driving factors are analyzed, including natural factors and socioeconomic factors. The results show that for the study area, only the raw material production service and nutrient cycle maintenance service, water supply service and hydrological regulation service, environmental purification service and biodiversity maintenance service, environmental purification service and aesthetic landscape service, and biodiversity maintenance service and aesthetic landscape service show extremely strong synergistic correlations at four spatial scales. The spatial autocorrelation among services at different scales is basically consistent with the statistical correlation, but the degree of correlation varies. Unlike the grid, township, and county scales, all service pairs are spatially autocorrelated across the study area at the land use type scale, and the clustering characteristics are more obvious and similar. All service pairs are synergistic with low–low values at the mountain–plain junction in the Taihang Mountain (THM) and in the northern part of the Bashang region (BSR). The spatial trade-offs and synergies of the regulating and maintenance services in the study area are closely related to the spatial distribution of land use types. The main natural influence on the synergistic trade-offs of ecosystem services (ESs) at the township scale is elevation, while socioeconomics are mainly influenced by population and GDP. This study can contribute to strengthening decision makers’ understanding of the spatial scales of ES relationships in mountain areas and the extent to which different natural and socioeconomic factors influence them.

Spatiotemporal Analysis of Lumpy Skin Disease in Türkiye (2013-2021)

Article

Full-text available

Sep 2024
ACTA VET-BEOGRAD

Tuba Bayir

Lumpy skin disease (LSD) is a serious, transboundary disease that affects cattle all over the world and results in considerable productivity losses. Although Türkiye’s first outbreak of LSD was reported in August 2013, there is very little information available about the outbreak’s spatiotemporal distribution or severity. GIS-based data analysis provides crucial tools for describing the spatial epidemiology of the disease by assessing the spatial distribution of LSD across time. This study used information on outbreaks reported to the the World Animal Health Organization (WOAH-OIE) between 2013 and 2021 to conduct a retrospective study on the epidemiology of LSD in Türkiye. Differences in the number of reported outbreaks and cases across different regions, provinces, months, and years were evaluated and descriptive statistics were calculated. In addition, spatial statistical tests (Local Moran’s I and Getis-Ord Gi*) and Geographical Information Systems (GIS) were used to assess LSD outbreaks that had taken place at the province level in Türkiye. Possible epidemiological clusters of LSD were identified. A total of 1787 outbreaks and 10109 cases of LSD were reported from 75 out of 81 provinces of Türkiye during the course of the nine-year period. Hotspots for the circulation of LSD were identified in the Aegean, Southeastern and Eastern regions using spatial cluster analyses and it was observed that the spatial autocorrelation of LSD cases is positive across the country. The findings from this study, it may help us comprehend the disease’s spatial character and offer authorities the beneficial information for surveillance efforts.

METROPOL KENTLERDE HEMŞEHRİ KÜMELENMESİ ÜZERİNE BİR MEKANSAL ANALİZ ÇALIŞMASI BURSA’DAKİ- ERZURUMLULAR ÖRNEĞİ

Article

Full-text available

Dec 2024

Nedeni, oluşum süreci ve sonuçları göç eden kişi, aile veya topluluğa göre değişen göç, tek bir olay üzerinden incelenebileceği gibi genel değendirmelerle de ele alınabilir. Buradan hareketle insanların mekâna dağılışı ve bu dağılışlarında etkili olan birçok farklı etmenden bahsedebiliriz. Bu çalışma ile Bursa’da ikamet eden Erzurumluların mekansal kümelenme alanları tespit edilmeye çalışılmıştır. Bursa ilinde ikamet edenlerin Bursalılar dışında en çok Erzurumluların olması Erzurumlular özelinde bu çalışmanın hazırlanmasında etkili olmuştur. Bununla birlikte insanları bir arada tutan sosyo-kültürel unsurlar ile ekonomik olanakların Erzurumluların mekânsal kümelenmeleri üzerindeki etkisi karma çalışma deseni ile ortaya çıkarılmaya çalışılmıştır. Endeksa veri tabanı ile sahada gerçekleştirilen görüşmelerden elde edilen bilgiler kullanılmıştır. Bu çalışmanın amacı Bursa kentine Erzurum’dan göç edenler ve mekânsal kümelenme arasındaki ilişkiyi ortaya koymaktır. Buradan hareketle Bursa’ya göç ile gelen ve bugün de Bursa’da ikamet eden Erzurumluların mekânsal dağılışlarını analiz ederek ikamet ettikleri yerleri neden seçtikleri, seçimlerinde etkili olan unsurları ortaya çıkarıp, göç teorilerinden Network Göç Teorisi (göçmen ağları teorisi) ile ilişkilendirerek açıklanmaya çalışılmıştır. Ayrıca yerleştikleri mahallelerin coğrafi özelliklerini belirlemek, bu mahallelerde ortaya çıkan sosyo-kültürel yapının mekânı şekillendirmesi üzerinde durup Erzurumluların birbirleri ve diğer insanlarla ilişkilerinin seyrinin mekân üzerindeki izleri ortaya çıkarılmıştır.

Spatio-Temporal Modelling of Road Traffic Fatalities in the Western Cape

Article

Nov 2024

A machine learning model capable of predicting the probability of road fatal events in both time and space is developed. By aggregating relevant features of the Western Cape into an H3 grid, the model learns patterns of fatal events. Traditional machine learning and deep learning techniques are employed to understand the relationship between aggregated features and road fatal events. The models are compared against each other and against historical average models currently used in the industry. This study represents the first attempt to use machine learning techniques to model road traffic fatalities in South Africa, specifically in the Western Cape.

New Framework of Getis-Ord's Indexes Associating Spatial Autocorrelation with Interaction

Preprint

Mar 2018

Yanguang Chen

Spatial autocorrelation and spatial interaction are two important analytical processes for geographical analyses. However, the internal relations between the two types of models have not been brought to light. This paper is devoted to integrating spatial autocorrelation analysis and spatial interaction analysis into a logic framework by means of Getis-Ord's indexes. Based on mathematical derivation and transform, the spatial autocorrelation measurements of Getis-Ord's indexes are reconstructed in a new and simple form. A finding is that the local Getis-Ord's indexes of spatial autocorrelation are equivalent to the rescaled potential energy indexes of spatial interaction theory based on power-law distance decay. The normalized scatterplot is introduced into the spatial analysis based on Getis-Ord's indexes, and the potential energy indexes are proposed as a complementary measurement. The global Getis-Ord's index proved to be the weighted sum of the potential energy indexes and the direct sum of total potential energy. The empirical analysis of the system of Chinese cities are taken as an example to illustrate the effect of the improved methods and measurements. The mathematical framework newly derived from Getis-Ord's work is helpful for further developing the methodology of geographical spatial modeling and quantitative analysis.

New Approaches for Calculating Moran's Index of Spatial Autocorrelation

Preprint

Full-text available

Jun 2016

Yanguang Chen

Spatial autocorrelation plays an important role in geographical analysis, however, there is still room for improvement of this method. The formula for Moran's index is complicated, and several basic problems remain to be solved. Therefore, I will reconstruct its mathematical framework using mathematical derivation based on linear algebra and present four simple approaches to calculating Moran's index. Moran's scatterplot will be ameliorated, and new test methods will be proposed. The relationship between the global Moran's index and Geary's coefficient will be discussed from two different vantage points: spatial population and spatial sample. The sphere of applications for both Moran's index and Geary's coefficient will be clarified and defined. One of theoretical findings is that Moran's index is a characteristic parameter of spatial weight matrices, so the selection of weight functions is very significant for autocorrelation analysis of geographical systems. A case study of 29 Chinese cities in 2000 will be employed to validate the innovatory models and methods. This work is a methodological study, which will simplify the process of autocorrelation analysis. The results of this study will lay the foundation for the scaling analysis of spatial autocorrelation.

Sensitivity of codispersion to noise and error in ecological and environmental data

Preprint

Jan 2018

Codispersion analysis is a new statistical method developed to assess spatial covariation between two spatial processes that may not be isotropic or stationary. Its application to anisotropic ecological datasets have provided new insights into mechanisms underlying observed patterns of species distributions and the relationship between individual species and underlying environmental gradients. However, the performance of the codispersion coefficient when there is noise or measurement error ("contamination") in the data has been addressed only theoretically. Here, we use Monte Carlo simulations and real datasets to investigate the sensitivity of codispersion to four types of contamination commonly seen in many real-world environmental and ecological studies. Three of these involved examining codispersion of a spatial dataset with a contaminated version of itself. The fourth examined differences in codisperson between plants and soil conditions, where the estimates of soil characteristics were based on complete or thinned datasets. In all cases, we found that estimates of codispersion were robust when contamination, such as data thinning, was relatively low (<15\%), but were sensitive to larger percentages of contamination. We also present a useful method for imputing missing spatial data and discuss several aspects of the codispersion coefficient when applied to noisy data to gain more insight about the performance of codispersion in practice.

MuSpAn: A Toolbox for Multiscale Spatial Analysis

Preprint

Full-text available

Dec 2024

The generation of spatial data in biology has been transformed by multiplex imaging and spatial-omics technologies, such as single cell spatial transcriptomics. These approaches permit a detailed mapping of cell populations and phenotypes within the tissue context, which reveals that tissues are complex ecosystems that include multiple organisational structures over different length scales. Quantitative methods for maximising the information that can be retrieved from these images have not kept pace with technological advances in platforms, and no standard methodology has emerged for spatial data analysis. Proposed pipelines are often tailored to individual studies, leading to a fragmented landscape of available methods, and no clear guidance about which statistical tools are best suited to a particular research question. In response to these challenges, we present MuSpAn, a Multiscale Spatial Analysis package designed to provide straightforward access to well-established and cutting-edge mathematical tools for analysing spatial data. MuSpAn provides easy to use, flexible, and interactive access to quantitative methods developed from mathematical fields that include spatial statistics, topological data analysis, network theory, geometry, probability and ecology. Users can construct custom pipelines from tools across these fields to address specific biological problems, or conduct unbiased exploration of data for discovery spatial biology. In summary, MuSpAn is an extensive platform which enables multiscale analysis of spatial data, ranging from the subcellular to the tissue-scale.

Spatiotemporal dynamics of land-sea-scape carbon storage and natural-human coupled driving processes in Bohai Bay between 1979 and 2020

Article

Full-text available

Dec 2024
GEO-MAR LETT

Dramatic land use/land cover (LULC) change in the coastal zone driven by human and natural factors increases the urgency of studying the land-sea-scape pattern’s impacts on land and sea. Based on the LULC data of the Bohai Bay in 1979, 1989, 1999, 2008, and 2020, this study analyzed the characteristics of the spatial and temporal evolution of carbon storage in combination with the InVEST model, and revealed the impacts of change trajectories in LULC on carbon storage over five periods. The results of the study showed that the carbon storage in the Bohai Bay has decreased by 15.64% in the last 40 years, with a cumulative carbon loss of 0.91 × 10⁷ Mg. A total of 213 driving process trajectories were identified through the land use trajectories, with 23 major driving processes (94.25% of all processes). The number of Mono + , Mono-, Multiple + , and Multiple- were 4, 5, 95, and 100, respectively. Among them, Mono- has the largest area, driving the reduction of carbon storage in the study area by 40.63 × 10⁵ Mg from 1979 to 2020. The number of Natural + , Natural-, Human + , Human-, Couple + , and Couple- were 4, 5, 64, 61, 39, and 31, respectively. Of these, Human- had the largest area, driving a 59.67 × 10⁵ Mg reduction in carbon storage in the study area during 1979–2020. This study will provide coastal zone management and decision-making with a reliable reference, and facilitate the exploration of the optimal way of land use structure and enhance ecosystem services.

A PNDR e o Desenvolvimento Econômico do Nordeste: Uma Análise do FDNE e dos Incentivos Fiscais da Sudene

Preprint

Full-text available

Dec 2024

Este estudo avaliou a relação entre dois instrumentos da Política Nacional de Desenvolvimento Regional (PNDR)-os incentivos fiscais e o FDNE-e o desenvolvimento econômico dos municípios da área de atuação da Sudene. Para isso, foi desenvolvido o Indicador de Desenvolvimento Municipal (IDM), composto por três componentes: educação, saúde e renda. A análise foi dividida em duas etapas: primeiro, uma análise exploratória espacial identificou padrões na distribuição do IDM, dos pleitos de incentivos e do FDNE. Em seguida, utilizou-se o modelo econométrico espacial SAC para avaliar o impacto dos instrumentos no desenvolvimento municipal. Os resultados indicaram uma concentração de desenvolvimento econômico nas capitais litorâneas e focos no interior do Ceará, norte de Minas Gerais e Espírito Santo. Somente os incentivos fiscais tiveram impacto significativo no IDM, especialmente por meio da redução da mortalidade infantil e aumento do PIB per capita. O FDNE apresentou uma relação positiva apenas com o subíndice de renda. Transbordamentos espaciais foram observados para os incentivos fiscais, beneficiando municípios vizinhos. O estudo contribui para o aprimoramento das políticas da PNDR, sugerindo ajustes para maximizar o uso de recursos públicos e promovendo uma nova perspectiva sobre a dependência espacial no desenvolvimento regional, especialmente no Nordeste brasileiro.

Identification of natural and anthropogenic factors in the spatial pattern of Pb in the surface soil of the Nanliu River Basin

Article

Dec 2024
ECOTOX ENVIRON SAFE

O QUE IMPULSIONA O DESENVOLVIMENTO DO SETOR BANCÁRIO REGIONAL BRASILEIRO?: UMA PERSPECTIVA PÓS-KEYNESIANA

Article

Full-text available

Jun 2024

Após a criação do Plano Real, a consolidação do setor financeiro nacional contribuiu para o crescimento da oferta de crédito e o desenvolvimento econômico. Pela teoria pós-keynesiana, além das análises em nível nacional, é necessário também avaliar o processo de consolidação sob a ótica regional, pois a composição espacial pode afetar a determinação do emprego e renda em nível macroeconômico. Este artigo busca avaliar os principais fatores que afetaram o desenvolvimento regional do setor bancário no Brasil, no período 2006-2017. Para atingir tais objetivos, o trabalho utiliza, como estratégia metodológica, o cálculo do Índice de Herfindahl-Hirschman (HHI), a análise exploratória de dados espaciais a partir da estatística I de Moran e Indicador Local de Associação Espacial (LISA), e a estimação dos modelos espaciais autorregressivos, espacial Durbin, autocorrelação espacial e erro espacial, na forma de efeito fixo e aleatório. Os resultados indicam que o desenvolvimento do setor bancário no Brasil está condicionado à preferência pela liquidez do público e dos bancos, à densidade de empresas de pequeno porte e intensivas em capital, à densidade de crédito ofertado e ao emprego.

Earth Observation Data to Support Environmental Justice: Linking Non‐Permitted Poultry Operations to Social Vulnerability Indices

Article

Full-text available

Dec 2024

Concentrated Animal Feeding Operations (CAFOs) apply massive amounts of untreated waste to nearby farmlands, with severe environmental health impacts of swine CAFOs and proximity to disadvantaged communities well documented in some US regions. Most studies documenting the impacts of CAFOs rely almost exclusively on CAFO locations known from incomplete public records. Poultry CAFOs generate dry waste and operate without federal permits; thus, their environmental justice (EJ) impacts are undocumented. North Carolina (NC), a leading poultry producer, has seen a significant increase in poultry CAFOs, particularly since the 1997 swine CAFO moratorium. Using literature‐derived heuristics, this study refined the locations of poultry CAFOs derived based on Earth Observation (EO) data and deep learning, reducing the overestimation of poultry CAFO density by 54% after heuristic adjustments. We removed 51.8% of misclassified features in NC and 61.5% across the US, significantly improving data set accuracy. Spatial analysis, including Local Indicators of Spatial Association, revealed that poultry CAFOs often cluster in census tracts with high Social Vulnerability Index (SVI) scores, indicating potential EJ issues. Notably, one‐third of NC's census tracts with high poultry CAFO density also have high SVI, primarily in rural eastern regions. Similar patterns were observed in the South and Southeast of the US. However, not all high‐density CAFO areas correspond with high SVI, suggesting a complex relationship between CAFO locations and community vulnerabilities. This study highlights the critical need for comprehensive, high‐quality data on unpermitted poultry CAFOs derived using AI algorithms to fully understand their impacts on communities and accurately inform EJ evaluations.

ANÁLISE EXPLORATÓRIA ESPACIAL DA EXISTÊNCIA DE EQUIPAMENTOS PARA DIAGNÓSTICO DE CÂNCER NAS MICRORREGIÕES BRASILEIRAS PARA O ANO DE 2019.

Article

Full-text available

Dec 2024

O objetivo deste estudo é analisar a distribuição espacial dos equipamentos de diagnóstico de câncer nas microrregiões brasileiras para o ano de 2019. Para isso, foram utilizados dados do Cadastro Nacional de Estabelecimentos de Saúde (CNES), disponibilizados pelo Departamento de Informática do Sistema Único de Saúde (DATASUS). A metodologia utilizada neste trabalho foi uma Análise Exploratória de Dados Espaciais (AEDE), que permite verificar as associações espaciais existentes nas microrregiões brasileiras que possuem equipamentos de diagnóstico de câncer. Os resultados do estudo apresentam presença de autocorrelação espacial positiva nos dados. Notou-se a uma formação de clusters do tipo alto-alto de aparelhos de diagnóstico de câncer no Sul e Sudeste do Brasil, indicando que estas regiões possuem uma alta concentração de aparelhos. Entretanto, os resultados também indicaram que nas regiões Norte e Nordeste há uma forte presença de clusters do tipo baixo-baixo, indicando uma aglomeração de microrregiões com falta destes equipamentos médicos.

Large-scale georeferenced neuroimaging and psychometry data link the urban environmental exposome with brain health

Article

Dec 2024

Immigrant community integration in world cities

Preprint

Nov 2016

As a consequence of the accelerated globalization process, today major cities all over the world are characterized by an increasing multiculturalism. The integration of immigrant communities may be affected by social polarization and spatial segregation. How are these dynamics evolving over time? To what extent the different policies launched to tackle these problems are working? These are critical questions traditionally addressed by studies based on surveys and census data. Such sources are safe to avoid spurious biases, but the data collection becomes an intensive and rather expensive work. Here, we conduct a comprehensive study on immigrant integration in 53 world cities by introducing an innovative approach: an analysis of the spatio-temporal communication patterns of immigrant and local communities based on language detection in Twitter and on novel metrics of spatial integration. We quantify the "Power of Integration" of cities --their capacity to spatially integrate diverse cultures-- and characterize the relations between different cultures when acting as hosts or immigrants.

نمذجة التوزيع الجغرافي لسكان مدينة كركوك باستخدام تقنيات التحليل الإحصائي المكاني فيGISModeling the geographic distribution of the population of Kirkuk city using spatial statistical analysis techniques in GIS

Article

Full-text available

Nov 2024

ا.م.د. طوفان سطام حسن

تحتل دراسة التنظيم المكاني للدراسات السكانية أهمية كبيرة في الدراسات الجغرافية من الناحية النظرية والتطبيقية، وقد أحدثت التقنيات الجغرافية ما يشبه الثورة في عمليات التحليل والمعالجة للبيانات المكانية. وخلقت مفاهيم جديدة أعادت النظر بالأساليب التقليدية السابقة, وقدمت نماذج وطرائق مختلفة قادرة على محاكاة الواقع وفق نماذج مكانية تجمع بين علوم الرياضيات والإحصاء الجغرافية والحاسبات، في عمليات المعالجة والتحليل المكاني لنمذجة التنظيم المكاني للسكان وتفسيرها اعتمادا على الطرائق الإحصائية المكانية,Spatial Statistics Tools)) في برنامج (ArcGIS10.5). وقد تم استخدام نموذج التجمعات والقيم المتطرفة للظواهر الجغرافية:Cluster and Outlier analysisونموذج الارتباط الذاتي المكاني (Spatial Autocorrelation) لتحليل وتفسير التباين المكاني لتوزيع السكان في مدينة كركوك في العراق، حيث تقدم هذه البرمجيات وسائل القياسات الإحصائية المكانية لتوصيف النماذج المكانية السكانية، كميا وتحديد العلاقات المكانية لنماذج التوزيع بالعوامل الجغرافية وهي خطوة مهمة لفهم السلوك المكاني للظاهرة الجغرافية ، ولعل اهم ما تقدمه التقنيات الإحصائية هو إمكانية التحقق من النتائج التي نتوصل إليها حيث تزويدنا بمؤشرات لتحديد احتمال صحة النتائج وفق مستوى معنوية ودرجة ثقة إحصائية معينة. تصل إلى (99% (في البحث الحالي.

Clusters de renda no estado do Rio Grande do SulINCOME CLUSTERS IN THE STATE OF RIO GRANDE DO SUL: UMA ANÁLISE ESPACIALA SPATIAL ANALYSIS

Article

Full-text available

Dec 2024

O objetivo do presente trabalho consiste em mensurar a existência de clusters de renda no Estado do Rio Grande do Sul. Para isso, foi conduzida uma pesquisa com os 497 municípios do Estado no período 2002-2019. Como método, optou-se pela Análise exploratória de dados espaciais (AEDE). Verificou-se, através do Índice de Moran Local, a existência de duas regiões com dependência espacial estatisticamente significantes para os dois períodos. Uma apresenta um padrão do tipo baixo-baixo (Q1), localizada entre as regiões Sudeste e Centro Oriental. A segunda região de concentração de clusters compreende municípios localizados entre as regiões Norte e Nordeste Rio-Grandense, apresentando um padrão do tipo alto-alto (Q4). Além disso, observa-se que ouve um deslocamento da distribuição espacial da renda entre as regiões do Estado no decorrer do período analisado.

Local indicator of spatial association-LISA

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