Local Indicators Of Spatial Autocorrelation Research Articles

Socio-economic factors affecting spatial inequalities in pregnancy-related ambulance attendances in Greater London

Exploring inequalities in ambulance and pre-hospital demand is important to improve service equity and reduce wider health inequalities. Maternity incidents amongst ambulance demand are a key area of focus because of the specialized care that is needed for patients, as well as the impact of wider determinants of health on pregnancy outcomes. Since there are spatial inequalities amongst pregnant patients who call for an ambulance, the aim of this study is to assess the underlying factors associated with pregnancy related ambulance complaints, to determine why maternity patients utilize the ambulance service. Local indicators of spatial autocorrelation were used to identify clusters of ambulance maternity demand within Greater London (UK). A negative binomial regression model was used to explore associations between socioeconomic, environmental, accessibility and demographic variables. Our results reveal that neighborhoods with low adult skills (i.e. qualifications/English language abilities) have a higher rate of demand. Moreover, our results imply that the demand for ambulance services may not be directly tied to health outcomes; rather, it might be more closely associated with patients' reasons for calling an ambulance, irrespective of the actual necessity. The benefits of identifying factors that drive demand in ambulance services are not just linked to improving equity, but also to reducing demand, ultimately relieving pressure on services if alternative options are identified or underlying causes addressed. Doing so can improve health inequalities by firstly, improving ambulance care equity by directly supporting a better allocation of resources within ambulance systems to target patterns in demand.

Analysis of the spatial distribution patterns of education infrastructure development (A case study of 33 regencies/city in North Sumatra Province)

This study explores the spatial distribution pattern of educational infrastructure development across districts and cities in North Sumatra, identifying significant disparities between urban and rural areas. The study aims to: (1) determine the distribution of educational development across districts and cities, (2) analyze global spatial autocorrelation, and (3) identify priority locations for educational development policies in North Sumatra Province. The methodology includes quantile analysis, Moran’s Global Index, and Local Indicators of Spatial Autocorrelation (LISA) using GeoDa software to address spatial autocorrelation. The results indicate that there are nine areas with a low School Participation Rate Index (SPRI), eleven areas with a low School Facilities and Infrastructure Index (SFII), and eleven areas with a low Regional Education Index (REI). Spatial autocorrelation analysis reveals that SFII shows positive spatial autocorrelation, while SPRI and REI exhibit negative spatial autocorrelation, indicating a high level of inequality between regions. Labuhan Batu Selatan and Labuhan Batu are identified as priorities for the provincial government in overseeing educational development policies.

Spatial inequality in sub-national human development index: A case study of West Bengal districts

Human Development and its regional disparity is an area of topical interest. In this paper, we consider space as an instrumental variable in understanding this disparity and explore spatial interdependence in human development across districts of West Bengal using alternate indicators of human development. This study uses both the standard United Nations Development Programme (UNDP) technique and an alternative methodology to construct a sub-national human development index. Spatial interdependence is measured using Moran's I coefficient and Local Indicators of Spatial Autocorrelation (LISA) cluster maps. Spatial regression is used to evaluate the spatial association between the Human Development Index as the dependent variable and a few independent variables. A high degree of clustering in all three dimensions of human development is observed among the southern districts of West Bengal. The pattern of cluster maps indicates the presence of both the growth pole process and the metropole-periphery process in the state. Spatial regression results indicate that the share of socially backward class population and urbanization rate are important variables affecting regional human development patterns. The findings of this study suggest policies targeted to promote social inclusion and urbanization linked with better access to economic opportunities in the lagging areas can help reduce regional imbalances.

Spatial Estimation of Regional Economic Growth Heterogeneity During 2014‒2021

Introduction. The aggravation of socio-economic heterogeneity among Russian regions is one of the main challenges for sustainable development of the country. The main task of spatial policy is to ensure advanced rates of development of geostrategic and lagging behind territories as well as increasing competitiveness of regional economies. However, the events of the past decade substantially adjusted plans on regional economic growth enhancement. The aim of the study is to estimate spatial economic growth heterogeneity and interdependence of bordering entities of Russia over 2013‒2021. Materials and Methods. The main research method is Moran’s methodology for assessing local and global indices characterizing the inter-regional relation and A. Anselin’s approach for local indices of spatial autocorrelation and spatial clusters identification. The research uses regional data on real GRP per capita growth rates for 85 Russian regions for five annual intervals. Results. The results confirmed that geopolitical tensions significantly enhance the regional economic growth heterogeneity. The most developed regions, including main hydrocarbon producers, are found being most exposed to external economic shocks, which reduced their spatial interrelation. Southern and eastern regions demonstrated an upward growth trend. The COVID-19 pandemic shaped the appearance of western and eastern clusters. Regional economies demonstrated the decreasing heterogeneity through enhancing the economic growth rates in the post-crises period. Coincidently, we document that economic development of the regions influences the growth in bordering entities. Discussion and Conclusion. The research allows defining three stages of regional economic growth rates heterogeneity during 2013‒2021. The derived conclusions are recommended for actualizing state policy in reducing regional heterogeneity and strengthening the national economic space.

Autocorrelation Spatial Based on Specific Nutritional Interventions Achievement with Stunting Cases in Toddlers at Kendari City Using Local Indicator of Spatial Autocorrelation (LISA) Method

Background:Stunting is a priority target both globally and in Indonesia. There are 10 provinces in Indonesia that are the main focus of the stunting reduction program, one of which is Southeast Sulawesi Province. Kendari City, located in Southeast Sulawesi, has experienced an increase in stunting incidence over the past three years. However, progress in reducing stunting in Kendari City has not been evenly distributed across its regions and sub-regions, with significant disparities in stunting rates between different sub-districts. Objective:To determine the spatial autocorrelation based on the achievement of specific nutritional interventions for toddlers and the incidence of stunting in Kendari City using the Local Indicator of Spatial Autocorrelation (LISA). Method:This quantitative study used the Local Indicator of Spatial Autocorrelation (LISA) method. The data on stunting incidence consisted of the number of stunting cases among toddlers in 2023 across 11 sub-districts in Kendari City. The sub-districts analyzed were Abeli, Baruga, Kadia, Kambu, Kendari, West Kendari, Mandonga, Nambo, Poasia, Puuwatu, and Wua-Wua. The study was conducted from November 2023 to May 2024 in Kendari City. A local autocorrelation test with LISA was performed to determine the spatial relationships among the sub-districts based on the research variables, with results displayed in the form of Moran's scatterplot, cluster map, and significance map. Results:The results of Moran's local bivariate test (LISA) indicated that the majority of sub-districts, particularly Kambu, exhibited significant positive autocorrelation with neighboring sub-districts and fell into the cold-spot category. This indicates that the number of specific nutritional intervention programs for toddlers and the cases of stunting in toddlers in 2023 were low in Kambu and its surrounding sub-districts, which also had similarly low values. Conclusion:There is spatial autocorrelation among the sub-districts in Kendari City. Although the cases of stunting in the Kambu sub-district are low, the achievement of intervention programs should remain optimal, as cases still exist in the area. Additionally, since Kambu has a spatial correlation with its neighboring areas, the government should target these areas for appropriate interventions to accelerate stunting reduction, particularly in Kendari City. Keywords:Spatial Autocorrelation; LISA; Specific Nutrition Interventions; Stunting Toddlers

Mining-induced forest cover change of Paschim Bardhaman, a mining-based district of India

Mining activities are a recognized factor for Forest Cover Loss (FCL) worldwide. Huge forest cover areas are lost due to mining activities worldwide and in India. This study is conducted to identify the villages that experienced more FCL as a result of mining activities and also focuses on identifying the role of individual coal mines on FCL. Results indicate that the mining area increased to 70.79 km2 in 2020 from 25.56 km2 in 1990, and the vegetation area reduced to 149.22 km2 from 271 km2 at the same time. Mostly Jamuria, Barabani, Raniganj, and Pandabeswar blocks have lost large amounts of forest cover due to mining activities. Results also indicate that mining areas have increased nearly threefold and influenced the rate of FCL in the district. Village-level analysis of mining-induced FCL identified that more than ten villages had lost more than 10% of the total forest cover areas due to coal mine expansion resulting in environmental degradation. Analysis of spatial matrices indicates a fragmentation nature of vegetation cover areas of the selected coal mines and indicates that available vegetation areas are concentrated in some pocket areas. Local Indicators of Spatial Autocorrelation (LISA) based spatial patterns of mining-induced FCL show high cluster location in and around major coal mines of the area proving the role of open-cast coal mines on FCL and forest fragmentation. The analysis results may help the planners maintain the healthy environment of the affected villages by formulating alternative ways of forest cover increase.

Geospatial insights into open defecation vulnerability in Gulumbe, Nigeria

Open defecation (OD) poses a serious public health risk by exposing communities to diseases and compromising the well-being of people. One potential approach to mitigating its impact is the application of spatial planning techniques. However, spatial data paucity is one of the major problems in addressing open defecation issues in Nigeria. This study employed spatial analysis to identify areas particularly susceptible to OD using Geographical Information System (GIS) including Merginmap applications on Android and QGIS software, which were used with field observations to georeferenced residential areas, public toilets, and instances of open defecation. Proximity analysis and local indicators of spatial autocorrelation, based on Moran’s I, were employed to assess vulnerability. Buffer analysis was conducted to identify residential areas within 100 m of an OD site, and the findings indicated residential areas at high risk of experiencing negative impacts from OD. The LISA analysis revealed a high-to-high clustering pattern (p < 0.05) in the southern region of the study area, which aligns with the results of the buffer analysis and further suggests consistent spatial patterning. Furthermore, OD was more prevalent near water bodies and inner residential areas, increasing the risk of water and air pollution. This pollution could facilitate the spread of harmful bacteria, such as E. coli and Salmonella, from OD sites to nearby households, increasing the risk of disease. This study stresses the potential of GIS in evaluating areas vulnerable to OD and highlights the need to expand awareness campaigns regarding proper hygiene and sanitation practices in rural communities.

An ecological study mapping socioeconomic inequalities in tuberculosis incidence in a southern state of Brazil

ObjectiveTo analyze the spatial patterns and factors associated with tuberculosis incidence in the municipalities of Paraná, Brazil. Materials and methodsEcological study examining new tuberculosis cases from 2018 to 2022 in Paraná’s 399 municipalities. Incidence coefficients, relative risk, and local indicator of spatial autocorrelation were estimated. Negative binomial models were applied to identify associated factors. ResultsHigh-risk areas were observed in the coastal/port, north, and northeast regions. The following factors positively influenced tuberculosis incidence: municipal development index (incidence rate ratio [IRR]: 1.07; 95 % confidence interval [95 % CI]: 1.01–1.14), hospitalizations due to inadequate environmental sanitation (IRR: 1.07; 95 % CI: 1.01–1.14), and Gini index (IRR: 1.09; 95 % CI: 1.02–1.16). ConclusionsParadoxically, in municipalities with elevated development indices yet marked by socioeconomic disparities—including deficiencies in sanitation—substantial tuberculosis clusters persist. This suggests that income inequality might play a role in perpetuating the incidence even in regions that are otherwise considered developed.

Assessing Africa's child survival gains and prospects for attaining SDG target on child mortality.

This study assessed Africa's child survival gains and prospects for attaining Sustainable Development Goals (SDG) target 3.2. We analysed multiple country-level secondary datasets of 54 African countries and presented spatial analysis. Results showed that only 8 out of the 54 African countries have achieved substantial reductions in under-5 mortality with an under-five mortality rate (U5MR) of 25 deaths per 1,000 live births or less. Many countries are far from achieving this target. Results of the predictions using supervised machine learning on the Bayesian network reveal that the probability of achieving the SDG target 3.2 (i.e., having U5MR of 25 deaths per 1000 live births or less) increases (from 21.6% to 100%) when the contraceptive prevalence increases from 49.8% to 78.5%; and the use of skilled birth attendants increases from 44.8% to 86.3%; and percentage of secondary school completion of female increases from 42.5 to 74.0%. Our results from Local indicator of spatial autocorrelation (LISA) cluster maps show that 7 countries (mainly in West/Central Africa) formed the high-high clusters (hotspots for U5M) and may not achieve the SDG target 3.2 unless urgent and appropriate investments are deployed. As 2030 approaches, there is a need to address the problem of limited access to quality health care, female illiteracy, limited access to safe water, and poor access to quality family planning services, particularly across many sub-Saharan African countries.

Analisis Spasial Persebaran Jumlah Kasus Malaria di Kalimantan Timur Menggunakan Indeks Moran dan Local Indicator Spatial of Autocorrelation

Spatial analysis is an analysis that considers the location and distance of an object in the research data. Moran’s index is one of the spatial methods used to analyze spatial autocorrelation globally. Furthermore, there is the Local Indicator of Spatial Autocorrelation (LISA) method which is used to analyze spatial autocorrelation locally. This study aims to determine whether there is spatial autocorrelation and determine the distribution pattern formed in the data on the average number of malaria cases in East Kalimantan based on the regency/city during 2018-2022. The results showed that based on the Moran index globally, there was no spatial autocorrelation in the average number of malaria cases in East Kalimantan in 2018-2022. The type of spatial pattern in the distribution of malaria cases in East Kalimantan is a clustering pattern indicated by the clustering of malaria cases in each district/city in East Kalimantan. Furthermore, the results of spatial autocorrelation using LISA show that locally there is spatial autocorrelation in several districts/cities in East Kalimantan, namely Paser, Kutai Timur, Kutai Barat and Penajam Paser Utara.

Geographic clustering of cannabis stores in Canadian cities: A spatial analysis of the legal cannabis market 4 years post-legalisation.

Following the legalisation of non-medical cannabis in 2018, the number of cannabis stores in Canada has rapidly expanded with limited regulation on their geographic placement. This study characterised the clustering of cannabis stores in Canadian cities and evaluated the association of clustering with provincial policy and sociodemographic variables. Cross-sectional spatial analysis of cannabis store density in dissemination areas ('neighbourhoods', n = 39,226) in Canadian cities in September 2022. Cannabis store density was defined as the count of stores within 1000 m of a neighbourhood centre. Clusters of high-density cannabis retail were identified using Local Indicators of Spatial Autocorrelation. Associations between provincial policy (privatised vs. public market), sociodemographic variables and cannabis store density were evaluated using multivariable regression. Clusters of high-density cannabis retail were identified in 86% of Canadian cities, and neighbourhoods in clusters had a median of 5 stores within 1000 m. Toronto, Canada's most populous city, had the most extreme clustering where neighbourhoods in clusters had a median of 10 stores (and a maximum of 25 stores) within 1000 m. Neighbourhoods in private versus public retail markets had a significantly higher neighbourhood-level density of cannabis stores (adjusted rate ratio [aRR] 63.37, 95% confidence interval [CI] 25.66-156.33). Lower neighbourhood income quintile was also associated with a higher neighbourhood-level density of cannabis stores (Q5 vs. Q1, aRR 1.28, 95% CI 1.17-1.40). Since cannabis was legalised, clusters of high-density cannabis retail have emerged in most Canadian cities and were more likely to form lower income neighbourhoods and in private retail markets.

Abstract PO3-10-03: Nationwide Impact of Neighborhood Poverty Hotspots on Breast Cancer Mortality

Abstract Title: Nationwide Impact of Neighborhood Poverty Hotspots on Breast Cancer Mortality Background: Poverty is known to lead to the worst breast cancer (BC) outcomes, at the individual and county level. However, we believe that the relationship between poverty and breast cancer mortality is more complex and can be influenced by the economic status and available resources of the surrounding neighborhoods. Our goal is to understand to what extent poverty in the surrounding neighborhood may contribute to breast cancer mortality on a national scale and the county areas at the highest risk. Methods: This study examines the association between neighborhood effects of poverty on overall and BC mortality among women diagnosed between 2005 and 2019 from NCI SEER. We also obtained county-level five-year poverty estimates from the American Community Survey. Counties with poverty estimates above or below the national mean were high-poverty (H) or low-poverty (L) counties, respectively. We then used local indicators of spatial autocorrelation (LISA) analysis with Local Moran’s I of the poverty estimates and their H- and L-county categorization to define four neighborhood poverty environments for US counties. Specifically, H-counties among other H-counties were classified as poverty hotspots (HH) counties, L-counties among other L-counties were classified as poverty cold spots (LL), H-counties among LL-counties were HL-counties, and L-counties among HH-counties were LH-counties. Non-Hispanic White (NHW), Non-Hispanic Black, and Hispanic women at least 20 years of age diagnosed with local, regional, or distal stage BC between 2005-2009 and 2010-2014 that survived more than one year after diagnosis were aggregated by county of residence at diagnosis to determine their neighborhood poverty environment at BC diagnosis. Women included in the study had a Local Moran's I p-value less than or equal to 0.3 for their county of residence. A Poisson regression model that adjusts for age at diagnosis, race/ethnicity (NHW vs. not-NHW), BC stage, year of diagnosis (2005-2009 vs. 2010-2014), both local and systemic treatment except for hormone therapy, and Estrogen Receptor (ER) status (negative vs. positive) was used to assess the effects of county-level poverty cluster types on BC mortality. Results: Our analysis includes 24,070 women diagnosed with BC, of which 6,919 women died, and 2,262 of these deaths were specific to BC. In total, 4,528 women were in HH counties from CA, GA, KY, LA, NM, UT, and HI; 14,112 women were in LL counties; 4,338 women were in HL counties; 1,092 women were in LH counties. Women in HH, LH, and HL counties had a 1.28 (95%CI:1.15,1.42), 1.23 [95%CI:1.01-1.48], and 1.05 [CI: 0.93,1.17] relative risk of cause-specific BC death respectively when compared to deaths in LL counties after adjustment for year of diagnosis, age at diagnosis, race/ethnicity, stage at diagnosis, treatment, and estrogen receptor status. When stratified by ER status or BC stage at diagnosis, the relative risk of BC mortality for women in HH counties diagnosed with ER-positive, local or regional stage had a 1.35 (95% CI: 1.19,1.52), 1.32 (95%CI: 1.13,1.54), and a 1.21 (95%CI:1.02,1.43) when compared to women in LL counties, respectively. Our analysis also showed that total death trends were similar to the trends observed in cause-specific deaths. In addition, we observed similar total and cause-specific BC death trends when using a p-value of 0.05 for the LISA analysis. Conclusion: We use LISA spatial clustering to demonstrate the impact national poverty neighborhood environments have on BC mortality, thus identifying counties of worst BC outcomes through neighborhood poverty environments. Our findings suggest that prioritization of resource allocations to HH counties would significantly improve BC outcomes. Citation Format: Ifeoma Nwigwe, Michael Desjardins, Kala Visvanathan. Nationwide Impact of Neighborhood Poverty Hotspots on Breast Cancer Mortality [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-10-03.

Neighborhood Context as a Barrier to Sport Participation for Girls: Evidence From a Sport Diversity Index.

Evidence suggests neighborhood contexts play a vital role in shaping the availability and diversity of youth sport and participation rates, especially for African American or Black girls. Currently, no index captures interscholastic sport opportunities (eg, sport diversity) within and across school districts and specifically applied to African American or Black girls. To visualize the inequalities present in interscholastic sport opportunities for girls across school districts using a novel index in a selected study area of St Louis City and County, Missouri, and discuss the implications for African American or Black girls. Cross-sectional study. Database secondary analysis. Data for 47 public high schools in the 23 St Louis City and County school districts. We gathered data from the 2014-2018 American Community Survey and Missouri State High School Activities Association. We assessed sport diversity for girls by constructing a sport diversity index (SDI) that uses an entropy index as its foundation. Census-tract data were used to examine the association with neighborhood demographics and contributors to school district income and sport diversity. Descriptive spatial statistics were calculated to evaluate distributions in St Louis City and County, with the bivariate local indicator of spatial autocorrelation used to determine any correlations between variables of interest. The St Louis City school district, which has areas with high rates of renter-occupied housing and poverty and high percentages of non-Hispanic African American or Black students, had the lowest SDI for girls, contrasted with the school districts in St Louis County, which showed an inverse pattern on average. The SDI for girls was correlated with the percentages of renter-occupied housing and poverty. The SDI for girls was also correlated with race: an increasing presence of the non-Hispanic African American or Black population was associated with decreased sport diversity for girls. The SDI for girls demonstrated a spatial association with neighborhood-level determinants of sport-opportunity availability for non-Hispanic African American or Black girls in St Louis. The role of social and political determinants of health in shaping community context and resultant health in athletic training research, policy, and practice should be considered.

Matching supply and demand of cooling service provided by urban green and blue space

Urban blue-green space (UBGS) has been extensively investigated for its climate regulation capability in the context of climate change. However, current efforts are limited toward the cooling effects mismatch in supply and demand of UBGS. This study adapts the framework of ecosystem supply and demand to quantify the supply-demand matching of the cooling effects of UBGS. Two indices were proposed to evaluate cooling provision and demand of UBGS: Cooling Supply Index (CSI), accounting for cooling contribution and spatial effect, and Cooling Demand Index (CDI), considering physical and social dimensions of cooling demand. Four-quadrant analysis and bivariate local indicators of spatial autocorrelation (BiLISA) analysis were employed to distinguish the supply-demand matching status: high supply with high demand, low supply with high demand, low supply with low demand, and high supply with low demand. The results show that 1) CSI and CDI yield great performance in quantifying cooling supply and demand, respectively; 2) the four-quadrant analysis and bivariate LISA analysis are complementary to each other, and can target those areas in different matching statuses for climate-sensitive planning and design; and 3) particular attention should be given to areas characterized by low supply with high demand. This study marks a crucial step toward integrating social aspects into the analysis of cooling effects provided by UBGS. It enriches the analysis framework for urban ecosystem services and provides valuable guidance for landscape planning to mitigate heat stress.

A novel framework for assessment of human impact of floods: Demonstrated for the Indian subcontinent

Flood damages are a result of mutual interaction between flood hazard and socio-economic factors. The increase in flood damages over time can be attributed to various enhancements in the magnitude and frequency of floods as well as socio-economic development in the floodplains. The quantification and analysis of the human dimensions of flood damages is challenged due to irreversibility and lack of understanding of complex-human flood interactions, especially in less developed regions. Since flood damage assessments play a critical role in designing effective flood preparedness plans, we suggest including the human dimension in evaluating damages. We present a framework for the assessment of the human impact of floods by incorporating statistics on the number of people exposed, displaced, and fatalities recorded for each flood event. The framework involves categorizing human impact, identifying spatial clusters of different categories, and exploring the role of various hazard and socio-economic characteristics in determining impact. While categorizing and clustering of impact helps to distinguish between regions prone to different impact, understanding the role of socio-economic and demographic factors is expected to help in developing tailor-made strategies to mitigate flood disasters. The methodology broadly consists of categorization using unsupervised learning, spatial clustering using Local Indicators of Spatial Autocorrelation (LISA), and spatial regression. The results indicate that the impact varies from one region to another and so does the role of hazard and socio-demographic variables in determining impact. Moreover, the framework developed may be used to study the relevance of various socio-hydrological theories and their role in determining the human impact of floods.

Revealing novel insights on economic structure from a spatial perspective: Empirical findings from Vietnam

Economic structure plays an essential role in distributing resources and shaping the development trend of a country. Although it has become a topic of interest for scholars, most studies focus on analyzing factors affecting the structural transformation process but ignore the correlation in economic structure between localities. This study explores this correlation through the case of a country undergoing a remarkable economic restructuring process-Vietnam. Based on the data from 2010 to 2019, the Moran?s index (I) is used to assess the level of spatial correlation in the economic structure of localities and the Local Indicator of Spatial Autocorrelation (LISA) is analyzed to determine the specific locations where local spatial correlation occurs. Research results show that the economic structure of localities is unevenly distributed across geographical space. In addition, there exists a spatial autocorrelation phenomenon in localities' economic structure for two sectors-agriculture and industry. At the same time, there is no evidence to show this for the service sector. This discovery confirms the necessity of incorporating spatial factors in research related to economic structure to avoid inaccurate conclusions. From a business perspective, based on the findings of this research, companies can assess the level of competition, risks, as well as business partnership opportunities in different areas, and make appropriate investment decisions. The research results might also serve government agencies regarding planning and functional zoning and formulating and implementing development and economic restructuring policies for various regions.

A spatiotemporal analysis of bovine brucellosis cases in Minas Gerais state, Brazil, from 2011 to 2018

Our study explored the patterns of bovine brucellosis dissemination in Minas Gerais state, Brazil, by examining data on passive surveillance of bovine brucellosis cases from the Instituto Mineiro de Agropecuaria (IMA) (Animal Health Authority), as well as cattle population and bovine brucellosis testing, from 2011 to 2018 by means of a spatiotemporal analysis. We plotted cases, populations and testing distributions and performed spatial autocorrelation (Moran’s I test) and local indicators of spatial autocorrelation (LISA) analyses. Moreover, we assessed the correlation of the spatial distribution and the compiled data (brucellosis cases, cattle populations, and brucellosis testing) by Lee’s test. Our results showed that bovine brucellosis cases occurred mainly in the Triângulo Mineiro, Alto Paranaíba and Northwest regions, which reported cases in all analyzed years (2011 to 2018). The cattle population of Minas Gerais was concentrated in the same regions as bovine brucellosis cases, and the performed tests through the analyzed years (2011 to 2018). Moran’s I test results of the case data showed significant spatial autocorrelation in 2011, 2015 and 2018 (p value < 0.05), and from 2011 to 2018, the population and testing data were also significant in Moran’s I test (p value < 0.01). The results of cluster analysis (LISA) of cases showed clusters mainly in the Triângulo Mineiro, Alto Paranaíba, Northwest and South regions in 2011, 2015 and 2018. The local clusters for cattle populations and brucellosis testing were also observed in the same regions as bovine brucellosis cases in all years (2011 to 2018). The correlation results between clusters (Lee’s test) were 0.22 (p value < 0.01) in 2011, 0.15 (p value < 0.01) in 2015 and 0.43 (p value <0.01) in 2018 between cases and populations, and 0.25 (p value <0.01) in 2011, 0.14 (p value <0.01) in 2015 and 0.38 (p value < 0.01) in 2018 for testing and cases. Therefore, our results showed that brucellosis cases were distributed together with cattle populations and brucellosis testing data, indicating that brucellosis in cattle in Minas Gerais state is being identified where there are more animals and where more tests are performed.

Spatial patterns on tourism establishments in five CIP´s in Mexico, 2010–2022

AbstractStudies on the spatial patterns of economic activity contribute to economic performance management, planning, and promotion. Investors, policymakers, and social organizations have access to valuable information on the concentration and location of activity. Spatial patterns of tourism have been studied to better organize tourist flows, optimize existing infrastructure, plan new facilities, and promote new destinations. In the late 1960s, the Central Mexican government started projects aimed at constructing large tourism facilities primarily for foreign visitors. These were called centros integralmente planeados or “fully planned centers” (CIP’s) and were headed by Fondo Nacional de Fomento al Turismo (National Tourism Promotion Fund) FONATUR. The objective of this study is to explore the spatial distribution patterns of tourism establishments in five municipalities where CIP?s are located. The study uses exploratory spatial data analysis (ESDA) tools, such as average nearest neighbor index, standard deviation ellipse, spatial kernel density, global and local indicator of spatial autocorrelation, applied to geographic information system (GIS) data representing tourism establishments. The results show changes in establishment distribution and orientation, as well as spatial concentration in all cases and years. All CIP?s experienced a significant reduction in establishments, particularly small and medium-sized businesses. This paper is one of the first to analyze simultaneously the spatial distribution and concentration of tourism establishments in five municipalities where CIP’s are located.

The Spatial Pattern of the Spread of the COVID-19 Pandemic (Case Study: DKI Jakarta Province)

The COVID-19 pandemic has been running in Indonesia for more than two years. The first case was found in March 2020. DKI Jakarta as the capital city of the country with a high population density and an economic center that was threatened because the area has a high vulnerability to the spread of COVID-19. The number of confirmed cases that continue to soar and the spread that is difficult to be controlled have resulted in the DKI Jakarta government taking policies such as implementing large-scale social restrictions (PSBB), which aims to stop the spread of COVID-19 and to look for patterns of spread of COVID-19. This study uses a geographic information system in looking for patterns of the spread of COVID-19. The analytical method used is spatial autocorrelation, which is carried out using the Moran Index. In addition, the autocorrelation test was also carried out using a Local Indicator of Spatial Autocorrelation (LISA) with the results in the form of a cluster map and a map of significance. The Ordinary Least Squares analysis method is a regression technique that provides a global model for understanding and predicting variables in research. The correlation variables used in this research are Markets, Supermarkets, Buses, and Stations. The result of this study is the spatial autocorrelation of the pattern of spread of COVID-19 between villages and spatially the distribution pattern is clustered. In the OLS regression distribution pattern, the supermarket variable with an R-Squared value of 0.128555 or 12% affects the spread of COVID-19. Based on the calculation of R-Square, Koenker (BP) in addition to the OLS model, the assumption of homoscedasticity is not met, so the model is Ordinary Least Squares not good compared to other models in analyzing the pattern of the spread of COVID-19 in DKI Jakarta.

Integrating equity, climate risks, and population growth for targeting conservation planning

Where landowners, non-profit organizations, and government agencies prioritize conservation activities has significant implications for people, ecosystems, and climate resilience. Our study builds on conservation decision-making scholarship by analyzing the relationships between biodiversity priorities, social vulnerability, climate risks, and projected population growth in Texas to identify geographies that simultaneously support multiple goals. Drawing from publicly available datasets, we show the potential for existing conservation priorities to exacerbate the inequitable distribution of environmental goods and services, especially for lower-income residents, communities of color, and socially vulnerable populations. Using bivariate local indicators of spatial autocorrelation, we demonstrate effective ways to avoid negative social impacts by identifying synergistic locations with high levels of social vulnerability and biodiverse landscapes. We overlay these locations with climate risks to further prioritize areas that could meet biodiversity, social vulnerability, and climate adaptation needs. Lastly, we consider how future population growth may inform the urgency of conservation activities given potential development pressures. Our study contributes to academic and policy debates seeking to jointly address biodiversity conservation, climate change, and environmental justice concerns.