Local Indicators Of Spatial Autocorrelation Research Articles

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.

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.

Spatial Inequity of Transit and Automobile Access Gap across America for Underserved Population

This study examines the correlates of the Modal Access Gap (MAG) between transit and automobile to employment opportunities in the 45 most populated American metropolitan areas by testing spatial lag regression models and employing the bivariate local indicator of spatial autocorrelation (BiLISA) at the census block group geographical level. Four findings are discerned. First, MAG is positive regardless of the metropolitan area and travel-time threshold and ranges from 0.22 to 0.98. This indicates transit trails automobile in offering access to employment opportunities. Second, millennials and carless households tend to reside in areas with a narrower MAG, while people with disabilities reside in areas with a wider MAG. Third, areas with a high share of carless households and relatively low transit access to employment opportunities are primarily clustered in the suburbs and exurbs of American metropolitan areas. Fourth, the MAG disproportionately affects socially vulnerable populations, including the elderly, people with disabilities, low-income households, Hispanics, and African Americans. Compared with the national average, modal access inequity is prevalent for the elderly in 27, for people with disabilities in 22, for low-income households in 17, for Hispanics in 14, and for African Americans in 12 metropolitan areas. This research is a necessary step forward for instilling social equity into transport planning strategies in parallel with governmental efforts.

Malaria in Kenya during 2020: malaria indicator survey and suitability mapping for understanding spatial variations in prevalence and risk

Abstract. Despite the availability of effective interventions malaria continues to be a major public health issue in Kenya, where young children and pregnant women are particularly vulnerable. In this study we examined the spatial distribution of malaria incidence and how this relates to the environmental conditions required for malaria in 2020. The Kenya Malaria Indicator Survey (N=11,549) for 2020 was used with the Local Indicators of Spatial Autocorrelation (LISA) method to determine spatial clusters of malaria and assess their significance as well as interventions in use. Climate data was used with a Fuzzy Overlay method to create malaria risk maps. The findings suggest that malaria incidence is not evenly distributed across Kenya, with some regions having higher rates of transmission and others having lower rates. High-rate clusters of malaria and high-risk areas of malaria transmission could benefit from increased vector control measures.

Heterogeneity Assessment of Kenaf Breeding Field through Spatial Dependence Analysis on Crop Growth Status Map Derived by Unmanned Aerial Vehicle.

The investigation of quantitative phenotypic traits resulting from the interaction between targeted genotypic traits and environmental factors is essential for breeding selection. Therefore, plot-wise controlled environmental factors must be invariable for accurate identification of phenotypes. However, the assumption of homogeneous variables within the open-field is not always accepted, and requires a spatial dependence analysis to determine whether site-specific environmental factors exist. In this study, spatial dependence within the kenaf breeding field was assessed in a geo-tagged height map derived from an unmanned aerial vehicle (UAV). Local indicators of spatial autocorrelation (LISA) were applied to the height map using Geoda software, and the LISA map was generated in order to recognize the existence of kenaf height status clusters. The spatial dependence of the breeding field used in this study appeared in a specific region. The cluster pattern was similar to the terrain elevation pattern of this field and highly correlated with drainage capacity. The cluster pattern could be utilized to design random blocks based on regions that have similar spatial dependence. We confirmed the potential of spatial dependence analysis on a crop growth status map, derived by UAV, for breeding strategy design with a tight budget.

Geospatial analysis of population-based incidence of multiple myeloma in the United States

BackgroundWe investigated the spatial patterns of multiple myeloma (MM) incidence in the United States (US) between 2013 and 2017 to improve understanding of potential environmental risk factors for MM. MethodsWe analyzed the average county-level age-adjusted incidence rates (“ASR”) of MM between 2013 and 2017 in 50 states and the District of Columbia using the U.S. Cancer Statistics Public Use Databases. We firstly divided the ASR into quintiles and described spatial patterns using a choropleth map. To identify global and local clusters of the ASR, we performed the Spatial Autocorrelation (Global Moran’s I) analysis and the Anselin’s Local Indicator of Spatial Autocorrelation (LISA) analysis. We compared the means of selected demographic and socioeconomic factors between the clusters and counties of the whole US using Welch one-sided t-test. ResultsWe identified distinct spatial dichotomy of the ASR across counties. High ASR were observed in counties in the Southeast of the US as well as the Capital District (metropolitan areas surrounding Albany) and New York City in the state of New York, while low ASR were observed in counties in the Southwest and West of the US. The ASR showed a significant positive spatial autocorrelation. We identified two major high-high local clusters of the ASR in Georgia and Southern Carolina and five major low-low local clusters of the ASR in Alabama, Arizona, New Hampshire, Ohio, Oregon, and Tennessee. The racial population distribution may partly explain the spatial distribution of MM incidence in the US. ConclusionFindings from this study showed distinct spatial distribution of MM in the US and two high-high and five low-low local clusters. The non-random distribution of MM suggests that environmental exposures in certain regions may be important for the risk of MM.

A new local indicator of spatial autocorrelation identifies clusters of high rendaku frequency in Japanese place names

AbstractThe methods of spatial statistics have been successfully applied to the study of linguistic variation, especially for detecting the existence of spatial patterns in the geographical distribution of linguistic features. However, the use of local indicators of spatial autocorrelation for detecting spatial clusters have been limited to continuous variables, and we propose to apply the new method of Anselin and Li (2019) for categorical variables to linguistic data. We illustrate this method with the case of Japanese rendaku, or sequential voicing, whose dialectal variation is still poorly documented. Focusing on regional differences in the frequency of rendaku, we examined the occurrence of rendaku for four lexemes in 4,921 place names from all Japan. A statistical analysis of local spatial association and an unsupervised density-based cluster analysis revealed the existence of two cluster areas of high rendaku frequency centered around Wakayama and Fukushima-Yamagata prefectures. This suggests that rendaku is more frequent in those dialects, and we recommend that further studies in the dialectal variation of rendaku start by looking at those areas.

SPATIO-TEMPORAL ANALYSIS AND MODELLING OF DENGUE INCIDENCES IN QUEZON CITY USING ORDINARY LEAST SQUARES AND SPATIAL REGRESSION

Abstract. This research examined monthly dengue incidences per barangay (village) in Quezon City, Philippines over a period of six years (2010–2015) to determine the relative significance of environmental variables on dengue prevalence. The data were subjected to correlation analysis, spatial autocorrelation assessment, multiple factor analysis, ordinary least squares (OLS) and spatial regression. Local Indicators of Spatial Autocorrelation (LISA) Moran’s I cluster maps indicate significant (p=0.05, 0.01) High-High clustering and Low-Low clustering in the northern and southern parts of the city, respectively. Monthly total cases indicated increasing trend staring from May/June, peaking at around August/September, and declining afterwards to lower levels in November/December. This corresponds to the typical temporal rainfall pattern. Dengue cases were found to be positively correlated (α=0.05) with Population (R=0.84), Informal_Settlements (IS) (R=0.716), Very_Low_Density_Residential (VLDR) (R=0.512), Open_Spaces (OS) (R=0.339), Mean_Rainfall (RFM) (R=0.637), and Mean Elevation (EM) (R=0.498). Dengue incidence (DI) was negatively correlated with Mean Air Temperature (ATM) (R=−0.3 to 0.5). Based on factor analysis, the dengue incidences were closely related to these variables, though factors F1 and F2 accounted for only 28% of the data variability. Ordinary Least Square (OLS) regression analysis of DI with general land use (LU) classes (e.g., no subclasses in residential areas) identified only IS and OS, explaining 43% of the variability of DI, with IS having twice as much influence on DI compared to OS. When residential subclasses are considered, VLDR was added to the model, slightly increasing R-squared to 0.452. Considering, in addition, EM, RFM, and ATM, the R-squared improved to 0.589, with RFM and EM considered more influential on dengue compared to IS and OS. ATM was however removed due to multicollinearity. The use of Spatial Error regression (SER) and Spatial Lag regression (SLR) produced improved models relative to the OLS model with R-squared of 0.676 and 0.667, respectively. This indicates the importance of spatial dependence. This can be explained by the fact that mosquitos fly over considerably long distances, traversing across the different barangays. The SER model (AIC=1359.89; SE=26.9584) is slightly better than the SLR model (AIC=1366.05; SE= 27.3399).

The patterns of depopulation in Bosnia and Herzegovina: Spatial autocorrelation analysis

Depopulation is one of the key contemporary demographic challenges for Bosnia and Herzegovina. Adverse demographic trends are causing various problems for Bosnian society and its economy, affecting the national and local labor markets, the public pension system, education, the public health system, etc. Therefore, the purpose of this research is to analyze the spatial patterns of depopulation in Bosnia and Herzegovina in order to identify the areas of the country that are particularly vulnerable to negative demographic trends and to identify the factors that may be contributing to these trends. A geospatial analysis of depopulation was carried out by using global and local indices of spatial autocorrelation. To identify spatial patterns of depopulation in Bosnia and Herzegovina, the study used the data on the total population change, natural population change, the vital index, the ageing index, and the ageing coefficient for the period 2013-2021 at the municipal level. The findings of this study can serve as a basis for future demographic research and the demographic development of Bosnia and Herzegovina.

Anthrax hotspot mapping in Kenya support establishing a sustainable two-phase elimination program targeting less than 6% of the country landmass

Using data collected from previous (n = 86) and prospective (n = 132) anthrax outbreaks, we enhanced prior ecological niche models (ENM) and added kernel density estimation (KDE) approaches to identify anthrax hotspots in Kenya. Local indicators of spatial autocorrelation (LISA) identified clusters of administrative wards with a relatively high or low anthrax reporting rate to determine areas of greatest outbreak intensity. Subsequently, we modeled the impact of vaccinating livestock in the identified hotspots as a national control measure. Anthrax suitable areas included high agriculture zones concentrated in the western, southwestern and central highland regions, consisting of 1043 of 1450 administrative wards, covering 18.5% country landmass, and hosting 30% of the approximately 13 million cattle population in the country. Of these, 79 wards covering 5.5% landmass and hosting 9% of the cattle population fell in identified anthrax hotspots. The rest of the 407 administrative wards covering 81.5% of the country landmass, were classified as low anthrax risk areas and consisted of the expansive low agricultural arid and semi-arid regions of the country that hosted 70% of the cattle population, reared under the nomadic pastoralism. Modelling targeted annual vaccination of 90% cattle population in hotspot administrative wards reduced > 23,000 human exposures. These findings support an economically viable first phase of anthrax control program in low-income countries where the disease is endemic, that is focused on enhanced animal and human surveillance in burden hotspots, followed by rapid response to outbreaks anchored on public education, detection and treatment of infected humans, and ring vaccination of livestock. Subsequently, the global anthrax elimination program focused on sustained vaccination and surveillance in livestock in the remaining few hotspots for a prolonged period (> 10 years) may be implemented.

Spatiotemporal analysis of malaria incidence in Côte d'Ivoire from 2015 to 2019.

The collection of malaria cases over time allows the identification of areas with the highest incidence. Our objective was to characterize the spatial distribution of malaria in Côte d'Ivoire from 2015 to 2019 at the health district level. Data on the number of reported malaria cases confirmed by rapid diagnostic test (RDT) in the general population, the number of patients attending medical consultations and the total population by health district and year were collected from the National Malaria Control Program in Côte d'Ivoire. Crude and adjusted incidence rates were estimated for each health district and year. Adjusted incidence rates were used to perform global (Moran's index) and local indicators of spatial autocorrelation (LISA) analyses. Between 2015 and 2019, mean crude incidence rates increased from 155.5‰ to 229.8‰. We observed significant heterogeneity in malaria incidence rates across the study period and within a given year. The overall Moran index showed spatial autocorrelation for every year analysed except 2017. The LISA analysis showed that the health districts with high incidence rates were concentrated in the western zone of Côte d'Ivoire. The use of spatial analyses to identify the areas with the highest malaria incidence rates is a relevant approach to optimize control measures in targeted areas.

Import of Agricultural Products in the Intra-Regional Comprehensive Economic Partnership (RCEP)

The flow of agricultural products through imports has the potential to increase with the geographical conditions of adjacent RCEP countries. Economic and non-economic factors can affect imports of agricultural products. This study aims to analyze the spatial effect and the factors that influence the import of agricultural products in Intra RCEP. This study uses a data period from 2013-2019. The analytical method used in this research is Moran's global index, Local Indicator of Spatial Autocorrelation (LISA), and Geographically Weighted Panel Regression (GWPR). The results show a spatial effect of imports of agricultural products in intra-RCEP. China, Japan, and South Korea are countries that are geographically concentrated in importing agricultural products within the intra-RCEP. The factors that significantly affect the total value of imports of agricultural products are GDP per capita, trade openness, Foreign Direct Investment (FDI), and government effectiveness. The policy recommendations in this study are implementing the RCEP agreement in the long term, open market access, encouraging increased investment in the agricultural sector, and an efficient bureaucracy.

Evaluating urban environment quality (UEQ) for Class-I Indian city: an integrated RS-GIS based exploratory spatial analysis

Urban environmental quality consisting of ecological, physical, and socio-economic components, often deteriorates due to rapid urbanization. Therefore, using Remote sensing and GIS environment, a composite measure is applied to quantify the spatial heterogeneity of urban environmental quality for the Class-1 Indian city (Siliguri). In this study, the Urban Environmental Quality Index was constructed using 15 indicators and three interconnected dimensions (eco-environment, landscape and built-up, and socio-economy). The three domains and Urban Environmental Quality Index were computed utilizing Principal Component Analysis with average aggregation techniques. Exploratory Spatial Data Analysis includes Moran’s I and Local indicator of spatial auto-correlation, were used to leverage the information of spatial clusters, spatial heterogeneity, and outliers based on the Urban Environmental Quality Index. The results show that Siliguri’s northern, north-western, and southern parts experience good environmental quality. The effectiveness of the employed model was checked using R2 (0.832), providing a good fit for the model. Moreover, the spatial pattern of urban environmental quality and the constructed domains (except socio-economy) revealed that the Low-Low values were predominantly clustered in the city centre, while High-High patterns are concentrated towards the periphery. Also, the value of Moran’s I indicated the existence of spatial autocorrelation and non-randomness pattern in Siliguri City. The results obtained from the analysis indicate spatial heterogeneity and spatial differentiation across the study area. The study’s outcome is relevant for urban planning, frequent monitoring of urban environmental quality, urban governance, and the well-being of urban inhabitants for a sustainable urban space.