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The glacier is a crucial freshwater resource in arid and semiarid regions, and the vulnerability of the glacier change is intimately linked to regional ecological services and socio-economic sustainability. Taking the Tianshan Mountains region in China as an example, a basic framework for studying the vulnerability of glacier change was constructed so as to address factors such as physical geography, population status, socio-economic level, agricultural development, and social services. The framework was based on key dimensions, that is, exposure, sensitivity, and adaptability, and this constituted a targeted evaluation index system. We examined the spatial structure and spatial autocorrelation of the glacier change vulnerability using ArcGIS and GeoDa software. The influence and interaction of natural, social, economic, population and other factors on glacier change adaptability was examined using the GeoDetector model. The results suggested the following: (1) The vulnerability level decreased from the western region to the eastern region with significant differences between the two regions. The eastern region had the lowest vulnerability, followed by the central region, and then western region which had the highest vulnerability. (2) Significant positive and negative correlations were found between exposure, sensitivity, and adaptability, indicating that the areas with high exposure and high sensitivity to glacier change tended to have a low adaptive capacity, which led to high vulnerability, and vice versa. (3) The spatial heterogeneity regarding the ability to cope with glacier change reflected the combined effects of the natural, social, economic, and demographic factors. Among them, factors such as the production value of secondary and tertiary industries, the urban population, urban fixed-asset investment, and the number of employees played major roles regarding the spatial heterogeneity of glacier change.

To effectively distribute resources and formulate policies, governments and enterprises usually need to obtain accurate geographical information about housing transaction volumes. In this study, the data of counties and cities in Taiwan from 2009 to 2018 were used for the analysis. First, Moran’s I was adopted to test the degree of spatial autocorrelation of the housing transaction volumes of the counties and cities, and GeoDa, a piece of spatial statistical software, was utilized to draw the spatial distribution and local indicators of spatial association cluster of the housing transaction volumes of the counties and cities in Taiwan. After the explanatory comparison between the panel data model and multiple spatial economic models, the Spatial Durbin Model was employed to explore the direct effects on local housing transaction volumes and the spillover effect of neighborhood on the housing transaction volumes at different geographical locations, especially the spatial spillover effect of local fiscal expenditures and the regulations for planned urban environments. Finally, policy implications were offered as reference for the governance of the authorities.

This study has conducted in the United States of America (USA) in the 1960s, 1970s, 1980s, and 1990s to determine whether divorce rates, unemployment rates, and the population had an impact on homicide numbers. Initially, all variables were examined and interpreted geographically on the map by districts. Subsequently, the stationary circumstance of variables has been tested with the Augmented Dickey-Fuller (ADF) Test, which is one of the unit root tests. After it has found that the variables did not need to stabilize, regression analysis has performed by the Least Squares (LS) method. Quantile regression, which is an alternative method to the LS method, has been used since all the resulting models do not have a normal distribution. These models have been created with 3 diverse quantile values for each period. Among these models, the ones with the highest correlation coefficient are the models having the 0.75 quantile value. Therefore, the results have been obtained from models with the 0.75 quantile value. Hence, for the homicide counts in the USA, those have found that the country population had a positive effect in the 1960s, the country population and the divorce rates had positive effects in the 1970s, the country population had a positive effect in the 1980s, and the country population and the unemployment rates had positive effects in the 1990s. Furthermore, the unemployment rates in the 1970s and 1980s had a negative effect on the homicide counts in the USA., Bu çalışma, 1960, 1970, 1980 ve 1990'larda ABD'deki boşanma oranlarının, işsizlik oranlarının ve ülke nüfusunun cinayet sayılarını etkileyip etkilemediğini belirlemek amacıyla yapılmıştır. İlk olarak, tüm değişkenler harita üzerinde bölgeler tarafından coğrafi olarak incelenmiş ve yorumlanmıştır. Ardından değişkenlerin durağanlık durumu, birim kök testlerinden biri olan ADF testi ile test edilmiştir. Değişkenlerin durağanlaştırılmasına gerek olmadığı tespit edildikten sonra, en küçük kareler (EKK) yöntemi ile regresyon analizi yapılmıştır. Elde edilen tüm modellerin normal dağılım göstermemesi nedeniyle, EKK yöntemine alternatif bir yöntem olan kantil regresyon kullanılmıştır. Oluşturulan bu modellerde, her bir dönem için 3 farklı kantil değeri kullanılmıştır. Bu modeller arasında 0,75 kantil değerine sahip modeller, en yüksek korelasyon katsayına sahiptir. Bu nedenle 0,75 kantil değerine sahip modeller kullanılarak sonuçlar elde edilmiştir. Elde edilen bu sonuçlara göre, ABD’deki cinayet sayıları için; 1960'larda yalnızca ülke nüfusunun olumlu bir etkisi olduğu, 1970'lerde ülke nüfusunun ve boşanma oranlarının olumlu etkileri olduğu, 1980'lerde ülke nüfusunun olumlu bir etkisi olduğu ve son olarak ülke nüfusunun ve işsizlik oranlarının 1990'larda olumlu etkilerinin olduğu bulunmuştur. Ayrıca, işsizlik oranlarının 1970 ve 1980'lerde cinayet sayıları üzerinde olumsuz bir etkisinin olduğu sonucu da elde edilmiştir.

Exploratory Spatial Analysis Techniques (ESDA) have become popular to identify the spatial association of different variables in many fields of natural and social sciences. The application of Global Moran’s I statistics enables us to provide visual insights of spatial data. It helps to detect spatial patterns and hotspots of an activity or process, based on spatial autocorrelation. This study aims to investigate the spatial dependence of domestic and inbound tourist arrivals to 123 cities of all eight provinces of the Aegean Region of Turkey. For analysis, city-level data about domestic and inbound tourist arrivals during 2015–2019 is collected from the Turkish Ministry of Culture and Tourism and is converted to logarithm form to avoid any skewness. The Arc GIS and GeoDa programs are employed for the analysis of spatial autocorrelation and visualization of hotspots of tourist flows in the regions. The results of the study reveal that tourist flows in the region are concentrated in the coastal areas, while inland cities receive an insufficient number of tourists. The hotspots of tourist flow are located mostly in the coastal towns of the provinces of Izmir, Aydın, and Muğla. The study is significant in the provision of useful information regarding resource allocation to the tourism hotspots and the implication of sustainable tourism policy to better utilization of tourism potential.

Background : Daily energy requirements must be fulfilled for a healthy and active life. Prevalence of Undernourishment ( PoU ) is an indicator which are developed for goal. A number of factors were expected affecting PoU were tested in this study. We also tested the possibility of these factors could have a spatial correlation Objectives: The study produced a map of the spread of PoU and the factors which were influenced them (independent variables). The study need to yield the best estimation model. Method : This study produce spread map of all variables, for visible purposes, and the classical regression were made. All OLS assumption will be test. Then, SAR and SEM models will be made. Finally, the best model for this study will be chosen. GeoDa software helps all steps. Results : This study concludes PoU has positive auto correlation and growth has a negative autocorrelation. The best model which produced is the Spatial Error Model (SEM). The slow trend of PoU in West Sumatera Barat is strongly suspected due to the habit of West Sumatera residents in consuming high-calorie foods such as rendang, curry and coconut milk.

To investigate the social determinants of cervical cancer screening and report the locations vulnerable to poor utilization of cervical cancer screening services.An ecological study with the data derived from fourth round of the National Family Health Survey conducted in India in the period 2015-2016.The study focused on the percentage of women who have never undergone cervical cancer screening across 639 districts in India. Moran's I statistic was used to investigate the overall clustering of location. The Getis-Ord Gi* statistic was used for the detection of significant local clusters. Spatial error, spatial lag, spatial Durbin and spatial Durbin error models were compared, and the model with best fit was reported. ArcGIS, GeoDa and R software were used for the analysis.The existence of spatial autocorrelation (Moran's I = 0.61) necessitates the consideration of spatial component while studying the screening data. A significant clustering of districts with poor screening has been observed in the North-Central and North-Eastern regions of India. The geographic arrangement of the percentage of women who have undergone cervical cancer screening was associated with the percentage of women with poor wealth index (P 0.001), not using a modern method of contraception (P 0.001), residing in rural areas (P = 0.033) and never heard of sexually transmitted infection (P = 0.014). The range of percentage of women getting cervix screened for cancer was 0.5-68.4%, presenting the heterogeneity among the population elements.A higher risk of poor cervical cancer screening is observed in the districts where most of the women have poor wealth index, reside in urban area, have never heard of sexually transmitted infection and do not use a modern method of contraception.

Leaf area is a component of crop growth and yield prediction models. Few studies have used the structure from motion (SfM) algorithm, which is based on the principles of traditional stereophotogrammetry, to obtain the leaf area index (LAI). Thus, the objective of this study was to follow the evolution of the LAI and percentage of land cover (%COV) in coffee plants, using pre-established equations and plant measurements obtained from generated 3-D point clouds, combined with the application of the SfM algorithm to digital images recorded by a camera coupled to an unmanned aerial vehicle (UAV). The experiment was conducted in a coffee plantation located in southeastern Brazil. A rotary wing UAV containing a conventional camera was used. The images were collected once per month for 12 months. Image processing was performed using PhotoScan software. Regression analysis and spatial analysis were performed using R and GeoDa software, respectively. The resulting %COV data had R2 and RMSE values of 89% and 3.41, respectively, while those for LAI had R2 and RMSE of 88% and 0.47, respectively. Significant %COV results were obtained in the months of January, February, and March of 2018. There was significant autocorrelation for the LAI values from January to May 2018, with most blocks in the central and center-west regions presenting LAI values > 3.0. It was possible to monitor the temporal and spatial behavior of the LAI and %COV, allowing for the conclusion that this methodology generated results that are consistent with the literature.

Background Despite public health efforts to reduce the leprosy burden in Yunnan, China, leprosy remains an important public health problem in some specific areas. We analyzed the epidemiological characteristics and spatial distribution of leprosy in Yunnan, China, and provide data to guide disease prevention and control efforts. Methodology/principal findings The surveillance data of newly detected leprosy cases in Yunnan, China, during 2011–2020 were extracted from the LEPROSY MANAGEMANT INFORMATION SYSTEM IN CHINA (LEPMIS), and spatial distribution analysis, spatial autocorrelation analysis, and spatiotemporal scanning were performed with ArcGIS 10.6.1, GeoDa 1.8.8, and SaTScan 9.4.3 software, respectively. A total of 1907 newly detected leprosy cases were reported in Yunnan, China, during 2011–2020. The new case detection rate (NCDR) decreased from 0.62 in 2011 to 0.25 in 2020, with an annual incidence of 0.41/100,000 population. The proportions of multibacillary (MB) cases, cases in female patients, cases causing grade 2 physical disability (G2D), and cases in pediatric patients were 67.07%, 33.93%, 17.99%, and 2.83%, respectively. The number of counties with an incidence above 1/100,000 population decreased from 30 in 2011 to 8 in 2020. The Moran’s I of leprosy in Yunnan, China, during 2011–2020 ranged from 0.076 to 0.260, indicating the presence of spatial clusters. Local spatial autocorrelation (LSA) analysis showed that high-high cluster areas (hot spots) were mainly distributed in the southeastern, northern, and northwestern regions. Spatiotemporal scanning showed three clusters with high NCDRs. The probably primary clusters, occurring during January 1, 2011–December 31, 2015, covered 11 counties in the southeastern region (RR = 5.046515, LRR = 271.749664, P = 0.000). Conclusion The number of leprosy cases in Yunnan decreased overall, although some high-NCDR regions remained. Geographic information system (GIS) analysis coupled with spatial analysis indicated regions with leprosy clusters. Continuous leprosy prevention and control strategies in Yunnan Province should be established, and interventions in high-risk regions should be prioritized and further strengthened., Author summary China has achieved the goal of leprosy elimination established by the WHO. The overall incidence and prevalence rates of leprosy indicate a low endemic level in China. However, there are still specific areas with leprosy transmission in some parts of China, especially Yunnan Province. This study aimed to reveal the epidemic state and identify spatial and spatiotemporal clusters of leprosy in Yunnan, China, from 2011–2020. A total of 1907 newly detected cases were identified; 67.07% were MB and 32.93% were PB leprosy cases. Males were predominant (66.07%), 17.99% of patients presented with G2D, and 2.83% of patients were under 15 years old. Performed with ArcGIS 10.6.1, GeoDa 1.8.8, and SaTScan 9.4.3 software, three significant spatial clusters (hot spots) and three significant spatiotemporal clusters (high-risk areas) were observed. These results highlight the at-risk areas for prioritization and further intervention.

River eco-environmental value assessment is indispensable for the optimal allocation of watershed water resources. In this study, river eco-environmental values were divided into the values inside and outside the river based on the energy transfer and transformation of the water cycle. Their spatial distribution characteristics (spatial distribution map, spatial autocorrelation, and spatial aggregation) of 67 regions (states, leagues, and cities) in the Yellow River Basin (YRB) were analysed by combining emergy theory and GeoDa 1.14 software (emergy-GeoDa), and the significance of the results was tested. The results showed that: (1) the eco-environmental values inside the river were higher than those outside in the YRB, proving that eco-environmental water inside the river should be guaranteed and water consumption outside the river should not occupy water inside the river from the perspective of value; (2) the spatial distributions of eco-environmental values inside and outside the river were uneven, but obvious spatial aggregations were observed; (3) high- and low-value aggregations of eco-environmental values were observed inside the river in the lower and upper reaches of the YRB, respectively; (4) high- and low-value aggregations of eco-environmental values were observed outside the river in the middle and upper reaches of the YRB, respectively. It was suggested that ecological conservation and high-quality development should be considered as the goals for consolidating the river eco-environmental values in high-value areas and promoting in low-value areas, and low-value areas should be improved by high-value areas while allocating water resources in the YRB. These results provide suggestions for the sustainable development of river eco-environmental system in the YRB from a spatial perspective. In addition, the analysis method is also applicable for studying the spatial distribution characteristics of the values generated by the water and energy cycles of other regions.

Extensive urbanization alters the natural landscape as vegetation were replaced with infrastructures composed of materials with low albedo and high heat capacity often resulting to increase in land surface temperatures (LST). The present study focused on the spatial and temporal variations of LST in Mandaue City, one of the metropolitan cities in the Philippines that had undergone a rapid rate of urbanization over the past years. Climate Engine (CE), a cloud computing tool that processes satellite images, was used in this study. Preprocessed LST, normalized difference water index (NDWI), normalized difference vegetation index (NDVI), shortwave infrared (SWIR 1) and near-infrared (NIR) layers were directly downloaded from CE while the normalized difference built-up index (NDBI) maps were calculated. Time-series dataset of these indices were analyzed to determine the impacts of reduced vegetation cover and increased built-up areas on surface temperature from years 2013 to 2019. The spatial distribution of LST were analyzed using Univariate Local Moran’s I in GeoDa to identify hotspots within the city. Analysis results showed that the hotspots are barangays Tipolo (100%), Bakilid (100%), Ibabao-Estancia (93.5%), Alang-Alang (87.2%), Guizo (84.4%), Subangdaku (84.1%), and Centro (79.4%). The results indicated that there is a linear relationship between LST and NDBI (r = 0.659, p r = −0.527, p r = −0.620, p&thinsp

Urbanization has played an important part in the development of the society, yet it is accompanied by environmental concerns including the increase of local temperature compared to its immediate surroundings. The latter is known as Urban Heat Islands (UHI). This research aims to model UHI in Quezon City based on Land Surface Temperature (LST) estimated from Landsat 8 data. Geospatial processing and analyses were performed using Google Earth Engine, ArcGIS, GeoDa, and SAGA GIS. Based on Urban Thermal Field Variance Index (UTFVI) and the normalized mean per barangay (village), areas with strong UHI intensities were mapped and characterized. high intensity UHIs are observed mostly in areas with high Normalized Difference Built-up Index (NDBI) like the residential regions while the weak intensity UHIs are noticed in areas with high Normalized Difference Vegetation Index (NDVI) near the La Mesa Reservoir. In the OLS regression model, around 69% of LST variability is explained by Surface Albedo (SA), Sky View Factor (SVF), Surface Area to Volume Ratio (SVR), Solar Radiation (SR), NDBI and NDVI. OLS yield relatively high residuals (RMSE = 1.67) and the residuals are not normally distributed. Since LST is non-stationary, Geographically Weighted Regression (GWR) regression was conducted, proving normally and randomly distributed residuals (average RMSE = 0.26).

One of the key steps that would lead winegrowers to implement precision viticulture as a management tool would be the clear demonstration of the agronomic and oenological significance of the zones delineated within a vineyard based, totally or partially, on remote-acquired information. To perform this analysis, it is necessary to compare image-derived variables to crop characteristics. Classical ordinary least square (OLS) regression is not well suit for spatially structured data, while Moran’s index (MI) and local indicators of spatial autocorrelation (LISA) take autocorrelation into account. The aim of this work was to evaluate the performance of statistical methods to compare different maps of a vineyard, some including variables derived from UAV acquired imagery, and some from in situ ground characterization. The study was conducted during 2015 and 2016 seasons in an adult 7.5 ha cv. ‘Tempranillo’ vineyard located in Traibuenas, Navarra, Spain. The maps obtained out of UAV-imagery, volume index (VI) and normalized difference vegetation index (NDVI) were compared to the maps obtained for the agronomic variables measured (yield, berry weight and total soluble solids). The bivariate MI and the bivariate LISA cluster map obtained using Geoda software indicate depict the spatial cluster association between variables in 2015 and 2016 with different types of local spatial autocorrelation. The use of these methods that take into account data spatial structure, to compare ground autocorrelated data and spectral and geometric information derived from UAV-acquired imagery has been proved to be highly necessary and advisable.

In this study, the southern expressway, which is the first and lengthiest E class highway (126 km) in Sri Lanka, was analysed for roadside accident incidences. The primary objective of this paper is to identify the best-fit interpolation techniques for the hotspots' most distinctive causes of vehicular crashes. The accident details were collected from the Police Headquarters consisting of 966 accidents that took place during the period from 2015 to 2017. To identify accident hotspots, GIS-based interpolation techniques such as Ordinary Kriging, Kernel Density Estimation (KDE), Inverse Distance Weighting (IDW), and Nearest Neighbour Interpolation methods were used. The spatial interpolation outcome of the four methods was compared based on the standard Prediction Accuracy Index (PAI). The analysis was executed using QGIS and GeoDa. Results of PAI revealed that an IDW and KDE outperformed the other two interpolation methods. The left and right lanes of the expressway, spotted with 11 and 20 hotspots, respectively, indicate the right lane was 50% more prone to accidents than the left lane. Notably, nearly 5% of the entire road stretch is estimated as accident-prone spots in both lanes. Peak accidents were recorded during afternoon and evening hours, and buses were the most active vehicle type. Uncontrolled speeding was the primary reason for more than 50% of the accidents, while unsuccessful overtake accounted for more than 20% of the accidents on the highway. The road design modifications and warning sign placements at appropriate places may be recommended as countermeasures.

The problem of land ecological security directly threatens the sustainable development of many regions, and exploring the spatio-temporal characteristics of land ecological security is helpful for analyzing the land ecological patterns between regions and over time. Based on the interpretation of remote sensing image data for Tianjin in 1980, 2000, 2010 and 2019, supported by software such as ArcGIS and GeoDa, the changes of land use in the study area are calculated by using the land use dynamic degree. Then, the land ecological security index and spatial autocorrelation analysis methods are used to study the spatial correlations and internal heterogeneity of land ecological security in each district in Tianjin. The results show that: (1) The land use of Tianjin has changed dramatically in the past 40 years: the building land has been expanding, while the farmland and barren have been shrinking continuously. (2) The overall level of land ecological security is in the high security area, and Jizhou District is the highest, while the Central areas and Binhai are low. (3) The spatial heterogeneity of land ecological security is not obvious, but the spatial agglomeration is strong.

COVID-19 is an infectious disease caused by the coronavirus, SARS-CoV-2 The spread of several types of diseases, including COVID-19, is influenced by location or spatial factor The purpose of this research was to examine the spatial relationship among the spread of COVID-19 in Indonesia Secondary data is used from the Indonesian Ministry of Health website The data analyzed was the cumulative number of positive cases of COVID-19 in Indonesia until October 19[sup th], 2020 Spatial Autocorrelation, Moran's Index, and Local indicators of spatial association (LISA) were used to examine these relationships Geoda software was used in performing spatial statistical analysis The spread of the cumulative number of COVID-19 cases in Indonesia is spatially related, with a Moran's Index value of 0 251 Four provinces have significant LISA scores, namely South Sumatra, Banten, West Java, and South Sulawesi South Sumatera is relatively safe since it is located in the an area with relatively low COVID-19 cases West Java is perilouse with high COVID-19 cases and surrounded by an area with a high number of cases Banten needs to be vigilant of the high number of cases' possibility since the surrounded area can be the main influence South Sulawesi has a very high number of COVID-19 cases, which can be the new source of infection for the surrounding areas The Indonesian government needs to act on a regulation in order to decrease COVID-19 distribution by limiting people's commute between provinces

Many research in economics only focus on the independence of a region while neglecting the effects of space and the interaction that occurs between mutually adjacent areas. The purpose of this study is to measure the multidimensional poverty concept in 15 districts/cities in the province of Lampung in 2015-2019. Spatial analysis such as moran i statistics, LISA clustered map, and lisa signification are used to analyze spatial patterns and spatial autocorrelation. Spatial modeling with spatial autoregressive model, geoda and geographical information systems are used as explanatory spatial data and spatial modeling. The results show that the percentage of poor people between districts/cities in Lampung Province have positive Moran's I values, there is a clustered pattern in 2015-2019, Moran scatter plot depicts 4 quadrants, LISA Cluster map indicates high-high and low-low areas, and LISA map has 4 significant areas. Spatial regression results show that per capita expenditure for nonfood has a negative effect, per capita expenditure for food has a positive effect, population growth rate has a positive effect, household clean water has a positive effect, life expectancy has a negative effect, mean years of schooling has a negative effect, and simultaneously the independent variables have a significant influence on the percentage of poor people. Poverty in Lampung Province is spatially related to each other between regions, the findings suggest that the variables used affect spatially. The implication of this result is one of the basis for inter-regional policies in the interests of multi-dimensional poverty alleviation between regions.Keywords: Poverty, Spatial analysis, Spatial Autoregressive Model (SAR)

Aim: This research analyzed a joint spatial distribution and explored the possible associations between epidemiological aspects and feminicide rates, in towns of the Sergipe State in Northeastern Brazil. Methodology: An exploratory ecological study investigated the global spatial autocorrelation of epidemiological aspects with femicide rates from towns in Sergipe State, Brazil, (n = 75), in the 2013–2017 period, using the “global” and “local” Moran statistic method and a multiple spatial regression. The exposure variables included socioeconomic and demographic conditions, services and health condition, and femicide rates. We used the software Stata 11.0, SPSS 18.0 and GeoDa 0.95-i. Results: The spatial distribution of femicide rates was not random and showed high spatial autocorrelation and predominance of significant spatial groupings of towns with the highest mortality rates due to femicide in the central region of Sergipe State. In the multiple regression analysis, the percentage of women in charge of families and the Municipal Human Development Index were positively associated with the femicide rates in towns in Sergipe's municipalities in the studied period (p < 0.05). The opposite situation occurred between the Income Concentration Index (GINI) and the femicide rates. Conclusion: This is the first study that has analyzed the factors associated with the spatial clusters of femicide rates in a geographical space where there is a predominance of patriarchal culture. There was a femicide increase in locations with the lowest social inequality, the highest human development and authority exercised by women in the family environment. RESUMO: Objetivo: Esta pesquisa analisou a distribuição espacial conjunta e explorou possíveis associações entre aspectos epidemiológicos e taxas de feminicídio, nos municípios do Estado de Sergipe, no nordeste brasileiro. Métodos: Um estudo ecológico exploratório investigou a autocorrelação espacial global de aspectos epidemiológicos com taxas de feminicídio em municípios do Estado de Sergipe, Brasil (n = 75), no período de 2013 a 2017, utilizando a estatística de Moran “global” e “local” e uma regressão espacial múltipla. As variáveis incluíram a condição socioeconômica, a situação demográfica, os serviços e estado de saúde e as taxas de feminicídio. Os softwares utilizados foram Stata 11.0, SPSS 18.0 e GeoDa 0.95-i. Resultados: A distribuição espacial das taxas de feminicídio não foi aleatória e apresentou elevada autocorrelação espacial e predomínio de agrupamentos espaciais significativos de municípios com as maiores taxas de mortalidade por feminicídio na região central do Estado de Sergipe. Na regressão múltipla, o percentual de mulheres chefes de família e o Índice de Desenvolvimento Humano Municipal associaram-se positivamente às taxas de feminicídio nos municípios sergipanos no período estudado (p < 0,05). Situação oposta ocorreu entre o Índice GINI e as taxas de feminicídio. Conclusão: Este é o primeiro estudo que analisou os fatores associados aos clusters espaciais das taxas de feminicídio em um espaço geográfico com predomínio da cultura patriarcal. Houve aumento do feminicídio em localidades em que há menor desigualdade social, maior desenvolvimento humano e maior autoridade exercida pelas mulheres no ambiente familiar.

Firstly, Moran's I in the spatial autocorrelation analysis method is used, and Geoda software is applied to sequentially analyze the spatial distribution of GDP per capita of 21 cities in Guangdong Province in 2018, the global autocorrelation test, the local autocorrelation test, as well as explore its spatial correlation and spatial heterogeneity. It is concluded that there is a significant spatial autocorrelation of GDP per capita of 21 cities in Guangdong Province in 2018. Secondly, the trend surface analysis method of ArcGIS is applied to conduct a three-dimensional trend analysis of the GDP per capita of each city in Guangdong Province in 2018. Finally, a spatial regression model is used to calculate the influencing factors of the spatial differences of GDP per capita of 21 cities in Guangdong Province in 2018, and it is concluded that the three factors of total retail sales of consumer goods, foreign exchange income from international tourism, internal expenditure of R&D funds have a positive relationship with GDP per capita, however the number of employed persons at the end of the year has a negative relationship with GDP per capita in Guangdong province in 2018.