Geographically Weighted Regression Analysis Research Articles

Geospatial variation of exclusive breastfeeding and its determinants among mothers of infants under 6 months in Ethiopia: spatial and geographical weighted regression analysis

BackgroundExclusive breastfeeding (EBF) is a major public health problem in Ethiopia. However, the spatial variation of EBF and the associated factors have not been studied as much as we have...

Mastering geographically weighted regression: key considerations for building a robust model.

Geographically weighted regression (GWR) takes a prominent role in spatial regression analysis, providing a nuanced perspective on the intricate interplay of variables within geographical landscapes (Brunsdon et al., 1998). However, it is essential to have a strong rationale for employing GWR, either as an addition to, or a complementary analysis alongside, non-spatial (global) regression models (Kiani, Mamiya et al., 2023). Moreover, the proper selection of bandwidth, weighting function or kernel types, and variable choices constitute the most critical configurations in GWR analysis (Wheeler, 2021). [...].

Coupling heat wave and wildfire occurrence across multiple ecoregions within a Eurasia longitudinal gradient

Understanding the relationship between heat wave occurrence and wildfire spread represents a key priority in global change studies due to the significant threats posed on natural ecosystems and society. Previous studies have not explored the spatial and temporal mechanism underlying the relationship between heat waves and wildfires occurrence, especially over large geographical regions. This study seeks to investigate such a relationship with a focus on 37 ecoregions within a Eurasia longitudinal gradient. The analysis is based on the wildfire dataset provided by the GlobFire Final Fire Event Detection and the meteorological dataset ERA5-land from Copernicus Climate service. In both cases we focused on the 2001–2019 timeframe. By means of a 12 km square grid, three wildfire metrics, i.e., density, seasonality, and severity of wildfires, were computed as proxy of fire regime. Heat waves were also characterized in terms of periods, duration, and intensity for the same period. Statistical tests were performed to evaluate the different patterns of heat wave and wildfire occurrence in the 37 ecoregions within the study area. By using Geographically Weighted Regression (GWR) we modeled the spatial varying relationships between heat wave characteristics and wildfire metrics. As expected, our results suggest that the 37 ecoregions identified within the Eurasia longitudinal gradient differ in terms of fire regimes. However, the occurrence of heat waves did not show significant differences among ecoregions, but a more evident variability in terms of relationship between fire regime metrics and heat waves within the study area. The outcome of the GWR analysis allowed us to identify the spatial locations (i.e., hotspot areas) where the relationship between heat waves and wildfires is positive and significant. Hence, in hotspots the presence of heat waves can be seen as a driver of wildfire occurrence in forest and steppe ecosystems. The findings from this study could contribute to a more comprehensive assessment of wildfire patterns in this geographical region, thus supporting cross-regional prevention strategies for disaster risk mitigation.

Geographic weighted regression analysis of hot spots of modern contraceptive utilization and its associated factors in Ethiopia.

Utilization of modern contraceptives is a common healthcare challenge in Ethiopia. Prevalence of modern contraception utilization is varying across different regions. Therefore, this study aimed to investigate Geographic weighted regression analysis of hotspots of modern contraceptive utilization and its associated factors in Ethiopia, using Ethiopian Demographic and Health Survey 2016 data. Based on the 2016 Ethiopian Demographic Health Survey data, a total weighted sample of 8,673 women was included in this study. For the Geographic Weighted Regression analysis, Arc-GIS version 10.7 and SaTScan version 9.6, statistical software was used. Spatial regression was done to identify factors associated with the hotspots of modern contraceptive utilization and model comparison was carried out using adjusted R2 and AICc. Variables with a p-value < 0.25 in the bi-variable analysis were considered for the multivariable analysis. Multilevel robust Poisson regression analysis was fitted for associated factors since the prevalence of modern contraceptive was >10%. In the multilevel robust Poisson regression analysis, the adjusted prevalence ratio with the 95% confidence interval was reported to declare the statistical significance and strength of association. The prevalence of modern contraceptive utilization in Ethiopia was 37.25% (95% CI: 36.23%, 38.27%). Most of the hotspot areas were located in Oromia and Amhara regions, followed by the SNNPR region and Addis Ababa City administration. Single Women, poor Women, and more fertility preference were significant predictors of hotspots areas of modern contraceptive utilization. In the multivariable multilevel robust Poisson regression analysis, Women aged 25-34 years (APR = 0.88, 95% CI: 0.79, 0.98), 35-49 years (APR = 0.71, 95% CI: 0.61, 0.83), married marital status (APR = 2.59, 95% CI: 2.18, 3.08), Others religions (APR = 0.76, 95% CI: 0.65, 0.89), number of children 1-4 (APR = 1.18, 95% CI: 1.02, 1.37), no more fertility preference (APR = 1.21, 95% CI: 1.11, 1.32), Afar, Somali, Harari, and Dire Dawa: (APR = 0.42, 95% CI: 0.27, 0.67), (APR = 0.06, 95% CI: 0.03, 0.12), (APR = 0.78, 95% CI: 0.62, 0.98), and (APR = 0.75, 95% CI: 0.58, 0.98), respectively. Amhara region (APR = 1.34, 95% CI: 1.13, 1.57), rural residence (APR = 0.80, 95% CI: 0.67, 0.95) High community wealth index (APR = 0.78, 95% CI: 0.67, 0.91) were significantly associated with modern contraceptive utilization. There were significant spatial variations of factors affecting modern contraceptive use across regions in Ethiopia. Therefore, public health interventions targeting areas with low modern contraceptive utilization will help to increase modern contraception use considering significant factors at individual and community levels.The detailed map of modern contraceptive use cold spots among reproductive age group and its predictors could assist program planners and decision-makers to design targeted public health interventions.Government of Ethiopia must develop more geographic targeted strategies for improving socioeconomic status of women and availability & accessibility of health facilities in rural areas of the countries.

Assessment and simulation of thermal environments in Taiyuan urban built-up area, China

The urban heat island (UHI) effect has intensified with increases in impervious surface areas and population densities due to urbanization, which affects the quality of urban life and ecological services. Here, the Moran’s I and hot spot analysis (Getis-Ord Gi*) are used to explore spatial autocorrelation of land surface temperature (LST) in Taiyuan built-up area. Then, the built-up area is divided to 41 sub-areas to accurately explore the urban LST differences caused by different LULC types. Moreover, geographically weighted regression (GWR) is used to analysis the spatial heterogeneity of LST. Finally, we simulate the LST changes using the CA-Markov model in the study area in the year 2030. Our results showed that 1) average LST was 22.76°C in Taiyuan built-up area in 2018. The Highest-temperature areas were distributed in heavy-industry intensive areas in the north, north central, and southeast, whereas the Lowest-temperature areas mainly corresponded to rivers, lakes, urban forests, and green spaces. 2) The Moran’s I gradually decreased from 0.8635 to 0.2097 with an increase in the spatial distance threshold. The optimal recognition effect was obtained at a 400 × 400-m scale. The Getis-Ord Gi* analysis indicated that the cold area was 1248.32 km2 (12.24% of the study area) and the hot area was 43.84 km2 (11.11% of the study area) in 2018. 3) The GWR analysis showed significant spatial non-stationarity in the influence of LULC types on LST. The GWR model was calculated with reference to the observation values of the adjacent areas, so as to better reveal the spatial relationship between artificial surface, woodland, water, grassland, and bare land and LST. 4) The UHI distribution was more concentrated in 2030 than in 2021. The statistics of the proportion and transfer matrix of LST indicated that the proportion of the Highest and Lowest-temperature areas in 2030 decreased and the UHI effect will further intensify. This study could be used to guide sustainable development in cities and provides theoretical support for adjusting the urban spatial structure.

Dynamics of land cover change, regional development, and its local dependence driving factors in Bojonegoro Regency

Bojonegoro is a regency in East Java province with a population of 1,322,474 (BPS, 2022) and high population density. Population growth is strong, and demand for land is increasing. Increased human activity leads to land conversion, which could impact regional development. The objectives of this study are to: 1) Analyze the dynamics of land cover change, 2) Compare the SDI (Sub-district Development Index) scores of 2008 and 2020 in Bojonegoro Regency, and 3) Analyze the factors affecting the level of Regional development in Bojonegoro Regency. The methods used are overlay (GIS analysis), scale plot analysis, and geographically weighted regression (GWR) analysis. The most significant land cover change occurred in forest cover, with the area decreasing from 99,815 hectares (2000) to 84,845 hectares (2020), while the built-up area increased from 12,665 hectares (2000) to 22,901 hectares ( 2020). Bojonegoro sub-district has the highest SDI of 44,043 (2008) and 44,917 (2020) because it has the largest population and become a capital district and central business district (CBD). Based on the results of the GWR analysis, the highest local R2 is located in the eastern part of the Regency. In contrast, the lowest local R2 is located in the western administrative district of Bojonegoro Regency. The results show that the driving forces influencing regional development in Bojonegoro Regency vary spatially.

Use of geographically weighted regression models to inform retail endgame strategies in South Korea: application to cigarette and ENDS prevalence

BackgroundReducing tobacco retailer availability is a key tobacco endgame policy. The development and evaluation of retail-based policies require spatial methodologies. We modelled the prevalence of adult cigarette and electronic nicotine...

Accessibility model of BRT stop locations using Geographically Weighted regression (GWR): A case study in Banjarmasin, Indonesia

• Composite measurement of accessibility can perform more effectively with GWR. • GWR performance on spatial variables results in a higher significance model. • The Mix-used entropy index criteria have the most significant influence on BRT accessibility. • Network Dataset can overcome the high demand for data on accessibility modeling. • GWR Simulation produces a visual-spatial model that easy-to-understand. Bus Rapid Transit (BRT) has advantages over rail-based systems as a public transportation system. The ease of implementation and low investment costs attract many cities to develop BRT systems, including Banjarmasin, Indonesia. Banjarmasin currently has eight BRT stop points that reach only two sub-districts out of five. The limited range of BRT stops within the city can affect the level of accessibility of the BRT system. The accessibility of the transit system itself can be seen from the number of daily passengers. This study aims to analyze the criteria that affect the level of accessibility of the BRT stops in the study area and then compile a model based on significant criteria. Previous literature on accessibility modeling shows varied methods and approaches. In this study, the system accessibility was measured using the composite method and modeled using Geographically Weighted Regression (GWR), which is a relatively new approach. The results show that seven criteria affect the level of accessibility of the BRT stops. The model was first built mathematically using OLS. Then, GWR analysis was accomplished on spatial variables, resulting in a higher significance model. Furthermore, the GWR produces a visual-spatial model and performs simulation and sensitivity tests to make the research purpose more informative. The spatial criteria for the accessibility of the BRT stop locations in the model include the distance of stops to the road intersection, mix-use entropy index, population density, and land value.

Towards a Sustainable and Safe Future: Mapping Bike Accidents in Urbanized Context

This manuscript presents a study on the spatial relationships between bike accidents, the built environment, land use, and transportation network characteristics in Budapest, Hungary using geographic weighted regression (GWR). The sample period includes bike crash data between 2017 and 2022. The findings provide insights into the spatial distribution of bike crashes and their severity, which can be useful for designing targeted interventions to improve bike safety in Budapest and be useful for policymakers and city planners in developing effective strategies to reduce the severity of bike crashes in urban areas. The study reveals that built environment features, such as traffic signals, road crossings, and bus stops, are positively correlated with the bike crash index, particularly in the inner areas of the city. However, traffic signals have a negative correlation with the bike crash index in the suburbs, where they may contribute to making roads safer for cyclists. The study also shows that commercial activity and PT stops have a higher impact on bike crashes in the northern and western districts. GWR analysis further suggests that one-way roads and higher speed limits are associated with more severe bike crashes, while green and recreational areas are generally safer for cyclists. Future research should be focused on the traffic volume and bike trips’ effects on the severity index.

Quantitative Evaluation of the Integrity of Natural Ecosystems and Anthropogenic Impacts in Shennongjia National Park, China

China launched its pilot national park system in 2015, and Shennongjia National Park has attracted much attention as one of the first batch of pilot national parks. The primary goal of national park construction in China is to effectively protect the integrity and authenticity of nationally important natural ecosystems. Based on the theory of landscape ecology, this study interpreted data from high-resolution remote sensing images and used landscape pattern analysis, spatial correlation analysis, and geographic weighted regression analysis to quantitatively evaluate the integrity of natural ecosystems at the landscape scale. A more accurate and operable calculation method was proposed to analyze the spatial variation in natural ecosystem integrity and to explore the scope and intensity of the impact of different anthropogenic activities on natural ecosystem integrity. The results showed that: (1) from the perspective of the spatial distribution patterns of natural ecosystem integrity, the degree of natural ecosystem integrity of Shennongjia National Park was generally high, with an integrity index value of 96.06, and the proportion of high-integrity areas accounted for 72.38%. The integrity index value of the strict protection zone was 98.83, and the proportion of the low-integrity index area only accounted for 0.01% of the strict protection zone, which was mainly distributed in the main urban areas of the nearby townships and along the highways in the national park, as well as in other areas with intensive anthropogenic activities. (2) From the perspective of the degree of impact of anthropogenic activities on natural ecosystem integrity, population density (0.3344), traffic accessibility (0.2389), traditional utilization activities (0.1101), and industrial and mining activities (0.0095) were, in descending order, the most impactful, and there was no significant correlation between ecotourism activities and natural ecosystem integrity. (3) From the perspective of the impact range of anthropogenic activities on natural ecosystem integrity, traditional utilization activities had the largest impact, accounting for 19.71% of the total area of the national park. The area affected by population density accounted for 1.52%. Industrial and mining activities had an influence of 4.75%, and the area affected by traffic accessibility accounted for 9.28%. Through conducting quantitative research into the integrity of natural ecosystems in Shennongjia National Park, this study provides a new research paradigm for the conservation of natural ecosystems and for the sustainable development of resources in protected areas, which is of great significance for the sensible development of national park conservation and management.

PENERAPAN METODE GEOGRAPHICALLY WEIGHTED REGRESSION PADA TINGKAT PENCEMARAN AIR BERDASARKAN TOTAL COLIFORM DI PROVINSI JAMBI

The purpose of this study is to obtain a local estimation model formed by considering spatial factors on the level of water pollution based on total coliforms in Jambi Province and can determine the most dominant factors affecting the total coliform water of PDAM Districts / Municipalities in Jambi Province using Geographically Weighted Regression (GWR) analysis. The type of data used in this study is primary data in quantitative form. This primary data was obtained by taking PDAM water samples directly in 9 regencies/cities in Jambi Province. The results of GWR analysis show that the dominant factors affecting the total coliform of PDAM water in each district/city in Jambi Province vary with different estimated parameters. The best model formed is a model with the GWR method, the adaptive bisquare weighting function which is shown from the value of the model's goodness criteria, namely the smallest AIC value and the largest coefficient of determination

Effects of urban environmental factors on heat-related emergency medical services (EMS) response time

Due to the time-sensitive nature of heat-related illnesses, disparities in access to heat-related emergency medical services (EMS) services may contribute to urban health disparities. This paper is an empirical study utilizing Austin-Travis County EMS data to estimate the delays in response time due to traffic congestion through spatiotemporal analysis and to conduct the Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR) models to examine the underlying factors affecting delays in peak traffic rush hours. Our results reveal that heat-related EMS is most delayed in the morning and the evening; there are higher clustering patterns of EMS travel time difference in Austin's metropolitan outskirts, notably in the east and west Austin. OLS and GWR analyses suggest that larger EMS counts, longer distances from an EMS station to the scene and from the scene to a hospital, and neighborhoods with a greater black and Hispanic population exacerbate heat-related EMS delays. Road density, average speed limit, and open space growth rate are statistically significant in the OLS model, although GWR findings suggest coefficient signs vary locally, requiring more investigation. Our findings provided additional insights through the spatial patterns of EMS delays to practitioners for their reference to reduce local response times.

Effects of socio-ecological factors on the pastoralists-farmers conflicts in Nigeria's Mid-Benue Trough

This study examined the effects of socio-ecological factors on the pastoralists-farmers conflicts in Nigeria's Mid-Benue trough from the year 2000–2020. To understand the nexus of climate and land-use change, population density, economic development and conflicts, the study used conflict data and analyzed satellite data of Land Surface Temperature (LST) as a proxy for climate change, and Land Use Land Cover (LULC) data. Also, Night Time Light (NTL) which was a proxy for economic development, as well as population density data were used. The data were analyzed through multiple spatial analyses such as classification and reclassification to distinguish feature classes and overlays to show probable relationships. The conflict data was overlaid on the other datasets and the events that occurred within each data class were identified, while the Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) were used to establish effects. Results showed that 87 percent of the conflicts occurred in areas with high LST (>30 °C) which increased by 10 percent, and 63 percent occurred in croplands which increased the most (25.7 percent). As well, 79.3 percent of the conflicts occurred in low population density areas (3–8 persons per 30 m2) and 77.3 percent in the lowest NTL radiance class (0–8 W/m2-sr). Further, the OLS and GWR analyses revealed that socio-ecological factors significantly influence the lethality of pastoralist-farmers conflicts in the Mid-Benue Trough as the three exploratory variables (climate change, economic development and population density) were statistically significant. The study recommends location-specific programs, policies and interventions to manage the conflicts based on deductions from the spatial analyses.

PEMODELAN MIXED GEOGRAPHICALLY WEIGHTED REGRESSION (MGWR) DENGAN JARAK EUCLIDEAN DAN JARAK MANHATTAN (STUDI KASUS : KEMATIAN BAYI NEONATAL DI JAWA TENGAH TAHUN 2018-2020)

Neonatal is a condition of babies from birth to 28 days. Data on Indonesia's health profile in 2020 showed that 72% of the number of deaths of toddlers occurred during the neonatal period and Central Java became the highest province of cases. Factors that are suspected to influence are the number of low birth weight babies (X1), the number of obstetric complications (X2), the number of Puskesmas (X3), the number of Posyandu (X4), the number of exclusive breastfeeding babies 0-6 months (X5), the number of pediatricians (X6), the number of ambulance cars (X7). Linear regression modeling on the number of neonatal infant deaths in Central Java has a heteroskedasticity problem so that Geographically Weighted Regression (GWR) is used. The distances used are Euclidean and Manhattan as well as the weighting function using Exponential and Tricube Kernel with Fixed Bandwidth. GWR modeling shows that not all independent variables are local, so Mixed Geographically Weighted Regression (MGWR) is used. The results of the GWR analysis with both distances and the two variable weighting functions are not local, including X2, X5, and X7. MGWR distance Manhattan Fixed Tricube Kernel became the better model, as the AICC value was smaller.

Evaluation for Water and Land Resources System Efficiency and Influencing Factors in China: A Two-Stage Network DEA Model

Improving the water and land resource system efficiency can effectively alleviate the severe situation of water and land resources in China. Through the two-stage network DEA model, spatial autocorrelation analysis, multiple linear regression, and geographic weighted regression analysis, this paper revealed the change characteristics, distribution types, spatial correlation relationship, and main driving factors of China’s water and land resources system efficiency. The results show that the water and land resources system efficiency fluctuates widely in different regions. Water and land resources systems in nearly half of the provinces belong to the high development, low economic benefit transformation type, mainly distributed in southwestern and northwestern China. The economic benefit transformation is becoming the weak link of water and land resources system. The overall efficiency of water and land resource system has significant spatial positive correlation, and this correlation has an increasing trend. Low-Low Clusters occupy more provinces. The urbanization level, population density, proportion of output value of secondary and tertiary industries, and effective irrigation all have a positive impact on the overall efficiency of water and land resources system. The impact of the proportion of construction land is bidirectional and the per land pesticide application has negative impact in general.

Regional differences in health screening participation between before and during COVID-19 pandemic

Health screening is a preventive and cost-effective public health strategy for early detection of diseases. However, the COVID-19 pandemic has decreased health screening participation. The aim of this study was to examine regional differences in health screening participation between before and during COVID-19 pandemic and vulnerabilities of health screening participation in the regional context. Administrative data from 229 districts consisting of 16 provinces in South Korea and health screening participation rate of each district collected in 2019 and 2020 were included in the study. Data were then analyzed via descriptive statistics and geographically weighted regression (GWR). This study revealed that health screening participation rates decreased in all districts during COVID-19. Regional vulnerabilities contributing to a further reduction in health screening participation rate included COVID-19 concerns, the population of those aged 65+ years and the disabled, lower education level, lower access to healthcare, and the prevalence of chronic disease. GWR analysis showed that different vulnerable factors had different degrees of influence on differences in health screening participation rate. These findings could enhance our understanding of decreased health screening participation due to COVID-19 and suggest that regional vulnerabilities should be considered stringent public health strategies after COVID-19.

Driving Factors of CO2 Concentration in Mainland China Based on GWR

Considering that the complexity and dynamicity of CO2 emissions, the spatiotemporal distribution pattern of atmospheric CO2 and its drivers remain unclear. In this study, we used the Geographically Weighted Regression (GWR) method to analyze the comprehensive distribution of CO2 concentration in mainland China from 2015 to 2019. We considered the relationship between nine factors, including natural and human activities, and CO2 concentration. To clarify the correlation between CO2 concentration and drivers, we utilized Pearson’s correlation coefficient. Then, the GWR analysis revealed the spatial heterogeneity across provinces, which reflects the extent to which impact factors influence CO2 concentrations. Finally, we analysed CO2 concentration spatiotemporal variation characteristics and predicted future trends of CO2 concentration in 31 provinces in China. According to our research, GDP has a major impact on CO2 growth, while natural factors have a minor influence on CO2 concentration. Our study found significant regional differences in the effects of combined variables on CO2 concentrations, with monthly rotational patterns temporally and clustering of high growth rates spatially and CO2 concentration in mainland China will continue to steadily increase. The findings of this research are crucial for China’s future energy low-carbon transition and policy-making.

Spatio-Temporal Spread of Covid-19 in Children Aged 0-11 Years at Central Java Province Indonesia Using Geographical Weight Regression

During the COVID-19 pandemic, children under the age of 12 are the most vulnerable age group to health concerns. The goal of this study was to conduct a spatiotemporal analysis of the distribution of COVID-19 cases in Central Java children using the GWR (Geographically Weighted Regression) approach. The data source is the Central Java Provincial Health Office, and the study objects are 35 cities and districts in Central Java province. The data obtained are the number of COVID-19 cases in children aged 0-11 years, the total number of Covid-19 cases, the number of PCR tests per day, the number of vaccinations and the number of health care facilities per city and district per month from March 2020 to November 2021. Hotspot analysis and the GWR approach were used to examine data in semesters 1–4 (S1–S4). From S1 to S4, the number of COVID-19 cases in children increased. Areas that became hotspots for more than two semesters were Semarang City, Semarang Regency, Banyumas, Cilacap, Kendal, and Demak. According to the GWR analysis in S1-S4, the total number of COVID-19 cases, PCR tests per day, vaccinations, and health care facilities all affect the number of COVID-19 patients in children by more than 75%. The total number of COVID-19 cases has a significant impact on the number of COVID-19 cases in children but the number of health care facilities has no effect. The results of the GWR prediction of COVID-19 cases in children show that the cities of Semarang and Banyumas became areas with a larger number of COVID-19 cases in two semesters. According to the hotspot and GWR analysis, the cities of Semarang and Banyumas are regions to be on the lookout for in the spread of COVID-19 cases in S1-S4.

낙뢰 분포와 배전용 변압기 뇌해고장의 영향에 관한 지리가중회귀분석

Distribution transformers are known to be affected by not only direct lightning strikes but also induced lightning induced overvoltage due to their relatively low insulation strength to withstand lightning strikes. In this regard, the purpose of this paper is to implement an explanatory model between the lightning strikes and the lightning failures of distribution transformers. Since spatial distributions are hard to be analyzed by the ordinary least square (OLS) method, geographically weighted regression (GWR) analysis was applied to find the explanatory properties between the spatial lightning strikes and the spatial lightning failure transformers. As a result of the regression analysis, GWR analysis shows improved explanatory power compared to the OLS method and enables additional analysis to be performed by specifying points showing high explanatory power and regression coefficients in the visual analysis.

Socio-Economic and Demographic Factors Associated with the Spatial Distribution of COVID-19 in Africa.

Corona is a disease that affects the whole world. Countries with weak economies are specifically more vulnerable. A proper understanding of COVID-19 spreading, identifying the high-risk areas, and discovering factors influencing the spread of the disease are crucial to improving disease control. This study evaluates the geo-statistical distribution of COVID-19 to identify critical areas of Africa using spatial clustering pattern analysis. In addition, the spatial correlation between infected cases and variables such as the unemployment rate, gross domestic product (GDP), population, and vaccination rate is calculated using Geographically Weighted Regression (GWR) analysis. The hot-spot map showed a statistically significant cluster of high values in southern and northern Africa. Moreover, the outcome of the GWR analysis revealed the GDP and population had the most significant correlation with the spreading of COVID-19, with Local R2 values of (0.01-0.99) and (0-0.89), respectively.