Local Indicators Of Spatial Association Research Articles

Spatiotemporal Patterns and Socioeconomic Influences on Host Participation in Short-Term Rental Markets: Airbnb in San Francisco

This paper examines spatiotemporal patterns and socioeconomic influences on host participation in Airbnb’s short-term rental (STR) marketplace in San Francisco during the years 2019–2022, a four-year period that spans the COVID-19 pandemic. This provides the motivation for the study to examine how San Francisco’s demographic and socioeconomic fluctuations influenced Airbnb hosts to rent their properties on the platform. To do so, Airbnb property densities, indicators of host participation, are estimated at the census tract level and subsequently mapped in a GIS along with points of interest (POIs) located all over the city. Mapping unveils spatiotemporal patterns and changes in Airbnb property densities, which are also analyzed for spatial autocorrelation using Moran’s I. Clusters and outliers of property densities are identified using K-means clustering and geostatistical methods such as local indicators of spatial association (LISA) analysis. Locationally, San Francisco’s Airbnb hotspots are not located in the city’s core, unlike other major Airbnb markets in metropolitan areas. Instead, such hotspots are in the city’s northeastern neighborhoods around ethnic enclaves, in close proximity to POIs that are frequented by visitors, and have a higher proportion of hotel and lodging employment and lower median household income. A conceptual model posits associations of Airbnb property densities with sixteen demographic, socioeconomic factors, indicators of trust, social capital, and sustainability, along with proximity to points of interest. Ordinary least squares (OLS) regressions reveal that occupation in professional, scientific, and technical services, hotel and lodging employment, proximity to POIs, and proportion of Asian population are the dominant factors influencing host participation in San Francisco’s shared accommodation economy. The occupational influences are novel findings for San Francisco. These influences vary somewhat for two main types of properties—entire home/apartment and private rooms. Implications of these findings are discussed in relation to supply side motivations of Airbnb hosts to participate in San Francisco’s STR marketplace.

Analysis of spatiotemporal transit accessibility and transit inequity of essential services in low-density cities, a case study of Nashville, TN

Evaluating the accessibility and equity of transit services to essential services that fulfill people’s fundamental needs is critical, especially in lower-density areas with limited transit services. Although numerous studies have analyzed the inequity of transit accessibility in dense, urban areas with robust transit networks, few of them evaluated transit inequity across multiple essential service categories (e.g., food stores, healthcare facilities, schools, etc.). This paper fills this research gap by focusing on a low-density, automobile-oriented city (Nashville, TN) to evaluate spatial transit inequities across essential service deserts and sociodemographic groups. The results of the case study show that many areas with higher transit-dependent demand (i.e., populations with limited personal vehicles, those living in poverty, and minority populations) tend to experience more restricted transit access to food, healthcare, school, and childcare facilities; interestingly, this is not the case for transit access to green space (specifically, parks). This study further identified essential service deserts by Local Indicator of Spatial Association (LISA) clustering and analyzed the correlation among them. The correlation results suggest that food deserts, healthcare deserts, school deserts, and childcare deserts are highly correlated with each other, while they have a lower correlation with park deserts. The findings of spatial transit inequity and the identification of essential service deserts provide crucial policy implications that can be used by transit agencies and local planning departments to prioritize their resources and target interventions to improve transit equity. This study contributes to the existing literature by proposing a systematic process that utilizes publicly available datasets to measure spatiotemporal transit supply, identify essential service deserts, and explore the correlation among these areas in a low-density city.

Community Resilience in Accessing Essential Service Facilities Considering Equity and Aging Demand: A Case of Shanghai, China

The COVID-19 lockdown has deepened inequity among vulnerable groups, such as the elderly. Reducing inequity in access to essential service facilities is an effective way to improve community resilience in dealing with pandemics. In this research, three indexes were created to measure community resilience in accessing essential services. Specifically, we have considered the different walking capacity and different needs of the elderly and the general population. We selected Shanghai as the case for our research and analyzed the spatial patterns of both space-based and population-based essential service facilities. The Lorenz curve and the Gini coefficient were used to measure the spatial equity. And, we attempted to reveal the relationships between the population density and three indexes through bivariate Local Indicators of Spatial Association. The results suggest that the Diversity Index enjoys the highest equity, followed by the Demand Accessibility Index, and the equity of the Per Capita Quantity Index is the lowest. Furthermore, the accessibility of essential services in urban areas is excellent, while in some suburban areas it is low. Our findings contribute valuable scientific insights for policy makers to strengthen community resilience and address inequities for immediate or long-term measures.

Spatial correlation of dengue with socioeconomic status and land temperature in northwest Mexico

Objective. To characterize the geographic distribution of dengue and to evaluate the spatial autocorrelation with social and climatic determinants at the census-tract level in two medium sized cities in northwestern Mexico. Methods. In this work we apply spatial analysis ecological tools, such as the Moran’s Index and the Local Indicator of Spatial Association (LISA) method, to examine global and local spatial correlation between incidence of dengue, and socioeconomic and climatic factors at the census tract-level. For the analysis of the spatial clustering, the Getis-Ord method was used to find statistically significant hot spots in each city. Results. Overall, a global spatial autocorrelation could not be identified, although local clusters of a high dengue incidence, soil surface temperature ≤ 31 °C and high degree of social marginalization coincide. Discussion. We found that at the census-tract level in urban settings, socially disadvantaged populations showed higher clusters of dengue when compared to areas with better socioeconomic conditions. In the two study sites, a similar spatial pattern was observed when considering public health conditions and its aggregation with physical attributes using spatial analysis techniques, supporting the application of this technique for a better understanding about the dengue distribution in urban areas.

A Geospatial Analysis of Social and Structural Determinants of Health and High HIV Prevalence in Alabama, USA.

This study utilizes geospatial analytic techniques to examine HIV hotspots in Alabama leveraging Medicaid utilization data. This cross-sectional study leveraged Medicaid utilization data from Alabama's 67 counties, averaging 9,861 Medicaid recipients aged > 18 years old per county. We used Alabama Medicaid administrative claims data from January 1, 2016, to December 31, 2020, to identify individuals with HIV. Using Microsoft SQL Server, we obtained the average annual count of HIV Medicaid claims in each of the 67 Alabama counties (numerator) and the number of adult Medicaid recipients in each county (denominator), and standardized with a multiplier of 100,000. We also examined several other area-level summary variables (e.g., non-high school completion, income greater than four times the federal poverty level, social associations, urbanicity/rurality) as social and structural determinants of health. County-boundary choropleth maps were created representing the geographic distribution of HIV rates per 100,000 adult Medicaid recipients in Alabama. Leveraging ESRI ArcGIS and local indicators of spatial association (LISA), results were examined using local Moran's I to identify geographic hotspots. Eleven counties had HIV rates higher than 100 per 100,000. Three were hotspots. Being an HIV hotspot was significantly associated with relatively low educational attainment and less severe poverty than other areas in the state. Findings suggesting that the HIV clusters in Alabama were categorized by significantly less severe poverty and lower educational attainment can aid ongoing efforts to strategically target resources and end the HIV epidemic in U.S.' Deep South.

Spatial variation and clustering of anaemia prevalence in school-aged children in Western Kenya.

Anaemia surveillance has overlooked school-aged children (SAC), hence information on this age group is scarce. This study examined the spatial variation of anaemia prevalence among SAC (5-14 years) in western Kenya, a region associated with high malaria infection rates. A total of 8051 SAC were examined from 82 schools across eight counties in Western Kenya in February 2022. Haemoglobin (Hb) concentrations were assessed at the school and village level and anaemia defined as Hb<11.5g/dl for age 5-11yrs and Hb <12.0g/dl for 12-14yrs after adjusting for altitude. Moran's I analysis was used to measure spatial autocorrelation, and local clusters of anaemia were mapped using spatial scan statistics and local indices of spatial association (LISA). The prevalence of anaemia among SAC was 27.8%. The spatial variation of anaemia was non-random, with Global Moran's I 0.2 (p-value < 0.002). Two significant anaemia cluster windows were identified: Cluster 1 (LLR = 38.9, RR = 1.4, prevalence = 32.0%) and cluster 2 (LLR = 23.6, RR = 1.6, prevalence = 45.5%) at schools and cluster 1 (LLR = 41.3, RR = 1.4, prevalence = 33.3%) and cluster 2 (LLR = 24.5, RR = 1.6, prevalence = 36.8%) at villages. Additionally, LISA analysis identified ten school catchments as anaemia hotspots corresponding geographically to SatScan clusters. Anaemia in the SAC is a public health problem in the Western region of Kenya with some localised areas presenting greater risk relative to others. Increasing coverage of interventions, geographically targeting the prevention of anaemia in the SAC, including malaria, is required to alleviate the burden among children attending school in Western Kenya.

Geochemical prospectivity mapping using compositional balance analysis and multifractal modeling: A case study in the Jinshuikou area, Qinghai, China

To effectively map the prospecting targets related to deposit-type sought, local indicators of spatial association (LISA), compositional balance analysis (CoBA), and multifractal modeling were applied to process stream sediment geochemical data that are collected from the Jinshuikou area, Qinghai province, China. In this study, 14 elements, including Zn, Cu, Ni, Mo, Co, V, Ti, Sb, Ag, Pb, Sn, Hg, Cd, and W, were analyzed in 493 samples. The result of LISA shows that the ore-forming elements Zn and Cu have a close spatial autocorrelation, which supports the construction of balance b1 related to the Zn-Cu mineralization assemblage. A traditional contrast additive method was applied to compare with balance b1 for geochemical anomaly identification. The results clearly indicate that balance b1 significantly assisted with not only reduction of geochemical data internal imbalance caused by extreme value, but also intensification of Zn-Cu assemblage geochemical anomalies associated with Zn polymetallic mineralization. The high anomalies of the two balances, b5 (Co-V-Ti) and b8 (Sn-Pb-Ag-Sb), reflect well the distribution of basic and intermediate-acid rocks in the area, respectively. The geochemical distribution pattern of the balance b1 can be depicted by multifractal parameters such as singular index α, as well as asymmetry index R, which are useful for describing the spatial heterogeneity of the balance b1. The singularity indices of balance b1 were classified by the concentration-area (C-A) fractal model to produce the high potential areas of the Zn-Cu assemblage. The high potential areas were obviously correlated with all Zn polymetallic occurrences. So, combined with the metallogenic geological characteristics of the study area, five prospective prediction targets were mapped. The anomaly verification demonstrates that the Zn concentration has reached the lowest industrial grade and cut-off grade corresponding to the target area I-1 and I-2, respectively, and Cu also has certain metallogenic potential. There is good potential for Zn-Cu mineralization as represented by this integrated method.

Geospatial analysis of the hospitalisation rate of patients with rheumatoid arthritis in Hunan: a cross-sectional Chinese study

ObjectiveLittle is known about spatial variability of hospitalisation rate (HR) of patients with rheumatoid arthritis (RA) worldwide, especially in China.MethodsA cross-sectional study was conducted among patients with RA admitted to...

The impact of urbanization on urban flood risk of Nakhon Ratchasima, Thailand

Urban flooding is a hazardous consequence of urbanization, climate change, and spatial factors. It occurs when people gather in specific areas, disrupting natural and built environments. Despite its importance, the manner in which urbanization transforms land use patterns and overall land topography, contributing to the uncertainty of urban flooding, remains unclear. This study presents a comprehensive spatiotemporal assessment of the impact of urbanization on urban flood risk based on the Urban Flood Risk Triangle Index (UFRTI) of Nakhon Ratchasima, Thailand, in 2000, 2010, and 2020. The study developed this assessment framework through spatial analysis using Bivariate Local Indicators of Spatial Association and K-means clustering analysis. The revised parameters for urban flood hazard, exposure, and vulnerability constitute the UFRTI, illustrating how urbanization influences the degree and characteristics of urban flood risk. This study highlights the potential of using BiLISA and K-means clustering in GIS-based spatial analysis that reveals flood risk characteristics are shifting towards the city's periphery, requiring land use regulations and efficient flood risk management to confront urban growth.

Undernutrition among children and its determinants across the parliamentary constituencies of India: a geospatial analysis.

In India, undernutrition among children has been extremely critical for the last few decades. Most analyses of undernutrition among Indian children have used the administrative boundaries of a state or a district level as a unit of analysis. This paper departs from such a practice and focuses instead on the political boundaries of a parliamentary constituency (PC) as the unit of analysis. The PC is a critical geopolitical unit where political parties and party candidates make election promises and implement programmes to improve the socio-economic condition of their electorate. A focus on child undernutrition at this level has the potential for greater policy and political traction and could lead to a paradigm shift in the strategy to tackle the problem by creating a demand for political accountability. Different dimensions and new approaches are also required to evaluate the socio-economic status and generate concrete evidence to find solutions to the problem. Given the significance of advanced analytical methods and models embedded into geographic information system (GIS), the current study, for the first time, uses GIS tools and techniques at the PC level, conducting in-depth analysis of undernutrition and its predictors. Hence, this paper examines the spatial heterogeneity in undernutrition across PCs by using geospatial techniques such as univariate and bivariate local indicator of spatial association and spatial regression models. The analysis highlights the high-low burden areas in terms of local hotspots and identifies the potential spatial risk factors of undernutrition across the constituencies. Striking variations in the prevalence of undernutrition across the constituencies were observed. Most of these constituencies that performed poorly both in terms of child nutrition and socio-economic indicators were located in the northern, western, and eastern parts of India. A statistically significant association of biological, socio-economic, and environmental factors such as women's body mass index, anaemia in children, poverty, household sanitation facilities, and institutional births was established. The results highlight the need to bring in a mechanism of political accountability that directly connects elected representatives to maternal and child health outcomes. The spatial variability and pattern of undernutrition indicators and their correlates indicate that priority setting in research may also be greatly influenced by the neighbourhood association.

Relative spatial variability in building heights and its spatial association: Application for the spatial clustering of harmonious and inharmonious building heights in Tokyo

Whether or not a streetscape skeleton – the 3D spaces of streets defined by the arrangement of surrounding buildings – is vertically harmonious depends to a large extent on the degree of difference between the heights of buildings adjacent to one another, known as the relative spatial variability in building heights. Surprisingly, this subject has been overlooked in previous studies examining the harmony of streetscapes. Data on precise building heights are indispensable for evaluating the relative spatial variability in building heights and its spatial association. The recent relaxation of data limitations on precise building heights in Tokyo enabled us to identify the relative spatial variability in building heights and quantify its spatial association. Therefore, in this paper we aim to answer the following question: where are harmonious or inharmonious building heights locally clustered? To this end, we computed the spatial association of the relative spatial variability in building heights as a set of edges whose indices enabled us to evaluate the local indicator of spatial association (LISA). Subsequently, we statistically demarcated locally harmonious and inharmonious building heights without having to set predetermined basic spatial units. In this respect, our methods and findings are novel and can contribute to establishing a new method for measuring the variability in vertical streetscape skeletons, which is important for developing urban design policies.

Contamination and source-specific health risk assessment of polycyclic aromatic hydrocarbons in soil from a mega iron and steel site in China

The iron and steel industry has always been a key and difficult point of environmental pollution control. In the present study, 493, 175, 153, 72, and 42 soil samples were collected from the soil depths of 0–0.5, 0.5–2, 2–3, 3–4, and 4–5 m (herein called the layers) of the Shougang Steel site, respectively. Compared with the evaluation criteria, the Shougang Steel surface soil was severely polluted by polycyclic aromatic hydrocarbons (PAHs). Inverse distance-weighted interpolation and the Kruskal-Wallis H test revealed that the soil PAH pollution in the iron-making area, especially the coking area, was severer than those in other areas. The PAH concentrations first decreased, and then, increased with the increase of depth. With the increase in depth, the contributions of 2- and 3-ring PAHs increased, while those of 4-, 5-, and 6-ring PAHs decreased. The bivariate local indicators of spatial association (LISA) analysis was used to identify the areas prone to soil PAH pollution due to atmospheric deposition of industrial waste gas and traffic emissions. The method could be used to analyze the impact of anthropogenic activities on soil's PAH pollution for other contaminated sites. Three main pollution sources of soil PAHs, the backfill source, the combustion of coal, and the traffic emissions, were identified based upon three diagnostic ratios, positive matrix factorization and the bivariate LISA analysis, and accounted for 53.8%, 23.5%, and 22.7%, respectively. The combination of bivariate LISA analysis and other source analysis methods could improve the accuracy of source analysis. Benzo[a]pyrene contributed the most to the total health risk among sixteen PAHs. The health risks related to the three pollution sources decreased in the order of backfill sources > coal combustion > traffic emissions. The incremental life-time carcinogenic risks were all below 10−4, indicating negligible or acceptable risks.

Evaluation of the Resilience of the Catering Industry in Hong Kong before and after the COVID-19 Outbreak Based on Point-of-Interest Data

COVID-19 has caused a serious economic shock which challenges the resilience of businesses around the world. Understanding the spatial distribution pattern of business resilience, as well as identifying factors that promote business resilience, is crucial to economic recovery. Most existing studies mainly analyze one side of the concept of resilience, such as how businesses closed, expanded, and innovated, while no studies take all the characteristics of resilience into account and analyze them from a geographical view. To fill this gap, this study first relates the method of calculating stability in ecology to geography, and proposes a point of interest (POI)-based index to evaluate an industry’s resilience in a city. Then, with the catering industry in Hong Kong as an example, the spatial distribution of resilience in June 2020 and December 2020 is investigated using the local indicators of spatial association (LISA) approach. An ordinary least squares (OLS) regression model is adopted to identify impactful factors on resilience. The results reveal that the resilience of restaurants is quite stable in local central areas, but areas near the checking points at Shenzhen in mainland China are severely affected. Most traditional location factors had the benefit of stabilization, while hospitals had negative responses. The presented analysis framework is possible to be easily generalized to other industries or cities. The overall result of the study provides a spatial understanding which would be essential as a reference for future urban planning regarding post-pandemic recovery.

Exploring the spatial pattern of animal bites in Iran (2021–2022)

ObjectiveKnowledge of the spatial pattern of animal bites can be helpful for targeted resource allocation and to develop and deliver effective intervention programs. The aim of this study was to explore the spatial pattern of the animal bites in Iran during 2021–2022. MethodsAnimal bite cases from all provinces and counties in Iran were obtained from a nationally based registry. Global Moran's I was applied to check spatial autocorrelation. The spatially adjusted standardized incidence ratios (SIRs) were estimated using the Besag, York, and Mollie (BYM) model. Spatial clusters were identified by local indicators of spatial association (LISA) and purely spatial scan statistic. ResultsA total of 260,470 animal bites were registered during the study period. There was a positive spatial autocorrelation (global Moran's I=0.27, p-value=0.001). The majority of SIRs greater than 1.00 was found in counties in the northern belt of Iran (e.g., observed greater than expected animal bites). LISA found that approximately 7% of counties in the north and northeast, 18% in the west and south, and 3% in the central part of Iran were significant hot spots, cold spots, and spatial outliers (p-value≤0.05). Spatial scan statistic detected primary hot spot cluster in the counties in the Mazandaran and Alborz provinces (Relative Risk=2.56, p-value<0.001), while primary cold spot cluster involved counties in Kurdistan and Kermanshah province (0.37, <0.001). ConclusionAnimal bites were unevenly distributed in Iran. Further prevention and control programs as well as appropriately resource allocation are needed in order to reduce the observed animal bites spatial disparity.

A web-based analytical framework for the detection and visualization space-time clusters of COVID-19

ABSTRACT The COVID-19 pandemic has had a profound impact worldwide and continues to spread due to various mutations of the virus. Many governmental and nonprofit agencies at different levels have quickly developed COVID-19 dashboards to disseminate information on the pandemic to the public. However, most of these systems have mainly distributed “plain” information (e.g. cases, death counts, vaccination), and rarely provided insights that can be gained from spatiotemporal analyses, such as the detection of emerging clusters. The results from these analyses hold tremendous potential for health policymakers as they try to identify ways to slow down transmission. We present a web-based geographic framework to detect and visualize space-time clusters of COVID-19. Our tightly coupled framework integrates the prospective space-time scan statistics and local indicators of spatial association (LISA) with novel 2D and 3D interactive visuals in a cyber environment (http://159.223.164.41/app/). We illustrate the applicability of our approach using COVID-19 data for the continental US. Our framework is portable to other regions that may experience infectious diseases but is also flexible to handle data of different spatial and temporal granularities. This paper fits within an effort to integrate space-time analytics for the monitoring of infectious diseases in web environment, ultimately improving health surveillance systems.

The Impact of Gender Inequality on GDP in EU Countries

Abstract In recent years, gender inequality has been considered the main characteristic of insufficient gross domestic product (GDP) growth. This paper discusses the evolution of GDP per capita in 21 countries of the European Union between 2015 and 2019. Using panel regression, we investigated the change in GDP per capita through five variables. The analysis results showed that female employment rate is the most statistically significant and positive variable on GDP. Gender Equality Index also appeared to be an essential variable. The second part of our analysis consisted of an explanatory spatial data analysis of all variables to examine the spatial dimension of the variables. To explain spatial econometrics, we used selected methods, namely, choropleth maps, Local Indicators of Spatial Association (LISA) cluster analysis, Moran‘s scatter plots, and Moran‘s I statistics. Based on the visualization of choropleth maps, GDP per capita did not change during the observed period, even though the values of the explanatory variables changed. For GDP per capita, the same applies in the case of LISA cluster analysis. At the end of the monitored period, the countries were included in the same cluster as at the beginning. When plotting Moran‘s scatter plot, it was found that GDP per capita did not tend to have positive or negative spatial autocorrelation or no spatial autocorrelation. Moran‘s I statistic showed that GDP per capita values were not randomly dispersed; they were grouped according to a specific formula into clusters.

The spatial analysis of green economy indicators of OECD countries

The present study aims to examine the effect of the geographical location relationship between economic growth and environmental pollution. For this purpose, the spatial relationship between the variable CO2 emission and the variables energy consumption (ENC), real GDP per capita (GDP), urbanization rate (URB), and trade liberalization (DAE) was investigated by using the data of 37 OECD countries for the period of 1990–2015. The geographical location relationship was determined by using LISA (Local Indicators of Spatial Association) analysis, which is one of the spatial autocorrelation analysis methods. Spatial distribution maps were prepared. Considering the years determined according to Moran I index results, a gradually increasing positive autocorrelation was found for CO2 and ENC variables and a low increasing positive correlation for DAE and GDP variables. For the variable URB, a low increasing positive autocorrelation was found for the year 1990 and a high increasing positive autocorrelation for the year 2015. Then, using the LISA clustering maps, the relationships between the countries were clustered as low, high, and non-related. As a result of this study, given the spatial analysis results, the effect of energy consumption on the carbon emission was found to be positive in general. Increases in trade liberalization increased carbon emissions in some countries and decreased it in some others. On the other hand, increases in the urbanization rate decreased carbon emissions in some countries and had a positive effect in some others. The trade openness index was found to have a generally negative effect on the carbon emission. Within the scope of this study, Spatial Regression Analysis was conducted separately for the years 1990 and 2015. In this analysis, CO2 is the dependent variable, whereas ENC, GDP, URB, and DAE are independent variables. Given the results of spatial regression analysis, it was found that ENC, GDP, and DAE variables have a positive relationship with the CO2 variable. It was determined that there was no significant relationship between URB and CO2. Considering the results achieved, it could be possible to observe the increasing and decreasing effects of variables, which were examined here, on the CO2 emissions.

An investigation of geographical clusters of leptospirosis during the outbreak in Pangandaran, West Java, Indonesia.

Leptospirosis is neglected in many tropical developing countries, including Indonesia. Our research on this zoonotic disease aimed to investigate epidemiological features and spatial clustering of recent leptospirosis outbreaks in Pangandaran, West Java. The study analysed data on leptospirosis notifications between September 2022 and May 2023. Global Moran I and local indicator for spatial association (LISA) were applied. Comparative analysis was performed to characterise the identified hotspots of leptospirosis relative to its neighbourhoods. A total of 172 reported leptospirosis in 40 villages from 9 sub-districts in Pangandaran District were analysed. Of these, 132 cases (76.7%) were male. The median age was 49 years (interquartile range [IQR]: 34-59 years). Severe outcomes including renal failure, lung failure, and hepatic necrosis were reported in up to 5% of the cases. A total of 30 patients died, resulting in the case fatality rate (CFR) of 17.4%. Moran's I analysis showed significant spatial autocorrelation (I=0.293; p=0.002) and LISA results identified 7 High-High clusters (hotspots) in the Southwest, with the total population at risk at 26,184 people. The hotspots had more cases among older individuals (median age: 51, IQR: 36-61 years; p<0.001), more farmers (79%, p=0.001) and more evidence of the presence of rats (p=0.02). A comprehensive One Health intervention should be targeted towards these high-risk areas to control the transmission of leptospirosis. More empirical evidence is needed to understand the role of climate, animals and sociodemographic characteristics on the transmission of leptospirosis in the area studied.

Spatial association between socio-economic health service factors and sepsis mortality in Thailand.

Sepsis is a significant global health issue causing organ failure and high mortality. The number of sepsis cases has recently increased in Thailand making it crucial to comprehend the factors behind these infections. This study focuses on exploring the spatial autocorrelation between socio-economic factors and health service factors on the one hand and sepsis mortality on the other. We applied global Moran's I, local indicators of spatial association (LISA) and spatial regression to examine the relationship between these variables. Based on univariate Moran's I scatter plots, sepsis mortality in all 77 provinces in Thailand were shown to exhibit a positive spatial autocorrelation that reached a significant value (0.311). The hotspots/ high-high (HH) clusters of sepsis mortality were mostly located in the central region of the country, while the coldspots/low-low (LL) clusters were observed in the north-eastern region. Bivariate Moran's I indicated a spatial autocorrelation between various factors and sepsis mortality, while the LISA analysis revealed 7 HH clusters and 5 LL clusters associated with population density. Additionally, there were 6 HH and 4 LL clusters in areas with the lowest average temperature, 4 HH and 2 LL clusters in areas with the highest average temperature, 8 HH and 5 LL clusters associated with night-time light and 6 HH and 5 LL clusters associated with pharmacy density. The spatial regression models conducted in this study determined that the spatial error model (SEM) provided the best fit, while the parameter estimation results revealed that several factors, including population density, average lowest and highest temperature, night-time light and pharmacy density, were positively correlated with sepsis mortality. The coefficient of determination (R2) indicated that the SEM model explained 56.4% of the variation in sepsis mortality. Furthermore, based on the Akaike Information Index (AIC), the SEM model slightly outperformed the spatial lag model (SLM) with an AIC value of 518.1 compared to 520.

Spatial Association and Modeling of Infant Mortality in Thailand, 2020

Infant mortality remains a pressing public health challenge globally. Despite advancements in healthcare, glaring disparities persist, as exemplified in Thailand. This study explored spatial variations in infant mortality rates (IMRs) across Thai provinces, integrating socio-economic, demographic, and health factors. Using data from national databases, we employed univariate and bivariate Local Indicators of Spatial Association (LISA) analyses to visualize spatial disparities, and Moran's I statistic assessed global spatial autocorrelation. Spatial regression models, including Ordinary Least Squares (OLS), Spatial Lag Model (SLM), and Spatial Error Model (SEM), analyzed the associations between IMRs and determinants. Our findings revealed stark IMRs disparities, especially in provinces like Phitsanulok, Narathiwat, and Songkhla. The SEM emerged as the most fitting model, given the data's spatial autocorrelation (R-Squared = 0.46). Crucial factors such as community organization strength, nighttime light, and exclusive breastfeeding were significantly linked to IMRs. Additionally, provinces like Phra Nakhon Si Ayutthaya and Rayong underscored socio-economic challenges, emphasizing the importance of tailored interventions. This study offers valuable insights for crafting targeted strategies, underscoring the pivotal role of geospatial techniques in shaping public health policies in Thailand.