Local Spatial Autocorrelation Research Articles

Socio-Spatial Analysis of Water Affordability at Small Scales: A Needs-Based Approach

Water affordability as a dimension of water poverty is becoming an increasing source of concern in cities of the Global North. Studies on water affordability are either based on water wants and not needs or tend to use spatial scales too large for effective analyses of local inequities that can truly guide policy actions. In this contribution, we calculate and map a Water Affordability Index (WAI) based on the minimum water requirement of 100 litres/person/day at the scale of the census tract for the Metropolitan Area of Barcelona. We also apply global and local spatial autocorrelation analyses to investigate spatial relationships between the WAI and poverty-related sociodemographic variables. Results show that, even though average WAI values are moderate, the distribution pattern of higher and lower values tends to be clustered in some districts and neighbourhoods of the study area. Bivariate correlations indicate that water affordability is not only related to poverty variables but also to the diversity of water prices. Findings exemplify how the constructed index can complement existing affordability indicators, revealing and mapping important risk groups struggling to meet the costs of essential water needs. Water affordability could be mitigated by supportive water pricing policies for vulnerable households in water poverty hotspots.

The Influence of Spatial Heterogeneity of Urban Green Space on Surface Temperature

Urban green space (UGS) has been recognized as a key factor in enhancing the urban ecosystem balance, particularly in arid areas. It is often considered an effective means to mitigate the urban heat island (UHI) effect. In this study, the reference comparison method was utilized to optimize the process of nighttime lighting data; the random forest classification method was employed to extract UGS data; and the radiative transfer method was applied in land surface temperature (LST) inversion. Additionally, moving window analysis was conducted to assess the robustness of the results. The objective of this research was to analyze the spatial distribution characteristics of UGS and LST and to explore their bivariate local spatial autocorrelations by calculating four landscape metrics, including the aggregation index (AI), edge density (ED), patch density (PD), and area-weighted mean shape index (Shape_am). It was found that the distribution of UGS in the study area was uneven, with higher temperatures in the eastern and western regions and lower temperatures in the central and southern regions. The results also revealed that ED, PD, and Shape_am were negatively correlated with LST, with correlation coefficients being −0.469, −0.388, and −0.411, respectively, indicating that UGS in these regions were more effective in terms of cooling effect. Conversely, AI was found to be positively correlated with LST (Moran’ I index of 0.449), indicating that surface temperatures were relatively higher in regions of high aggregation. In essence, the fragmented, complex, and evenly distributed green patches in the study area provided a better cooling effect. These findings should persuade decision makers and municipal planners to allocate more UGS in cities for UHI alleviation to improve quality of life and enhance recreational opportunities.

Uneven primary healthcare supply of rural doctors and medical equipment in remote China: community impact and the moderating effect of policy intervention

BackgroundUnequal access to primary healthcare (PHC) has become a critical issue in global health inequalities, requiring governments to implement policies tailored to communities’ needs and abilities. However, the place-based facility dimension of PHCs is oversimplified in current healthcare literature, and formulating the equity-oriented PHC spatial planning remains challenging without understanding the multiple impacts of community socio-spatial dynamics, particularly in remote areas. This study aims to push the boundary of PHC studies one step further by presenting a nuanced and dynamic understanding of the impact of community environments on the uneven primary healthcare supply.MethodsFocusing on Shuicheng, a remote rural area in southwestern China, multiple data are included in this village-based study, i.e., the facility-level healthcare statistics data (2016–2019), the statistical yearbooks, WorldPop, and Chinese GDP’s spatial distribution data. We evaluate villages’ PHC service capacity using the number of doctors and essential equipment per capita, which are the major components of China’s PHC delivery. The indicators describing community environments are selected based on extant literature and China’s planning paradigms, including town- and village-level factors. Gini coefficients and local spatial autocorrelation analysis are used to present the divergences of PHC capacity, and multilevel regression model and (heterogeneous) difference in difference model are used to examine the driving role of community environments and the dynamics under the policy intervention.ResultsDespite the general improvement, PHC inequalities remain significant in remote rural areas. The village’s location, aging, topography, ethnic autonomy, and economic conditions significantly influence village-level PHC capacity, while demographic characteristics and healthcare delivery at the town level are also important. Although it may improve the hardware setting in village clinics (coef. = 0.350), the recent equity-oriented policy attempts may accelerate the loss of rural doctors (coef. = − 0.517). Notably, the associations between PHC and community environments are affected inconsistently by this round of policy intervention. The town healthcare centers with higher inpatient service capacity (coef. = − 0.514) and more licensed doctors (coef. = − 0.587) and nurses (coef. = − 0.344) may indicate more detrimental policy effects that reduced the number of rural doctors, while the centers with more professional equipment (coef. = 0.504) and nurses (coef. = 0.184) are beneficial for the improvement of hardware setting in clinics.ConclusionsThe findings suggest that the PHC inequalities are increasingly a result of joint social, economic, and institutional forces in recent years, underlining the increased complexity of the PHC resource allocation mechanism. Therefore, we claim the necessity to incorporate a broader understanding of community orientation in PHC delivery, particularly the interdisciplinary knowledge of the spatial lens of community, to support its sustainable development. Our findings also provide timely policy insights for ongoing primary healthcare reform in China.

Child feces disposal practices in humanitarian and non-humanitarian settings across 34 low- and middle-income countries

Globally, safe sanitation has improved significantly in the last two decades, but unsafe child feces disposal remains a growing challenge in many regions, exposing household members and communities to infectious pathogens. The drivers associated with child feces disposal in several contexts including humanitarian settings are not well understood. This study investigated child feces disposal (CFD) practices and associated factors in low- and middle-income countries, including in humanitarian settings. Data from 352,173 women in 34 countries, collected between 2012 and 2021 through Demographic and Health Surveys (DHS), were used. We utilized multivariate logistic regression to assess CFD practices among children under two years old and the factors linked to these practices. We incorporated data from the United Nations High Commissioner for Refugees (UNHCR) regarding refugee camps' locations in the analysis. Time series and local spatial autocorrelation analyses were run to examine changes in safe CFD practices over time and space, respectively. Results showed minimal improvement in safe child feces disposal over the past decade, with 55.6 % of respondents in non-humanitarian settings and 38.1 % in humanitarian settings improperly disposing of feces. Improper CFD correlated with increased odds of diarrhea in non-humanitarian settings (OR 1.09 95 % CI: 1.05–1.13) but not in humanitarian settings (OR 1.14 95 % CI: 0.53–2.49). The most significant factors (p < 0.05) associated with safe CFD included being in the richest wealth quintile (OR 3.27 95 % CI: 3.06–3.49), having basic education (OR 1.28 95 % CI: 1.22–1.33), children eating solid food (OR 1.53 95 % CI: 1.48–1.57), improved sanitation access (OR 1.88 95 % CI: 1.81–1.96), and listening to radio at least weekly (OR 1.40 95 % CI: 135–1.46). Policymakers and development partners must include safe CFD guidelines in national policies and programs, as well as prioritize investments in household-level sanitation and educate caregivers about safe CFD practices.

Spatial-temporal variation and correlation between vegetation coverage and soil conservation of Ten Kongdui watershed in the Yellow River basin

The Ten Kongdui watershed is an essential part of the upper Yellow River baisn of China. The ecosystem in this region has low stability and hazards of sandstorm and soil erosion are serious. By studying the spatio-temporal changes and spatial correlation between vegetation cover and soil conservation, the inhibition effect of vegetation on soil erosion in time and space can be clarified, and the driving factors of soil erosion in a certain area can be further explained. The research results provide data support for artificial vegetation construction and soil erosion control in the Yellow River basin in the future. In this study, the invest model was used to calculate the soil retention of the watershed, and then the spatial autocorrelation analysis was used to analyze the vegetation coverage and soil retention in space. The relationship between changes of the vegetation coverage and soil conservation during 2000–2020 in the Ten Kongdui watershed is analyzed in this paper. The research shows that: (1) During the research period, the vegetation coverage maintained an increasing trend in Ten Kongdui watershed; (2) From 2000 to 2020, with the increased vegetation coverage, soil retention increased, indicating that increasing vegetation coverage can effectively prevent soil erosion; and (3) Vegetation coverage and soil conservation presented a local spatial auto-correlation, Moran’s I index was less than 0, but the years from 2000 to 2020 witnessed gradual growth of the correlation coefficient. There are significant differences in spatial distribution. ‘High-high’ and ‘low-low’ are sporadically distributed in the sandstorm area on the middle reaches of Kubuqi and sporadically in the hilly and gully areas on the upper reaches. Moreover, ‘high-low’ and ‘low-high’ correlation coefficients were distributed in gully areas on the upper reaches and alluvial plains on the lower reaches. The alluvial plains on the lower reaches are mainly farmland ecosystems, with a high NDVI in the growing season but poor soil erosion resistance and weak soil conservation ability. The results emphasized the importance of vegetation coverage change in space for soil conservation of different type of underlying surfaces. The vegetation coverage change effects of soil conservation in plain terrain will be muted. This work provided a certain theory and criterion for us to further research the predictive model of soil erosion .

Spatial Analysis of Pulmonary Tuberculosis Incidence in Kupang City in 2019-2021

Abstract Introduction: Pulmonary tuberculosis has a fairly high number of cases in several regions of Indonesia, including Kupang City. In 2019, tuberculosis cases in Kupang City amounted to 667 cases, in 2020 it increased to 693 cases and in 2021 it decreased to 419 cases. This study aims to analyze the factors that influence the incidence of pulmonary tuberculosis which are studied in a spatial analysis model.Methods : This research is a quantitative research with an analytical observational design using an approach to Geographic Information Systems (GIS). This study aims to see the autocorrelation of population density, poor families, and healthy homes with the incidence of pulmonary tuberculosis and to see the pattern of relationships that are formed.The data used in this research is secondary data taken from related agencies. The samples in this study were 11 community health centers in Kupang City, namely Naioni Health Center, Alak Health Center, Manutapen Health Center, Sikumana Health Center, Penfui Health Center, Bakunase Health Center, Oebobo Health Center, Oepoi Health Center, Pasir Panjang Health Center, Kupang City Health Center, and Oesapa Community Health Center. Spatial analysis uses computer programs, namely the QGIS and GeoDa applications. QGIS was used to produce a map of the distribution of pulmonary tuberculosis cases in 11 health centers in Kupang City in 2019-2021 and the GeoDa application was used to see the pattern of TB distribution in Kupang City in 2019-2021, in general or globally through the LISA (Local Indicators Spatial Autocorrelation) tests.Results: Based on the results of the LISA (Local Indicators Spatial Autocorrelation) bivariate test, it shows that population density, poor families, and healthy housing coverage have no relationship with the incidence of TB in Kupang City (p-value &gt; 0.05) and show a random pattern of case distribution (Morans Index ' I is smaller than E[I] = -0.1000). However, in 2019-2020, the Morans I Index value covering healthy homes was greater than E[I] = -0.1000, which shows a clustered pattern of case distribution.Conclusion: It was concluded that there was no positive spatial autocorrelation between population density, poor families, and healthy homes with the incidence of pulmonary tuberculosis in Kupang City in 2019-2021

Analyzing spatio-temporal changes and trade-offs/synergies of gross ecosystem product based on water-energy-food nexus.

The value of the ecosystem's ultimate goods and services for human welfare and long-term economic and social development is known as the gross ecosystem product (GEP). For the study of GEP accounting, the suggested water-energy-food (WEF) nexus offers a fresh viewpoint. This work aims to build a GEP accounting index system based on WEF, investigate its spatio-temporal evolution characteristics, and assess trade-offs and synergies between and within the water, energy, and food subsystems. Using the Three Gorges Reservoir area (TGRA) as an illustration, the findings revealed that, firstly, the comprehensive benefit of GEP based on WEF showed an upward trend in TGRA. Still, it was worth noting that the total production of the food ecosystem decreased. Secondly, the GEP based on WEF in five periods showed a spatial pattern of "high east and west, low middle." Thirdly, the Pearson correlation coefficient indicated that the GEP trade-off relationships based on WEF were dominant in TGRA, with the strongest trade-offs between AQV, SCV, APV, and LEV. In addition, in bivariate local spatial autocorrelation, the value of the six ecosystem service function relationships was dominated by the trade-off relationship, and the distribution of trade-offs and synergies showed significant heterogeneity at the county scale in the TGRA. Finally, hot spot analysis showed that the hot spots of the gross water and energy ecosystem products were scattered in the tail area of the study area. In contrast, the hot spots of the gross food ecosystem product were concentrated in the belly region. The findings of this study provided a basis for the scientific formulation of territorial spatial pattern optimization for water, energy, and agricultural resources in the TGRA and can more accurately reflect the status of the ecological environment and changes of WEF over time. Moreover, this paper also gives full play to the growth advantages of shipping and aquatic products, implements effective soil erosion prevention and control measures, and establishes water-saving mechanisms and other measures in terms of water resources. Subregional plans for industrial structure and strengthening of waste gas and wastewater treatment facilities regarding energy resources are developed. Implement the cultivated land protection system and promote the superiority of crop varieties and other measures in terms of food resources.

Spatial-temporal distribution characteristics of etiologically positive pulmonary tuberculosis in Jiangsu Province from 2011 to 2021

Objective: To analyze the spatial-temporal distribution of etiologically positive pulmonary tuberculosis (PTB) at the county (city, district) unit in Jiangsu Province from 2011 to 2021 to provide evidence for the implementation and adjustment of prevention and control strategies of PTB in Jiangsu Province. Methods: The registration data of etiologically positive PTB patients in Jiangsu Province from 2011 to 2021 were collected from the Tuberculosis Management Information System in the China Information System of Disease Control and Prevention. Data on the permanent population were from the statistical yearbook of each county (city, district) in Jiangsu Province. Geoda 1.18.0 software was used to analyze the global and local spatial autocorrelation and explore the spatial clustering. SaTScan 10.1 software was used to analyze the spatial-temporal clusters, and ArcGIS 10.7 software was used to visualize the spatial-temporal clusters. Results: A total of 128 240 etiological positive PTB cases were registered in Jiangsu Province from 2011 to 2021, with an average annual registration rate of 13.99/100 000. The registration rate showed an overall upward trend (trend χ2=63.49, P<0.001) after 2017, and the etiologically positive rate showed an overall upward trend (trend χ2=3 710.86, P<0.001). The annual Moran's I values ranged from 0.107 to 0.343, which showed a spatial clustering distribution. The results of local spatial autocorrelation analysis showed that there were "high-high" clustering areas in Jiangsu Province each year, showing a dynamic distribution, and most of the areas were distributed in the central and southern regions of Jiangsu Province, with the largest number (7) in 2015 and the smallest number (1) in 2011. A total of 4 spatial-temporal clustering areas were explored by spatial-temporal scanning analysis (all P<0.001), among which the first-level clustering area covered 3 counties (cities, districts), namely Changshu, Taicang, and Xiangcheng District of Suzhou, and the clustering time was from 2011 to 2015. The secondary clustering areas covered 24 counties (cities, districts), mainly covering Jiangsu's central and northern regions, such as Huai'an, Suqian, and Yancheng. The third-level clustering areas covered 26 counties (cities, districts); the fourth-level clustering area was the Gaochun District of Nanjing, with the clustering period from 2017 to 2021. Conclusions: From 2011 to 2021, the etiologically positive PTB registration rate at the county (city, district) level in Jiangsu Province had obvious spatial-temporal clustering characteristics. The clustering areas included the northern areas with relatively backward economies and the southern areas with better economic development. Multiple measures should be taken to prevent and control PTB according to the specific situation in different regions.

Abstract 4833: Impact of poverty in surrounding neighborhoods in cancer mortality in the US: A nationwide study among US women

Abstract Background: Poverty at the individual and county levels is known to lead to worse cancer outcomes. We believe the relationship between poverty and cancer may involve individual-level factors including tumor biology, socioeconomic status, and community measures that alter cancer outcomes such as death. We aim to understand to what extent poverty in the surrounding neighborhood may identify counties at the highest risk of cancer mortality and whether it is modifiable. Methods: This study used data collected by SEER to examine the association between neighborhood effects of poverty on the top three sites of cancer mortality among women. We included women &amp;gt;= 20 years of age diagnosed with breast, lung, and colorectal cancer between 2005-2009 and 2010-2014 who survived more than one year after diagnosis. We determined county-level neighborhood poverty environments using local spatial autocorrelation (LISA) analysis with Local Moran’s I of five-year county-level poverty estimates from the American Community Survey. US counties with poverty estimates above or below the national median were defined as high (H)- or low (L)- poverty counties, respectively. We defined neighborhood poverty environments as H-counties among H-counties (HH), L-counties among L-counties (LL), H-counties among LL-counties (HL), and L-counties among HH-counties (LH). We used a Poisson regression model adjusting for age at diagnosis, race/ethnicity, cancer stage, year of diagnosis, treatment, and cancer type to assess county-level neighborhood poverty environment effects on cancer mortality. We also used stratified analyses to explore the effect of cancer type, stage, and race/ethnicity strata on the association between neighborhood poverty environments and cancer death. Results: Our analysis includes 36,481 women with 4,740 cancer deaths. In total 7,182 women were in HH counties; 21,112 women were in LL counties; 6,476 women were in HL counties; and 1,711 women were in LH counties. Women in HH, LH, and HL counties had a respective 1.23 [95%CI:1.14,1.32], 1.20 [95%CI:1.05,1.36], and 0.95 [95%CI: 0.88,1.03] relative risk (RR) of cancer death when compared to deaths in LL counties. In our stratified analyses, women with breast cancer in HH and HL counties had higher cancer deaths (RR = 1.21 [95%CI:1.03,1.42] and 1.37 [95%CI:1.13,1.65], respectively) when compared to women with lung cancer. We also observed higher cancer deaths from counties that were LL in 2005-2009 and changed to HH counties in 2010-2014 (RR = 1.60 [95%CI:0.85,2.68]) when compared to unchanged LL counties. Conclusion: We used LISA spatial clustering to demonstrate the effects HH and LH environments have on cancer mortality. We also demonstrate the impact that changes in neighborhood environments can have on cancer mortality. Our findings suggest that prioritization of resource allocations to HH neighborhoods could improve the burden of women with cancer. Citation Format: Ifeoma Nwigwe, Michael Desjardins, Kala Visvanathan. Impact of poverty in surrounding neighborhoods in cancer mortality in the US: A nationwide study among US women [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4833.

Industrial fluoride emissions and their spatial characteristics in the Nansi Lake Basin, Eastern China.

Excessive fluoride emissions threaten ecological stability and human health. Previous studies have noted that industrial sources could be significant. However, quantifying industrial fluoride emissions has not been yet reported. In this study, both bottom-up and top-down approaches were used to estimate the fluoride emissions in the Nansi Lake Basin. Global and local spatial autocorrelation were adopted to reveal the spatial agglomeration effects. The fluoride emissions calculated by the bottom-up approach were larger than those calculated by the top-down method. The highest fluoride input mainly occurred in Zoucheng and Mudan. The highest fluoride emissions mainly occurred in Zoucheng and Rencheng using the bottom-up approach. The highest fluoride emissions mainly occurred in Zoucheng and Yanzhou using the top-down approach. Mining and washing of bituminous coal and anthracite (BAW) was the most significant source of fluoride input and emissions. A significant spatial agglomeration effect of fluoride emissions was found. These findings could provide a method for accurate industrial fluoride emission estimation, complement the critical data on the fluoride emissions of main industrial sectors, and provide a scientific basis for tracing fluoride sources.

Spatial Cluster Change of Schistosoma japonicum Transmission Foci in Indonesia During the Schistosomiasis Elimination Program.

The Government of Indonesia committed to eliminating schistosomiasis by 2025. Collaboratively snail control became one of the crucial strategies to ensure that the prevalence ofSchistosoma japonicuminOncomelania hupensis lindoensisreaches zero by the end of the program. This research investigated the spatial cluster change ofS. japonicumtransmission foci in Indonesia between 2017 and 2021. We mapped the snail foci, collected the snails, and calculated the snail density. We also conducted laboratory tests to detect the existence of cercariae in the snails. Identified infected snails were used to calculate the infection rate (IR) or snails' prevalence of schistosome cercariae among freshwater snails. We then analysed the spatial cluster using the Getis-Ord Gi* statistic to identify the hot and cold spots. The 5-year schistosomiasis elimination program successfully declined 18.84% of the snail foci and reduced 40.37% of the infected snail foci. Local spatial autocorrelation of snail density and infection rate identified that in 2017 and 2021, the number of cold spots decreased by 53.91% and 0%, while hot spots increased by 2.63% and 56.1%. The presence of more hotspots suggests a rise in the number of foci with high snail density and infection rates. The implementation of snail control was not optimal, and the parasite transmission through domestic animals still existed, causing the spatial cluster of hot spots to change during this period. Most hotspots have been observed near settlements, primarily in cocoa plantations, developed and deserted rice fields, grassland, and bush wetlands. During the schistosomiasis elimination program, the number of hot spots increased while cold spots decreased, and there were notable changes in the geographical distribution of hot spots, indicating a shift in the clustering pattern of schistosomiasis cases. The findings become essential for policymakers, particularly in selecting priority areas for intervention. In the Discussion section, we demonstrated the selection process based on the existence of hot and cold spots. Furthermore, we proposed that enhancing cross-sector integration is crucial, particularly in connection with the management of S. japonicum transmission through domestic animals.

Remediation Technologies of Contaminated Sites in China: Application and Spatial Clustering Characteristics

Screening remediation technologies through the lens of green, low-carbon, and sustainable development is crucial for contaminated land management. To better understand the applicability of remediation technologies, this paper explored their application in China based on a survey of 643 cases. By employing coupled analysis and local spatial autocorrelation methods, this study reveals the alignment between remediation technologies and pollutants, along with their spatial distribution and clustering patterns. Specifically, the four primary remediation technologies identified were cement kiln co-processing (CKCP), chemical oxidation/reduction (CO/CR), thermal desorption (TR), and solidification and stabilization (S/S), collectively accounting for over 90% of the cases. Additionally, our findings indicated significant variation in how different pollutants respond to remediation technologies, largely attributable to the characteristics of the pollutants. We observed High–High clustering patterns for CKCP, CO/CR, TR, and S/S. These were predominantly found in Jiangsu, Chongqing, Shandong, and Guizhou for CKCP and CO/CR and in Hebei, Jiangsu, Shanghai, and Chongqing for CO/CR. TR exhibited a High–High clustering in Shanghai, as did S/S. This research contributes to reducing the economic and resource costs associated with the trial-and-error of screening contaminated soil remediation technologies, offering valuable scientific and technological guidance for contaminated land regulation.

Influential Factors of Tuberculosis Notification Rates in Turkey: A Provincial-Level Spatial Analysis.

The total annual count of reported tuberculosis (TB) cases continues to decline throughout Turkey. Recognizing the regions with high and low burdens and revealing the factors affecting TB notification rates may play a role in guiding national control programs. This study aimed to analyze the spatial distribution of TB notification rates from 2005 to 2018 and evaluate the factors contributing to TB rates. In this ecological study, we used freely available open data from the Internet. We employed global and local spatial autocorrelation analysis to identify the spatial distribution and the clusters with low and high burdens. We conducted an ordinary least square regression model, spatial lag model, and spatial error model. The best-fitting model was selected via model parameters. Throughout the study period, the provinces in West Marmara Region (Edirne, Kırklareli, Tekirdağ, Çanakkale) were consistently in a high-burden cluster. In univariate ordinary least square regression, population density, the proportion of contacts screened for TB, the proportion of TB contacts who received prophylaxis, TB dispensary count, mean particulate matter 10 levels, and gross domestic product were found to be positively associated with TB notification rate. The best-fitting multivariate spatial lag model revealed that the proportion of contacts screened for TB (β, z-value: 0.89, 2.21) positively affected TB notification rate. The high TB burden in West Marmara Region should warn policymakers to maintain a focused approach to controlling TB in this area. This study showed the importance of contact tracing efforts to prevent the underdetection of TB cases.

Spatial Differentiation and Influencing Factors of Tertiary Industry in the Pearl River Delta Urban Agglomeration

The tertiary industry has become the main driving force for China’s economic development, and the adjustment and optimization of its structure are important prerequisites for achieving high-quality economic development. Existing studies have mostly focused on the spatial layout and influencing factors of the tertiary industry, with insufficient exploration of its internal structure. In this study, the PRD urban agglomeration is selected as the study area. On the basis of classifying the tertiary industry, the Dagum Gini coefficient, kernel density estimation, and local spatial autocorrelation are used to explore the spatial differentiation of various tertiary industries. The influencing factors are analyzed using geographical detectors, and suggestions for future development strategies are proposed. The results show that in terms of regional differentiation, the agglomeration of various tertiary industries in Guangzhou and Shenzhen is the most significant, but there is insufficient spillover to surrounding cities. In terms of development structure, the level of agglomeration of the consumptive tertiary industry is higher, the public tertiary industry tends to be more evenly distributed, and the productive tertiary industry is relatively dispersed. In terms of influencing factors, the interaction between population and employment dominates the spatial differentiation and evolution of the tertiary industry in the PRD urban agglomeration. Therefore, in the future, the tertiary industry in PRD urban agglomeration should promote the optimization of industrial structure and regional coordinated development under the guidance of the government.

Spatial correlation analysis between human disturbance intensity (HDI) and ecosystem services value (ESV) in the Chengdu-Chongqing urban agglomeration

The spatial correlation between human disturbances and ecosystem services carries profound implications, serving as a pivotal tool for quantifying the magnitude of human impacts on ecosystems and offering invaluable insights for the development of effective management and conservation strategies. This research endeavors to conduct an exhaustive analysis of the spatial patterns between human disturbance intensity and ecosystem services in the Chengdu-Chongqing urban agglomeration, as well as to explore the spatial associations between these two factors. In the paper, we conducted a comprehensive analysis of five key indicators of human pressure, namely population density, gross domestic product (GDP), land use type, transportation network and energy consumption. These indicators were carefully examined and utilized to create a spatial map depicting the integrated human disturbance intensity index in the Chengdu-Chongqing urban agglomeration area in the year 2020. Concurrently, we utilized the ESV equivalent factor method to estimate the ecosystem service value (ESV). Bivariate Local Moran Index and spatial autocorrelation analysis were introduced to quantitatively depict and visualize the spatial relationship between human disturbance and ecosystem services. This research provides valuable insights into the intricate patterns of human pressures and ecosystem services in urban agglomeration scale. The key findings are as follows. First, the integrated human disturbance intensity is relatively high in the hub parts of Chengdu, Chongqing, and Deyang, as well as the middle zone of the cities between Chongqing and Chengdu. Second, the high value of supply services is distributed in the periphery of the cities, while the value of supply services in the middle of the cities tends to be medium and low. The majority area in the middle of the entire study area has low and medium–low values of regulation and maintenance service. At the fringes, there are only a few cities with higher values, including Ya'an, Leshan, Deyang and the eastern part of Chongqing. The distribution of cultural service values is quite similar to the distribution of regulation and maintenance service values, with the low value areas being very large and located in the middle of the study area, while the high value areas are significantly smaller and scattered around the perimeter of the study area. Third, the global Moran's index of cumulative human disturbance is 0.798, indicating that human disturbance exhibits a significant spatial autocorrelation. The global Moran's index of ecosystem services is 0.410, indicating that ecosystem services present aggregation in space as well. Fourth, the Bivariate Moran′s I between human disturbance and supply services is −0.221. The Bivariate Moran′s I between human disturbance and regulation and maintenance service is −0.212. And the Bivariate Moran′s I between human disturbance and cultural services is −0.462, which shows a stronger negative correlation.

Epidemiological characteristics and spatio-temporal aggregation of severe fever with thrombocytopenia syndrome in Jinan City, China, 2018-2022.

Severe fever with thrombocytopenia syndrome (SFTS) has become a significant public health issue in Jinan City. However, the analysis of epidemiological characteristics and spatio-temporal clustering of SFTS in Jinan has not been studied yet. SFTS data from 2018-2022 in Jinan City were obtained from the China Information System for Disease Control and Prevention. Global spatial autocorrelation and local spatial autocorrelation analyses were performed using ArcGIS 10.2 software, and spatiotemporal hotspot area detection was carried out using SatScan 9.6 software. Between 2018 and 2022, 680 SFTS cases were reported in Jinan City, resulting in 53 deaths and an average case fatality rate of 7.8%. 99.0% of cases occurred between April and October, 91.9% individuals were over 50 years old, and 87.79% were primarily farmers. A positive spatial correlation of SFTS in Jinan was observed (Moran's I value between 0.135-0.197, P<0.001), indicating spatial aggregation, primarily in Licheng, Zhangqiu, Laiwu, and Gangcheng districts in southeastern Jinan. Spatiotemporal scanning detected one class I and two class II aggregation areas, with the class I aggregation area (RR = 5.66, LLR = 192.547, P<0.001) locating in southeastern Jinan City, comprising 31 towns/streets, and an aggregation time from 13 May 2020 to 13 October 2022. Spatial and temporal aggregation of SFTS is evident in Jinan. Based on the spatial and temporal distribution and epidemiological characteristics, prevention and control measures such as public education, monitoring, and training should target key populations in high-incidence epidemic areas.

Evaluation of the effect of a low-carbon green city policy on carbon abatement in South Korea: A city-level analysis based on PSM-DID and LSA models

Climate change response is a long-term, stage-specific global challenge that necessitates ongoing research on its routes. Although previous research has examined policy planning and indicator development for a low-carbon transition based on a variety of macropolicies, more research is needed to determine whether such policies can help reduce carbon emissions and accelerate urban transitions. Based on 2015–2021 panel data covering 17 South Korean first-level administrative districts, we employ a propensity score matching-difference in difference (PSM-DID) model and a mediating effect model to address issues of interregional development faults and policy feasibility and to investigate the impacts of the low-carbon green city (LCGC) policy in different regions on urban carbon emissions. To address the development gap and policy feasibility across regions, this study investigates the mechanism and path of the effect of the LCGC policy on urban carbon dioxide emissions in various regions, ultimately incorporating local spatial autocorrelation (LSA) to investigate the characteristics of the differences at the spatial level. The findings demonstrate that (1) the present regulations effectively limit total urban carbon emissions but are characterized by strong rebound elasticity and a short period. (2) Energy use and renewable energy production have the greatest influence on reducing total urban carbon emissions. (3) The local spatial autocorrelation clustering characteristics present the clustering characteristics of being high in the east and low in the north. The HH agglomerations have the largest coverage and are mainly distributed in the southeast and southwest directions of the study area. The results of the study provide a decision-making basis and theoretical support for relevant decision-makers to formulate differentiated carbon emission reduction policies and to eliminate the current transition dilemma with a new research methodology.

Exploring Intra-Island Population Mobility and Economic Resilience: The Case of Hainan Island, China

Islands may be more resilient than we realize when responding to shocks. As COVID-19 lessens international tourism to islands, it is valuable to explore whether island recovery can be accredited to the interactive behaviors within the island. Therefore, this research takes 18 administrative regions within Hainan Island as the research object, uses population migration big data to show the intra-island interaction network, and focuses on the impact of population mobility on economic resilience under the pandemic shock. Overall, population mobility contributes to the recovery of economic resilience under the pandemic shock, but this effect is regionally heterogeneous between the economic circle and the ecological conservation area. During the study period, there is a local spatial autocorrelation between economic resilience and population mobility, showing sporadic scattered distributions of the H-H, H-L and L-L cluster. The research findings offer practical strategies to improve island resilience.

Assessment of spatial heterogeneity of employment in Russian regions

The article studies the problem of spatial heterogeneity of employment in Russian regions. Despite the rather high indicators for Russia and their positive dynamics, the situation in the regions is strikingly different. Heterogeneity of employment distribution between regions can be intensified as a result of the influence of some territories on others. The purpose of the study is to assess spatial heterogeneity and spatial autocorrelation effects between Russian regions in terms of employment. As the main research method, the paper applies Moran’s methodology to calculate local and global spatial autocorrelation indices and Anselin’s methodology to form a spatial matrix. The results of the study demonstrate that among Russian regions there is a high potential for clustering by the number of employed people. At the same time, a group of western regions is clearly distinguished, which have greater territorial cohesion characterized by pronounced spatial mutual influence. The results of the study can be used in formulating strategies for Russian regions development, in particular, the Far Eastern regions, as well as in determining priority development goals.

Influence of Natural and Social Economic Factors on Landscape Pattern Indices—The Case of the Yellow River Basin in Henan Province

The Yellow River Basin holds significance as a vital ecological shield and economic hub within China. Adapting land utilization practices and optimizing landscape patterns are of paramount significance in preserving the ecological equilibrium of the Yellow River Basin while fostering high-quality economic development. In this study, we selected the Yellow River Basin in Henan Province as our research area. We use a land use transition matrix and FRAGSTATS 4.2 software to analyze changes in land use and landscape patterns within the study area from 1990 to 2020. Furthermore, Geographical Detector is employed to explore the impact of different natural and social economic factors that have influenced the progress of the landscape surface pattern in the study area. Finally, to identify the zonal aggregation effects of primary components in connection with landscaping feature indices at the city dimension, we use bivariate local spatial autocorrelation. The results are as follows: (1) In terms of land use change characteristics, the area of cultivated land, grassland, shrubs, and bare land shows a decreasing tendency, the area of construction land and forest land shows an increasing tendency, and the water area fluctuates and changes. Most of the cultivated land is shifted to construction land, followed by forest land, construction land, and cultivated land mainly transferred from grassland. (2) At the level of type in terms of shifting landscape patterns, cultivated land, forest land, water, and construction land have a more complex landscape shape, reduced fragmentation, and better natural connectivity. At the overall level, the overall landscape pattern indices are relatively stable, with more patch types and a more balanced distribution. (3) The findings regarding influencing factors reveal that the primary industry output value, population, secondary industry output value, and temperature are the principal driving forces behind the progress of the landscape surface pattern. The main drivers have changed over time in different regions. As indicated by the findings from bivariate local spatial autocorrelation analysis, at the city scale, the leading cause of landscape fragmentation in Luoyang is the primary industry output value, while in Xinxiang, landscape fragmentation is primarily driven by the secondary industry output value and temperature. In this study, we introduce the bivariate local spatial autocorrelation method to analyze the clustering effects of key influencing factors and landscape patterns at the city scale. This is crucial for the harmonized growth of land use planning and the urban economy in the Yellow River Basin.