Local Spatial Autocorrelation Analysis 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.

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 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.

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 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.

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.

Spatial distribution characteristics of the prevalence of advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody in Hunan Province in 2020

To investigate the spatial distribution characteristics of the prevalence of advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody, and to examine the correlation between the prevalence of advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody in Hunan Province in 2020, so as to provide insights into advanced schistosomiais control in the province. The epidemiological data of schistosomiasis in Hunan Province in 2020 were collected, including number of permanent residents in survey villages, number of advanced schistosomiasis patients, number of residents receiving serological tests and number of residents seropositive for anti-Schistosoma antibody, and the prevalence advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody were descriptively analyzed. Village-based spatial distribution characteristics of prevalence advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody were identified in Hunan Province in 2020, and the correlation between the revalence advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody was examined using Spearman correlation analysis. The prevalence of advanced schistosomiasis was 0 to 2.72% and the seroprevalence of anti-Schistosoma antibody was 0 to 20.25% in 1 153 schistosomiasis-endemic villages in Hunan Province in 2020. Spatial clusters were identified in both the prevalence of advanced schistosomiasis (global Moran's I = 0.416, P < 0.01) and the seroprevalence of anti-Schistosoma antibody (global Moran's I = 0.711, P < 0.01) in Hunan Province. Local spatial autocorrelation analysis identified 98 schistosomiasis-endemic villages with high-high clusters of the prevalence of advanced schistosomiasis, 134 endemic villages with high-high clusters of the seroprevalence of anti-Schistosoma antibody and 36 endemic villages with high-high clusters of both the prevalence of advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody in Hunan Province. In addition, spearman correlation analysis showed a positive correlation between the prevalence of advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody (rs = 0.235, P < 0.05). There were spatial clusters of the prevalence of advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody in Hunan Province in 2020, which were predominantly located in areas neighboring the Dongting Lake. These clusters should be given a high priority in the schistosomiasis control programs.

Unveiling the air pollution tapestry in China: A comprehensive assessment of spatiotemporal variations through geographically and temporally weighted regression

Study of air pollution informs environmentally-conscious policies and urban planning by government and businesses, leading to more scientific decision-making. This paper comprehensively analyzes the spatiotemporal correlation of six air pollutants (PM2.5, PM10, SO2, NO2, CO, and O3) at the national level of China from 2015 to 2021. The temporal changes in environmental air pollutants are examined through statistical data analysis. Secondly, the annual and quarterly spatial distribution characteristics of air pollutants are analyzed by utilizing global and local spatial autocorrelation analysis and visualizing pollutant concentration data. To quantify the spatiotemporal correlation information obtained from the data, we proposed a novel framework based on the Geographically and Temporally Weighted Regression (GTWR) model to provide a local approach for detecting the spatiotemporal correlation between urban air pollutants on a large scale. The framework's experimental results showed that the significant correlation between urban air pollutants in time and space ranged from 0.45 to 0.9, which is better than traditional correlation algorithm, and the degree of local spatiotemporal correlations can be explained by spatiotemporal coefficients. We find noticeable differences in the correlations between different pollutants and geographical boundaries in the spatial dimension. Furthermore, in terms of spatiotemporal location, PM2.5, PM10, NO2, SO2, and CO exhibit positive correlations with each other, while O3 shows both positive and negative correlations with other pollutants. This study offers an important reference for air quality monitoring and prediction, contributing to the improvement of accuracy and timeliness of air quality warnings.

Cumulative disadvantage during childhood: The association between moving and accumulated neighborhood deprivation over a child’s life course in the Netherlands

ABSTRACT While the importance of residential moves and neighborhood context for children is widely recognized, few studies examine childhood mobility and neighborhood context together and over time. Using a typology of mobility trajectories based on frequency, age, distance and change in population density, this study analyzes the interrelatedness between mobility trajectories and socioeconomic neighborhood composition throughout childhood. Using full-population register data, we follow children born in the Netherlands in 1999 until age 16. Local spatial autocorrelation analyses reveal concentrations of childhood mobility and neighborhood deprivation in cities. Focusing on children born in metropolitan areas, results indicate that children born in deprived neighborhoods are more likely to experience any move. Short-distance moves are associated with increased exposure and long-distance moves with less exposure to neighborhood deprivation. We argue we need a multidimensional longitudinal perspective to fully capture residential contexts to which children are exposed and enhance knowledge of childhood accumulations of disadvantage.

Analisis Risiko Bencana pada kasus Curah Hujan Ekstrem Provinsi Sulawesi Barat dengan Metode Moran's I &amp; Local Indicator Of Spatial Association

High rainfall in an area makes the area vulnerable to various disasters. To reduce the risk of damage caused by various disasters, it is important to look at the pattern of spatial distribution of areas that have a high risk of being affected by extreme rains. Information about distribution patterns of rainfall can assist policy makers in making decisions regarding extreme rain events in West Sulawesi which have the potential to cause various losses. Therefore, spatial analysis is carried out to detect connections between regions so that rainfall distribution patterns can be known. Based on this, this study aims to analyze the characteristics of the spatial distribution of rainfall in each sub-district of extreme rainfall on an annual scale in West Sulawesi. The results showed that there was a significant spatial autocorrelation. This showed that there was a correlation between the rainfall values between districts in West Sulawesi based on the spatial autocorrelation analysis with the Moran index test. Furthermore, the results of the Local Indicator Spatial Autocorrelation (LISA) analysis of rainfall data in each sub-district show that there are 10 sub-districts in 2019, 7 sub-districts in 2020, 13 sub-districts in 2021, and 7 sub-districts in 2022 which are identified as having spatial autocorrelation with a level significance 0.05. From all the results of the analysis carried out, namely the analysis of the moran index and LISA, it was obtained that the areas that have the greatest risk of high rainfall are the Kalukku, Tabang and Pana sub-districts. The research results obtained can be useful as information and evaluation material that is useful for preparing natural hazard mitigation programs and assisting the government in establishing policies based on information on areas that have a high risk of being affected by extreme rainfall.

Digital research on the resilience control of water ecological space under the concept of urban-water coupling

Introduction: The construction of digital governance of national land space depends greatly on the development of a digital model for robust control of water and ecological space. However, how changes to the urban-water coupling relationships affect the resilience control of water ecological space is rarely reported.Methods: The evolution characteristics of urban and water space in the study area from 2000 to 2020 and the correlation between them are analyzed based on the grid analysis using InVEST and Moran’I methods. Based on the theory of human-environment interaction territorial system to provide a theoretical framework to explain the urban and water space. We used digital to construct an resilience control framework in the Dongting Lake area based on the correlation between the urban and water space.Results: The results show that: 1) From 2000 to 2020, the geographical evolution of towns and cities in the research area displayed obvious spatial variation in intensity, indicating a process of expansion and change. The regional and temporal fluctuations of the water conservation function are significant. The mountainous areas in the east, south, and northwest are where the high values of the water conservation function are primarily found. These regions have more vegetation, which increases the water conservation function. 2) According to the results of local binary spatial autocorrelation analysis, it can be seen that from 2000 to 2020, the high-high agglomeration of town space and water-related is mainly distributed in Linxiang City, Yueyang County, Miluo City, Li County, and Yiyang City, and the low-low agglomeration is mainly distributed in Dongting Lake, Datong Lake, and along the Yangtze River. 3) Based on how urban spatial evolution affects water ecological space, we construct a theoretical framework of urban-water coupling and establish a digital model of water ecological space resilience control in the Dongting Lake area from four perspectives: threshold, visualization, dynamics, and intelligence.

A Hybrid Approach for Soil Total Nitrogen Anomaly Detection Integrating Machine Learning and Spatial Statistics

Soil total nitrogen is one of the most important basic indicators for fertiliser decision making, but tens of millions of soil total nitrogen sampling data have been accumulated, forming a huge database. In this large database, there is a large amount of anomalous data, which can interfere with data analysis, affect the construction of spatial interpolation and prediction models, and then affect the accuracy of nutrient management decisions. The traditional method of identifying soil total nitrogen anomalies based on boxplots suffers from the problems of not being able to identify local anomalies, which can easily lead to misclassification of soil total nitrogen data anomalies, and the detection efficiency is not high. We propose a method to identify soil total nitrogen outliers by combining the Isolation Forest algorithm and local spatial autocorrelation analysis, which can simultaneously detect global and local outliers from large amounts of data and combine organic matter as an auxiliary indicator in the spatial analysis to help judge local outliers. Finally, the results of global and local anomalies were combined to provide a comprehensive assessment of the soil nitrogen data, avoiding the misjudgement or omission of judgement that can occur when using a single method. Using 25,930 soil test data from Yunnan Province in 2009 as an example, we compared and analysed the typical boxplot method and the unsupervised OneClassSVM method and evaluated the performance of each method in terms of correct detection rate, false positive rate and false negative rate. The results show that the proposed method has a correct detection rate (TR) of 99.97%, a false positive rate (FPR) of 8.06% and a false negative rate (FNR) of 0.01% on the data, which shows high validity and accuracy; it is also comparable to the independent isolated forests (FNR = 4.76%), boxplot (FNR = 3.90%) and OneClassSVM (FNR = 4.77%), and the false negative rate is reduced by 4.75%, 3.89% and 4.76%, respectively.

Spatio-temporal evolution and the driving factors of municipal solid waste in Chinese different geographical regions between 2002 and 2020

Urbanization and economic development have contributed to the rapid and massive generation of municipal solid waste (MSW) and significant changes in spatial patterns, which are becoming a serious pollution problem. Previously, macroscopic studies on the driving factors of MSW have been widely conducted at the national level, but the exploration of the driving factors in different geographical regions on a regional scale has not received much attention in the previous literature. This study is based on China, spatial patterns were analyzed using spatial autocorrelation and movement of center of gravity, and time series clustering was used to explore temporal trends. Subsequently, Geodector was adopted to quantify the relationship between MSW generation and driving factors. The results of the study are as follows: 1) By analyzing the spatial pattern of MSW, this study found that MSW showed a spatial pattern of high in the southeast and low in the northwest during 2002–2020, and its separating line was the same as the Hu-line; the average center of gravity of MSW generation in the past 20 years was always located in Henan Province and shifted southward by 339.7 km. 2) The local spatial autocorrelation analysis results showed that the Low-Low clusters moved from southeast to northwest from 2002 to 2020, increasing to 20 cities. High-High clusters mainly appeared in the East Coast and South Coast regions, increasing from 8 to 17 cities in the last 20 years. 3) The analysis of driving factors by Geodetector revealed that Urbanization is the most critical dimension factor influencing MSW generation, with the strongest impact on the East Coast region. The next dimension is Economy, which has the most significant impact on MSW generation in the North West region. Energy is the third dimension that influences MSW generation, with the greatest impact on the North Coast region. The results of this study reveal trends in the spatial and temporal distribution of MSW in different geographic regions of China over the past 20 years and the impact of their driving factors, which can help the Chinese government take action to control MSW in a site-specific manner.

Comprehensive strength evaluation system of commercial centres based on multi-source data: a case of Hefei central city

Urban commercial centres are the most concentrated areas of economic activity. Understanding the spatial distribution pattern and comprehensive strength of urban commercial centres is important to guide the reasonable graded allocation of urban commercial space, spatial structure optimization and sustainable development of the commercial economy. Herein, mobile phone signalling data are used to identify the functional connection between recreational and residential places and local spatial autocorrelation analysis is used to identify the 24 commercial centres in the central city of Hefei. The comprehensive strength evaluation system of commercial centres is constructed from their basic conditions and customer consumption behaviours, and their comprehensive strength indices are accordingly measured and graded. The spatial distribution characteristics of commercial centres at all levels are analysed, and optimisation suggestions are made for the whole area and region. The following conclusions are drawn from the results of this study. (1) Compared to traditional single-perspective evaluations, the developed comprehensive strength evaluation system considers supply and demand perspectives for commercial centres, providing a more holistic and accurate portrayal of the strengths of various centres within a region. (2) The current commercial centres are characterised by ‘large dispersion and small concentration’ in spatial and hierarchical distribution. (3) The commercial centres in Hefei have formed a relatively complete ‘first, second, and third level’ commercial centre system, with the first level as the core and relying on the urban road system to form a network spatial connection. (4) Most of the commercial centres are concentrated in the First Ring Road, Swan Lake in the Government Affairs District and Binhu Century Town Estate, while most areas north of the Second Ring Road and Binhu New District still lack large-scale commercial centres. This study provides a technical reference for analysing urban commercial spatial structure patterns and provides decision support for optimising the spatial layout of urban commercial service functions.

Study of the Morphological Characteristics of Cultivated Land in Semiarid Sandy Areas

The unique natural geographical conditions and land use patterns in semiarid sandy areas have resulted in a distinct cultivated landscape. Identifying and classifying the morphological characteristics of cultivated land are key to improving land utilization efficiency. This study focuses on the semiarid sandy areas of China. We obtained information on cropland morphology by judging high-precision remote sensing images and landscape pattern analysis, and then determined the spatial clustering characteristics of different plot morphologies through local spatial autocorrelation analysis. The results are as follows: cultivated land can be classified into five main types: simple large plots, complex large plots, simple small plots, complex small plots, and scattered plots, and there are significant differences in morphology between eastern and western plots. In addition, different morphology types of plots formed a variety of aggregation patterns; clustered plots are surrounded by scattered plots, showing a staggered distribution pattern. Farmers and land managers can make more informed decisions regarding irrigation, fertilization, and crop selection, and this knowledge can provide a basis for further optimizing the layout of cultivated land, identifying fragmented cultivated land, and scientifically recovering farmland to ensure more scientific and refined classification management and zoning protection of cultivated land.

Spatial distribution of Oncomelania hupensis spread in Hubei Province from 2020 to 2022

To identify the spatial distribution pattern of Oncomelania hupensis spread in Hubei Province, so as to provide insights into precision O. hupensis snail control in the province. Data pertaining to emerging and reemerging snails were collected from Hubei Province from 2020 to 2022 to build a spatial database of O. hupensis snail spread. The spatial clustering of O. hupensis snail spread was identified using global and local spatial autocorrelation analyses, and the hot spots of snail spread were identified using kernel density estimation. In addition, the correlation between environments with snail spread and the distance from the Yangtze River was evaluated using nearest-neighbor analysis and Spearman correlation analysis. O. hupensis snail spread mainly occurred along the Yangtze River and Jianghan Plain in Hubei Province from 2020 to 2022, with a total spread area of 4 320.63 hm2, including 1 230.77 hm2 emerging snail habitats and 3 089.87 hm2 reemerging snail habitats. Global spatial autocorrelation analysis showed spatial autocorrelation in the O. hupensis snail spread in Hubei Province in 2020 and 2021, appearing a spatial clustering pattern (Moran's I = 0.003 593 and 0.060 973, both P values < 0.05), and the mean density of spread snails showed spatial aggregation in Hubei Province in 2020 (Moran's I = 0.512 856, P < 0.05). Local spatial autocorrelation analysis showed that the high-high clustering areas of spread snails were mainly distributed in 50 settings of 10 counties (districts) in Hubei Province from 2020 to 2022, and the high-high clustering areas of the mean density of spread snails were predominantly found in 219 snail habitats in four counties of Jiangling, Honghu, Yangxin and Gong'an. Kernel density estimation showed that there were high-, secondary high- and medium-density hot spots in snail spread areas in Hubei Province from 2020 to 2022, which were distributed in Jingzhou District, Wuxue District, Honghu County and Huangzhou District, respectively. There were high- and medium-density hot spots in the mean density of spread snails, which were located in Jiangling County, Honghu County and Yangxin County, respectively. In addition, the snail spread areas negatively correlated with the distance from the Yangtze River (r = -0.108 9, P < 0.05). There was spatial clustering of O. hupensis snail spread in Hubei Province from 2020 to 2022. The monitoring and control of O. hupensis snails require to be reinforced in the clustering areas, notably in inner embankments to prevent reemerging schistosomiasis.

Spatio-temporal variations of ecosystem service value and its spatial heterogeneity mechanism in the Dongjiang River Basin, China.

Comprehensive and deep research on the variations of ecosystem service value (ESV) based on land utilization/land cover change from a spatio-temporal perspective is of great significance for regional ecosystem optimization, as well as coordinated sustainable development of natural environment and economic society. Based on land utilization, natural environment, and socio-economic data of Dongjiang River Basin from 2010 to 2020, combined with hotspot analysis tools and local spatial autocorrelation analysis methods, we comprehensively analyzed the spatio-temporal variations of Dongjiang River Basin ESV, and further explored the spatial differentiation mechanism with geographic detector tools. The results showed that Dongjiang River Basin was dominated by forest ecosystem from 2010 to 2020. The construction land area had expanded significantly mainly from arable land and forest. The Dongjiang River Basin ESV showed a downward and then an upward trend. The ESV of arable land, forest and construction land continuously decreased, and the ESV of water decreased first and then increased substantially. The spatial distribution of ESV hot and cold spots had a significant agglomeration effect, presenting a pattern of hot spot dispersion in the upstream area and cold spots aggregation in the downstream area. The ESV distribution in the upstream and downstream area was not balanced, with the downstream area bearing greater ecological stress. According to the detection results of ESV spatial differentiation mechanism, land utilization was the main factor affecting the spatial differentiation, with the spatial difference of ESV (q value) reaching 0.462. The interaction of factors could greatly strengthen the spatial differentiation effect on Dongjiang River Basin ESV.