Spatial Autocorrelation Analysis Research Articles

Spatio-temporal variations of habitat quality in the Greater Bay Area around Hangzhou Bay, based on LUCC and simulation

Context Habitat quality (HQ) is vital for achieving sustainable regional development. Understanding the spatial patterns and temporal evolution of HQ in the context of land use–cover change (LUCC) is crucial for promoting ecological civilisation and high-quality growth, especially for regions with rapid economic development. Aims In order to analyse the impact of LUCC on habitat quality in the coastal areas with frequent human activities. Methods This study focused on the Greater Bay Area around Hangzhou Bay and analysed the HQ on the basis of LUCC data during 2010–2020 by using the InVEST model and spatial autocorrelation analysis. Additionally, land-use data for multiple scenarios in 2030 were predicted using the PLUS model, and the variations in land use and HQ in the study region during 2020–2030 were assessed. Key results During 2010–2020, the construction land in the region expanded by 1932.79 km2, primarily at the expense of cropland and water areas. The mean HQ values were 0.6287, 0.6181 and 0.6037 for 2010, 2015 and 2020 respectively, indicating a continuous decline. Spatially, HQ exhibited strong clustering during this period. However, there was a clear trend of fragmentation and reduction in ‘high–high’ cluster areas along the coast, mostly owing to the transformation of water areas and wetlands into construction land and cropland. In the projected scenarios (natural development, ND; economic development, ED; crop protection, CP; and ecological protection, EP) for 2030, the mean HQ values are estimated to be 0.5881, 0.5837, 0.5915 and 0.5965 respectively. Compared with 2020, there will be a certain decrease in HQ, with the EP scenario showing the lowest decrease of 0.0052. Conclusions The HQ changes were closely linked to LUCC, the construction-land expansion was the main cause of HQ destruction in the Greater Bay Area around Hangzhou Bay. To alleviate the trend of declining HQ, it is essential to select appropriate development scenarios for each city in the region and coordinate the development of the cities. Implications These findings provide valuable insights for promoting sustainable economic growth in the Greater Bay Area around Hangzhou Bay.

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.

Urban regeneration VS residential property characteristics: which is the most influential on property values?

AbstractUrban regenerations have always been a tool towards the improvement of the urban environment through environmental integration and thereafter the quality of life of citizens. However, all regenerations do not have the same success and impact on the surrounding properties. In this paper, two major urban regenerations of Thessaloniki, the second largest city of Greece, are studied through the scope of their effect on property values in combination with the analysis of the effect of traditional property characteristics on values. This study was necessary to fill the gap of determining that sustainable urban planning and environmental integration are nowadays proven more important than property characteristics, when population gathering in urban areas is at its highest point. The current study applied geographically weighted regression, spatial autocorrelation and hot-spot analysis through the environment of ArcGIS to determine which are the factors that citizens seek in their residential area and if these factors are more important than their property characteristics. The findings of the study indicate that citizens always seek for additional urban green and urban quality even in areas where the urban environment is already at a very good level. The difference appears in the fact that in areas where urban green or open spaces are upsent, the weight leans on the characteristics of the wider area and its citizens (socio-economic characteristics, habits etc.) rather than on the property characteristics, when determining property values. On the other hand, citizens living in areas with urban green and open spaces, move a step forward and seek for larger and more friendly green or regenerated areas as a counterpoint for already increased property values. In general, the result of this research underlines that a targeted urban planning for each sub-area is one of the keys toward property values stabilization against economic variations and towards the improvement of the quality of citizens’ lives through environmental integration. Therefore, decision makers and urban designers should take into account all different needs of the citizens in each area setting the goal of maximum possible urban sustainability and resilience and the minimum environmental degradation.

Spatio-Temporal Evolution and Multi-Scenario Simulation of Non-Grain Production on Cultivated Land in Jiangsu Province, China

Spatio-Temporal Evolution and Multi-Scenario Simulation of Non-Grain Production on Cultivated Land in Jiangsu Province, China

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.

Influence of land use change on habitat quality: a case study of coal mining subsidence areas.

Revealing the spatiotemporal evolution characteristics and key driving processes behind the habitat quality is of great significance for the scientific management of production, living, and ecological spaces in resource-based cities, as well as for the efficient allocation of resources. Focusing on the largest coal-mining subsidence area in Jiangsu Province of China, this study examines the spatiotemporal evolution of land use intensity, morphology, and functionality across different time periods. It evaluates the habitat quality characteristics of the Pan'an Lake area by utilizing the InVEST model, spatial autocorrelation, and hotspot analysis techniques. Subsequently, by employing the GTWR model, it quantifies the influence of key factors, unveiling the spatially varying characteristics of their impact on habitat quality. The findings reveal a notable surge in construction activity within the Pan'an Lake area, indicative of pronounced human intervention. Concurrently, habitat degradation intensifies, alongside an expanding spatial heterogeneity in degradation levels. The worst habitat quality occurs during the periods of coal mining and large-scale urban construction. The escalation in land use intensity emerges as the primary catalyst for habitat quality decline in the Pan'an Lake area, with other factors exhibiting spatial variability in their effects and intensities across different stages.

Spatio−Temporal Changes and Key Driving Factors of Urban Green Space Configuration on Land Surface Temperature

Changes in land cover by rapid urbanization have diminished the cooling effect of urban green spaces (UGS), exacerbating the upward trend of land surface temperature (LST). A thorough and precise understanding of the spatio-temporal characteristics of UGS and LST is essential for mitigating localized high temperatures in cities. This study identified the spatio-temporal changes in UGS configuration and LST in Shanghai from 2003 to 2022. The correlation between UGS configuration and LST was explored using spatial autocorrelation analysis and causal inference. The results show that (1) the high-temperature space had grown from 721 km2 in 2003 to 3059 km2 in 2022; (2) in suburbs, the largest area of UGS tended to decrease, while the number of patches tended to increase, indicating a distinct feature of suburbanization; (3) changes in the largest area of UGS had more significant spatial correlation, indicating that urban sprawl primarily impacts large UGSs; and (4) compared to the number and shape of UGS, changes in the largest area are the key factor influencing regional LST. These findings enrich the knowledge of the spatio−temporal relationship between the UGS configuration and its cooling effect in urbanization, offering valuable insights for building cooler cities.

Spatio-temporal patterns and population exposure risks of urban heat island in megacity Shanghai, China

Accompanied by global warming and rapid urbanization, the urban heat island (UHI) effect has been intensifying, which leads to an increased risk of population heat exposure, especially in mega-cities. However, there are fewer studies on the identification of UHI ranges and the associated population heat exposure risks, particularly on the fine scale. Therefore, this study took Shanghai as a case study and explored the spatio-temporal evolution characteristics of the UHI ranges and population heat exposure risks, based on five-period MODIS land surface temperature (LST) product, population density distribution data, and land use and cover change data from 2000 to 2020, combined with the temperature attenuation mutation (TAM) method, breakpoint regression model, population heat exposure relative risk assessment method, and spatial autocorrelation analysis. The results showed that: (1) From 2000 to 2020, the UHI range in Shanghai was continuously expanding, and the intensity of the surface UHI was gradually increasing. The overall pattern was characterized by a stable large heat island in the central urban area and new small heat islands that continued to emerge in the peripheral suburbs. (2) The population heat exposure risks in Shanghai intensified from 2000 to 2020, with an increasing number of people exposed to high-intensity UHI ranges, doubling the population coverage in these areas. By the end of 2020, the population exposed to UHI ranges accounted for approximately 81 % of the total population of Shanghai. (3) The spatial pattern of population heat exposure risks from 2000 to 2020 was generally similar. Population heat exposure regions of high risk level were primarily concentrated in the central urban areas, showing typical spatial clustering characteristics, while population heat exposure regions of low risk level were mainly dispersed in the peripheral suburbs, and showed a gradual transformation trend into population heat exposure regions of high risk level. Our findings can provide scientific basis and decision support for precise and refined urban heat risk control and planning management.

Abstract PO3-10-03: Nationwide Impact of Neighborhood Poverty Hotspots on Breast Cancer Mortality

Abstract Title: Nationwide Impact of Neighborhood Poverty Hotspots on Breast Cancer Mortality Background: Poverty is known to lead to the worst breast cancer (BC) outcomes, at the individual and county level. However, we believe that the relationship between poverty and breast cancer mortality is more complex and can be influenced by the economic status and available resources of the surrounding neighborhoods. Our goal is to understand to what extent poverty in the surrounding neighborhood may contribute to breast cancer mortality on a national scale and the county areas at the highest risk. Methods: This study examines the association between neighborhood effects of poverty on overall and BC mortality among women diagnosed between 2005 and 2019 from NCI SEER. We also obtained county-level five-year poverty estimates from the American Community Survey. Counties with poverty estimates above or below the national mean were high-poverty (H) or low-poverty (L) counties, respectively. We then used local indicators of spatial autocorrelation (LISA) analysis with Local Moran’s I of the poverty estimates and their H- and L-county categorization to define four neighborhood poverty environments for US counties. Specifically, H-counties among other H-counties were classified as poverty hotspots (HH) counties, L-counties among other L-counties were classified as poverty cold spots (LL), H-counties among LL-counties were HL-counties, and L-counties among HH-counties were LH-counties. Non-Hispanic White (NHW), Non-Hispanic Black, and Hispanic women at least 20 years of age diagnosed with local, regional, or distal stage BC between 2005-2009 and 2010-2014 that survived more than one year after diagnosis were aggregated by county of residence at diagnosis to determine their neighborhood poverty environment at BC diagnosis. Women included in the study had a Local Moran's I p-value less than or equal to 0.3 for their county of residence. A Poisson regression model that adjusts for age at diagnosis, race/ethnicity (NHW vs. not-NHW), BC stage, year of diagnosis (2005-2009 vs. 2010-2014), both local and systemic treatment except for hormone therapy, and Estrogen Receptor (ER) status (negative vs. positive) was used to assess the effects of county-level poverty cluster types on BC mortality. Results: Our analysis includes 24,070 women diagnosed with BC, of which 6,919 women died, and 2,262 of these deaths were specific to BC. In total, 4,528 women were in HH counties from CA, GA, KY, LA, NM, UT, and HI; 14,112 women were in LL counties; 4,338 women were in HL counties; 1,092 women were in LH counties. Women in HH, LH, and HL counties had a 1.28 (95%CI:1.15,1.42), 1.23 [95%CI:1.01-1.48], and 1.05 [CI: 0.93,1.17] relative risk of cause-specific BC death respectively when compared to deaths in LL counties after adjustment for year of diagnosis, age at diagnosis, race/ethnicity, stage at diagnosis, treatment, and estrogen receptor status. When stratified by ER status or BC stage at diagnosis, the relative risk of BC mortality for women in HH counties diagnosed with ER-positive, local or regional stage had a 1.35 (95% CI: 1.19,1.52), 1.32 (95%CI: 1.13,1.54), and a 1.21 (95%CI:1.02,1.43) when compared to women in LL counties, respectively. Our analysis also showed that total death trends were similar to the trends observed in cause-specific deaths. In addition, we observed similar total and cause-specific BC death trends when using a p-value of 0.05 for the LISA analysis. Conclusion: We use LISA spatial clustering to demonstrate the impact national poverty neighborhood environments have on BC mortality, thus identifying counties of worst BC outcomes through neighborhood poverty environments. Our findings suggest that prioritization of resource allocations to HH counties would significantly improve BC outcomes. Citation Format: Ifeoma Nwigwe, Michael Desjardins, Kala Visvanathan. Nationwide Impact of Neighborhood Poverty Hotspots on Breast Cancer Mortality [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-10-03.

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 .

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 Analysis of Internally Displaced Persons’ Camps in Borno State, Nigeria

The situation in Borno State, Nigeria has been exacerbated by the ongoing insurgency led by Boko Haram in northeast Nigeria, leading to widespread displacement and the establishment of Internally Displaced Persons (IDP) camps. This study aims to identify the locations of IDP camps in Borno State and analyze their spatial distribution, density, and clustering patterns. Furthermore, it seeks to investigate the geospatial implications of these patterns. To achieve this, the study employs geospatial analysis techniques such as Kernel Density Estimation, Cluster and Outlier Analysis, Nearest Neighbour Analysis, and Spatial Autocorrelation Analysis. The data collected for this study includes location coordinates, satellite imagery, demographic information, and administrative maps. The findings reveal that the distribution of IDP camps in Borno State is non-random and demonstrates varying camp densities across senatorial districts, with predominant high-high clusters and a deliberate, non-random arrangement. These results align with spatial analysis theories, emphasizing the importance of recognizing hidden inequalities in IDP camps and informing targeted interventions and resource planning for a more effective and equitable humanitarian response in Borno State. The implications extend to policy planning and enhanced vulnerability assessment for effective humanitarian responses, stressing the need for tailored interventions based on identified hotspots that address root causes and implement both immediate and long-term solutions. Future research directions could involve more in-depth investigations into the influencing factors, socio-economic impacts, and temporal dynamics of spatial patterns in displacement.

Correlation Methodologies between Land Use and Greenhouse Gas emissions: The Case of Pavia Province (Italy)

The authors present an analysis of the correlation between demographic and territorial indicators and greenhouse gas (GHG) emissions, emphasizing the spatial aspect using statistical methods. Particular attention is given to the application of correlation techniques, considering the spatial correlation between the involved variables, such as demographic, territorial, and environmental indicators. The demographic data include factors such as population, demographic distribution, and population density; territorial indicators include land use, particularly settlements, and road soil occupancy. The aims of this study are as follows: (1) to identify the direct relationships between these variables and emissions; (2) to evaluate the spatial dependence between geographical entities; and (3) to contribute to generating a deeper understanding of the phenomena under examination. Using spatial autocorrelation analysis, our study aims to provide a comprehensive framework of the territorial dynamics that influence the quantity of emissions. This approach can contribute to formulating more targeted environmental policies, considering the spatial nuances that characterize the relationships between demographics, territory, and GHGs. The outcome of this research is the identification of a direct formula to obtain greenhouse gas emissions from data about land use starting from the case study of Pavia Province in Italy. In the paper, the authors highlight different methodologies to compare land use and GHG emissions to select the most feasible correlation formula. The proposed procedure has been tested and can be used to promote awareness of the spatial dimension in the analysis of complex interactions between anthropogenic factors and environmental impacts.

Matching supply and demand of cooling service provided by urban green and blue space

Urban blue-green space (UBGS) has been extensively investigated for its climate regulation capability in the context of climate change. However, current efforts are limited toward the cooling effects mismatch in supply and demand of UBGS. This study adapts the framework of ecosystem supply and demand to quantify the supply-demand matching of the cooling effects of UBGS. Two indices were proposed to evaluate cooling provision and demand of UBGS: Cooling Supply Index (CSI), accounting for cooling contribution and spatial effect, and Cooling Demand Index (CDI), considering physical and social dimensions of cooling demand. Four-quadrant analysis and bivariate local indicators of spatial autocorrelation (BiLISA) analysis were employed to distinguish the supply-demand matching status: high supply with high demand, low supply with high demand, low supply with low demand, and high supply with low demand. The results show that 1) CSI and CDI yield great performance in quantifying cooling supply and demand, respectively; 2) the four-quadrant analysis and bivariate LISA analysis are complementary to each other, and can target those areas in different matching statuses for climate-sensitive planning and design; and 3) particular attention should be given to areas characterized by low supply with high demand. This study marks a crucial step toward integrating social aspects into the analysis of cooling effects provided by UBGS. It enriches the analysis framework for urban ecosystem services and provides valuable guidance for landscape planning to mitigate heat stress.

Unravelling regional development through the production-living-ecological perspective: Assessing heterogeneity and expert insights

This study delves into the spatial heterogeneity of Production-Living-Ecological Spaces (PLES) at the township scale in Zhejiang, China. By leveraging a comprehensive array of spatial data and advanced analytical methods, we uncover distinct patterns and variations within these spaces. Our analysis brings to light the complex interactions between human activities, land use, and ecological factors in the region. Through grid-based evaluation, significant spatial disparities in land use across Zhejiang Province are identified, reflecting the diverse socio-economic and environmental fabric of the area. Particularly notable is the dominance of ecological considerations in economically developed and coastal regions. Spatial autocorrelation analysis reinforces this finding by confirming functional clustering among neighboring townships. The application of triangular coordinate analysis further underlines the preponderance of ecological factors, pointing to a prevailing trend of environmental prioritization. Complementing the spatial data analysis, insights from expert interviews add depth to our understanding. These discussions with professionals engaged in PLES planning and development elucidate the real-world implications of our findings, offering a nuanced perspective on the challenges and opportunities in managing these spaces. Experts highlight the need for an improved approach to accommodating the functions of production, living, and ecological spaces, addressing both regional needs and sustainable development goals. The study contributes to the field of regional planning and sustainable development, emphasizing the importance of recognizing and addressing spatial heterogeneity in PLES. It provides insights for decision-makers and planners seeking to navigate the delicate balance of socio-ecological landscapes in Zhejiang and similar regions.

Spatial Analysis of Intercity Migration Patterns of China’s Rural Population: Based on the Network Perspective

Since entering the 21st century, many developing countries around the world have begun to enter a stage of rapid urbanization; large-scale “rural-urban” population migration has become a typical social phenomenon in these countries. Against this backdrop, this paper aims to elucidate the spatial migration characteristics of rural populations and to discuss future rural–urban development strategies. For this purpose, this paper takes China as a case and employs methods such as spatial autocorrelation analysis, hotspot analysis, and network analysis to construct an intercity migration network of rural migrants and analyze its spatial characteristics and internal structure. The results indicate that the migration pattern of the rural population exhibits notable spatial clustering features. Cities in the eastern and central regions are, respectively, hotspots for the inflow and outflow of rural populations, with internal migration dominating in western cities and relatively inactive rural population movements in northeastern cities. Municipalities directly under the central government, sub-provincial cities, and provincial capitals show a significant tendency to attract rural populations, while prefecture-level and county-level cities mainly radiate rural populations outward. Cities nationwide form seven major clusters in the migration network, and these clusters exhibit distinct structural characteristics. Rural population migration is influenced by various factors. In the future, considerations should focus on the county as the primary unit, attracting rural populations for local employment, and promoting rural revitalization and agriculture development. The findings of this paper are of reference significance not only to China but also to many developing countries with similar national conditions in the world.

Analysis of the situations and influencing factors of public anxiety in China: based on Baidu index data.

Anxiety disorders have emerged as one of the most prevalent mental health problems and health concerns. However, previous research has paid limited attention to measuring public anxiety from a broader perspective. Furthermore, while we know many factors that influence anxiety disorders, we still have an incomplete understanding of how these factors affect public anxiety. We aimed to quantify public anxiety from the perspective of Internet searches, and to analyze its spatiotemporal changing characteristics and influencing factors. This study collected Baidu Index from 2014 to 2022 in 31 provinces in mainland China to measure the degree of public anxiety based on the Baidu Index from 2014 to 2022. The spatial autocorrelation analysis method was used to study the changing trends and spatial distribution characteristics of public anxiety. The influencing factors of public anxiety were studied using spatial statistical modeling methods. Empirical analysis shows that the level of public anxiety in my country has continued to rise in recent years, with significant spatial clustering characteristics, especially in the eastern and central-southern regions. In addition, we constructed ordinary least squares (OLS) and geographically weighted regression (GWR) spatial statistical models to examine the relationship between social, economic, and environmental factors and public anxiety levels. We found that the GWR model that considers spatial correlation and dependence is significantly better than the OLS model in terms of fitting accuracy. Factors such as the number of college graduates, Internet traffic, and urbanization rate are significantly positively correlated with the level of public anxiety. Our research results draw attention to public anxiety among policymakers, highlighting the necessity for a more extensive examination of anxiety issues, especially among university graduates, by the public and relevant authorities.

Analysis of the spatial mismatch pattern of net carbon in agriculture and its influencing factors

Agricultural production has dual functions as a carbon emitter and sink, and fully exploiting the carbon sink function of agriculture is highly valuable for ensuring carbon neutrality. The layout of net carbon in agriculture shows an imbalance, i.e., underscaling in areas with a net carbon advantage or overscaling in areas with a net carbon disadvantage, which restricts the fulfillment of agricultural carbon sinks. Clarifying the global pattern of net carbon imbalances in agriculture and their drivers is highly important for improving the overall net carbon. In this regard, this paper focuses on the global net carbon in agriculture in 155 countries and regions, and a spatial mismatch index of net carbon in agriculture (MNCA) is constructed based on the relative difference between net carbon and cultivated land. Spatial autocorrelation analysis and the standard deviation ellipse are used to reflect its spatiotemporal evolution pattern. In addition, through geographically weighted regression, we explored the effects of agricultural structure, agricultural economic development, the scale of labor and cultivated land and organized the mismatches into carbon and area effects. The results show that (1) global agricultural production represents net carbon emissions, while developing countries represent net carbon sinks, and there is a mismatch of net agricultural carbon in most regions; (2) the MNCA is characterized by low-low clustering in North America, Asia and Europe, with its center of gravity falling in western Africa and migrating northeastward; and (3) the main factors influencing the MNCA are the agricultural structure and economic level. Underscaling in areas with a net carbon advantage is a major problem. To increase the global agricultural carbon sink capacity, we suggest that North America, South Asia and other regions strictly implement agricultural emission reduction policies and that Central America, South America and Africa tentatively upgrade agricultural technology and their economies to alleviate farmland pressure through transnational cooperation. In this paper, the global net carbon imbalance in agriculture is identified and the overall implementation path of increasing carbon sinks is explored to provide a reference for achieving carbon neutrality. SynopsisThis study reports a global mismatch in net carbon in agriculture and identifies the wasted carbon sink advantage of developing countries as the root problem.

Impact of transport superiority on ecosystem health in arid regions: A case study of southern Xinjiang, China

The increasingly improved transportation infrastructure network is the prerequisite and foundation for driving regional development. Scientifically investigating the mechanisms by which the transport superiority degree (TSD) in arid regions affects the health of ecosystems holds significant practical value and importance for ecosystem protection and regional sustainable development. This study investigates the impact of the TSD on the Ecosystem Health Levels (EHL) in Southern Xinjiang, China, an area characterized by its arid climate and ecological vulnerability. Arid regions, such as Southern Xinjiang, often face unique ecological challenges, including water scarcity, soil erosion, and biodiversity loss, which can be exacerbated by human activities including transportation infrastructure development. By leveraging multi-source remote sensing data, this research aims to uncover the intricate dynamics between TSD and EHL in such a fragile ecological context. The findings of this study are critical for understanding the balance between infrastructural development and environmental conservation in arid regions and provide valuable insights for sustainable planning and policy-making in similar ecological settings. The research results show the following: (1) Over the study period, county-scale TSD in southern Xinjiang generally increased, while the conditions of EHL deteriorated, with higher EHL in the eastern counties than in the western counties. (2) Bivariate spatial autocorrelation analysis reveals a significant spatial dependency between county-scale TSD and EHL, with primary relationship types being LH (Low TSD − High EHL) and HL (High TSD − Low EHL). (3) Spatial regression model results indicate that an increase in TSD leads to the deterioration of county-scale EHL, and during the study period, it was observed that the interference of TSD on EHL gradually weakened.

Effects of land utilization transformation on ecosystem services in urban agglomeration on the northern slope of the Tianshan Mountains, China

Land utilization transformation (LUT) is a key factor affecting ecosystem services (ESs). The urban agglomeration on the Northern Slope of the Tianshan Mountains (UATM) is located in a typical arid region with extremely fragile ecological environment. However, the impact of LUT on the spatial pattern of ESs in the region over the past 20 years is not clear. This study aimed to explore LUT characteristics of the urban agglomeration on the UATM using a land transfer matrix, information entropy, land utilization intensity, and land utilization dynamic degree. Various ESs indexes were quantitatively measured using the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, and the effect of LUT on ESs was revealed through a geographic detector and spatial auto-correlation analyses. The results of this study led to the following conclusions: First, between 2000 and 2020, the primary land utilization types in UATM were arable land, grassland, and bare land, with significant cross-transformations occurring among these types. Meanwhile, LUT showed marked differences among different regions. LUT changed rapidly and significantly in the central region; in contrast, slower and slight changes were observed in the northern and southern regions. Second, during the research period, habitat maintenance, water yield and carbon sequestration decreased, and the soil retention function increased. The accelerated urban development in the second decade led to more rapid changes in ESs. Finally, both the structure and intensity of land utilization showed the strongest explanatory capability for changes in ESs. Different dimensions of LUT showed significant interactions with ESs. Therefore, it is advisable to guide LUT scientifically, promote vegetation restoration projects, alleviate the impacts of human activities and climate change on ESs, and enhance the ecological safety and environmental sustainability of UATM and even arid regions in Central Asia.