Spatial Autocorrelation Research Articles

Trends in the mobility of primary healthcare human resources in underdeveloped regions of western China from 2000 to 2021: Evidence from Nanning

ObjectiveThis research aimed to identify the fundamental and geographic characteristics of the primary healthcare personnel mobility in Nanning from 2000 to 2021 and clarify the determinants that affect their transition to non-primary healthcare institutions.MethodsThrough utilizing the Primary Healthcare Personnel Database (PHPD) for 2000–2021, the study conducts descriptive statistical analysis on demographic, economic, and professional aspects of healthcare personnel mobility across healthcare reform phases. Geographic Information Systems (QGIS) were used to map mobility patterns, and R software was employed to calculate spatial autocorrelation (Moran’s I). Logistic regression identified factors that influenced the transition to non-primary institutions.ResultsPrimary healthcare personnel mobility is divided into four phases: initial (2000–2008), turning point (2009–2011), rapid development (2012–2020), and decline (2021). The rapid development stage saw increased mobility with no spatial clustering in inflow and outflow. From 2016 to 2020, primary healthcare worker mobility reached its peak, in which the most significant movement occurred between township health centers and other institutions. Aside from their transition to primary medical institutions, the primary movement of grassroots health personnel predominantly directs towards secondary general hospitals, tertiary general hospitals, and secondary specialized hospitals. Since 2012, the number and mobility distance of primary healthcare workers have become noticeably larger and remained at a higher level from 2016 to 2020. The main migration of primary healthcare personnel occurred in their districts (counties). Key transition factors include gender, education, ethnicity, professional category, general practice registration, and administrative division.ConclusionsThis study provides evidence of the features of primary healthcare personnel mobility in the less developed western regions of China, in which Nanning was taken as a case study. It uncovers the factors that impact the flow of primary healthcare personnel to non-primary healthcare institutions. These findings are helpful to policy refinement and support the retention of primary healthcare workers.

Does “National Civilized City” policy mitigate air pollution in China? A spatial Durbin difference-in-differences analysis

“National Civilized City” (NCC) is regarded as China’s highest honorary title and most valuable city brand. To win and maintain the “golden city” title, municipal governments must pay close attention to various key appraisal indicators, mainly environmental ones. In this study we verify whether cities with the title are more likely to mitigate SO2 pollution. We adopt the spatial Durbin difference-in-differences (DID) model and use panel data of 283 Chinese cities from 2003 to 2018 to analyze the local (direct) and spillover effects (indirect) of the NCC policy on SO2 pollution. We find that SO2 pollution in Chinese cities is not randomly distributed in geography, suggesting the existence of spatial spillovers and possible biased estimates. Our study treats the NCC policy as a quasi-experiment and incorporates spatial spillovers of NCC policy into a classical DID model to verify this assumption. Our findings show: (1) The spatial distribution of SO2 pollution represents strong spatial spillovers, with the most highly polluted regions mainly situated in the North China Plain. (2) The Moran’s I test results confirms significant spatial autocorrelation. (3) Results of the spatial Durbin DID models reveal that the civilized cities have indeed significantly mitigated SO2 pollution, indicating that cities with the honorary title are acutely aware of the environment in their bid to maintain the golden city brand. As importantly, we notice that the spatial DID term is also significant and negative, implying that neighboring civilized cities have also mitigated their own SO2 pollution. Due to demonstration and competition effects, neighboring cities that won the title ostensibly motivates local officials to adopt stringent policies and measures for lowering SO2 pollution and protecting the environment in competition for the golden title. The spatial autoregressive coefficient was significant and positive, indicating that SO2 pollution of local cities has been deeply affected by neighbors. A series of robustness check tests also confirms our conclusions. Policy recommendations based on the findings for protecting the environment and promoting sustainable development are proposed.

Sparse sensing to dense assessment: Incorporating spatial autocorrelation for assessing flood impacts.

A rapid and comprehensive assessment of flood impacts is critical to assist emergency managers in conducting effective relief operations. With advances in information technologies, various types of sensors have been widely used to assess flood impacts promptly as they are capable of providing rapid flood impact information. However, sensor-driven approaches are limited in the provision of a comprehensive impact assessment as sensors are often sparsely distributed. In this research, the authors integrate the sparse flood impact information obtained from sensors and the spatial autocorrelation of flood-impacted areas, in order to achieve a rapid and comprehensive flood impact assessment. To achieve such a purpose, a systematic approach is proposed to (1) extract flood impact information from sparsely distributed sensors; (2) model the spatial autocorrelation of flood-impacted areas based on flood evolution and geography principles; (3) learn the parameters of the spatial autocorrelation model through a gradient descent method; (4) infer the flood impacts of sensor-uncovered areas based on the sparsely sensed impacts and the modeled spatial autocorrelation. To illustrate the proposed approach, we studied flood impacts on Highways in Houston, Texas during Hurricane Harvey. Results show that the spatial autocorrelation model presents a decent generalization capability in inferring the probability of neighboring highway blocks having the same flood impacts. Compared to purely sensor-driven approaches, the proposed approach is capable of greatly extending the coverage of flood impact assessment while maintaining the nearly same accuracy.

Urbanization intensifies the imbalance between human development and biodiversity conservation: Insights from the coupling analysis of human activities and habitat quality

AbstractIntensified human activities have been seriously threatening the structure and ecological processes of ecosystems, resulting in habitat degradation. Therefore, coordinating the coupling between human activities and habitat quality (HQ) is crucial for high‐quality sustainable regional development and human well‐being. This study evaluated the human activities and HQ in the Pearl River Delta (PRD) urban agglomeration in China from 2000 to 2020 using the human footprint index (HFI) and the integrated valuation of ecosystem services and tradeoffs model. Then, we employed bivariate spatial autocorrelation and a coupling coordination degree (CCD) model to explore the synergistic relationship between human activities and HQ. The results show that spatial changes in HQ were predominantly driven by human activities. The gradual outward urban expansion resulted in significant HQ degradation. Slight HQ improvement by ecological restoration in urban outskirts cannot offset HQ losses caused by urbanization. During the study period, high‐HQ low‐HFI clusters decreased by 1.02%, while low‐HQ high‐HFI clusters increased by 4.67%, the two main clustering types in the PRD. Despite the CCD between HFI and HQ increased after 2010, the continuous changes of CCD characteristics from the HFI significantly lagged type to the HQ lagged. HFI showed an inverted U‐shaped relationship with CCD. The CCD peaks during 2000–2020 corresponded to HFI decreasing from 0.711 to 0.566. This indicates that the risk of decoupling between human activities and HQ gradually increased. Furthermore, CCD levels and characteristics in different bivariate clusters exhibited varying changes over time. These results reveal that the spatiotemporal dislocation between urbanization and ecological restoration induced the spatial nonstationarity of the coupling relationship between human activities and HQ. Urbanization exacerbates the imbalance between human development and biodiversity conservation. Therefore, we suggest reasonably delimiting urban boundaries, controlling the scale of urban sprawl, and strengthening biodiversity protection in areas undergoing rapid urbanization. In addition, we advocate for the division of ecological barrier zones, urban development buffer zones, and urban built‐up areas, each with tailored management and protection measures. Our findings can provide an important reference for the ecological restoration of urban agglomerations.

How do soil and topographic drivers determine tree diameter spatial distribution in even aged cork oak stands installed in average to high productivity areas

AbstractLocal terrain or microsite conditions influence the development of trees, particularly at early ages. These conditions might be described by edaphic or topographic variables. We mapped soil and topographic variables from four even-aged and even-spaced cork oak plantations located in two climatically distinct Portuguese regions. The major goal of this research was to understand the relation between soil and topographic fine-scale conditions and tree growth expressed by diameter without cork annual growth (idu). The methodology consisted in (1) analysing the spatial variability and autocorrelation of idu; (2) modelling idu with ordinary least squares (OLS) regressions; (3) comparing with spatial modelling of idu, incorporating spatial autocorrelation. The driest stands A and B, exhibited weaker spatial autocorrelation, distributed in smaller clusters (R2 < 0.03, OLS models), while stands C (R2 = 0.18, OLS models) and D (R2 = 0.11, OLS models) showed higher predictive capacity. Spatial models increased R2 scores, keeping most variables from OLS models and accounting for spatial autocorrelation. A + B + C + D OLS model obtained an R2 = 0.34 and respective spatial model R2 = 0.58. Apparent electrical conductivity at 0.5 (ECa0.5) and 1 m of soil depth, slope, elevation and topography position index were included as predictors (OLS), but only ECa0.5, slope and elevation were selected in the spatial model. Models were fitted using average to high productivity stands and should be used cautiously outside this range. Local terrain conditions determine the growth of young cork oak trees. Mapping soil and topographic variables before establishing new plantations may identify limiting microsite conditions where using cork oak species is not suitable due to low growth rates expectations.

Evolution Model and Driving Mechanism of Urban Logistics Land: Evidence from the Yangtze River Delta

Logistics land is the spatial carrier for the development of logistics enterprises. Its evolution mode and driving mechanism determine the level of high-quality development of the logistics industry, and serve as an important basis for urban planning and territorial spatial planning. This study introduced a Boston consulting group (BCG) matrix and geographically weighted regression (GWR) spatial econometric models to carry out empirical research on the Yangtze River Delta (YRD), in an effort to provide scientific information for evidence-based decision-making by governments and enterprises. The scale and ratio of logistics land (LLS and LLR) in the YRD showed significant spatial heterogeneity and autocorrelation, cities with large logistics land use converging from clusters to belts from 2000 to 2020, and agglomerations with high logistics land ratio (LLR) migrating from inland to coastal areas. Diversified models of logistics land evolution also emerged, such as high scale–high speed cities, low scale–low speed cities, high scale–low speed cities, and low scale–high speed cities. In addition, the driving mechanism of LLS and LLR was very complex, with a great difference in the intensity, nature and spatial effects of the influence of different factors. The inspiration from empirical case studies is urgent to revise the planning norms and clarify the LLS and LLR control standards for logistics land use. Meanwhile, the synergistic development target of the logistics industry in the new era is changing from the manufacturing industry to the commerce and trade industry; the establishment of planning zoning and the designing of differentiated management policies significantly improve the planning applicability.

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.

SAN: Mitigating spatial covariance heterogeneity in cortical thickness data collected from multiple scanners or sites.

In neuroimaging studies, combining data collected from multiple study sites or scanners is becoming common to increase the reproducibility of scientific discoveries. At the same time, unwanted variations arise by using different scanners (inter-scanner biases), which need to be corrected before downstream analyses to facilitate replicable research and prevent spurious findings. While statistical harmonization methods such as ComBat have become popular in mitigating inter-scanner biases in neuroimaging, recent methodological advances have shown that harmonizing heterogeneous covariances results in higher data quality. In vertex-level cortical thickness data, heterogeneity in spatial autocorrelation is a critical factor that affects covariance heterogeneity. Our work proposes a new statistical harmonization method called spatial autocorrelation normalization (SAN) that preserves homogeneous covariance vertex-level cortical thickness data across different scanners. We use an explicit Gaussian process to characterize scanner-invariant and scanner-specific variations to reconstruct spatially homogeneous data across scanners. SAN is computationally feasible, and it easily allows the integration of existing harmonization methods. We demonstrate the utility of the proposed method using cortical thickness data from the Social Processes Initiative in the Neurobiology of the Schizophrenia(s) (SPINS) study. SAN is publicly available as an R package.

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 .

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.

Synergistic regional emission reductions in China: Network evolution, spatial and temporal characteristics, and driving factor

Synergistic regional emission reduction (SRER) is an important part of the strategy to achieve carbon neutrality. In this paper, a system efficiency value-added model is proposed to measure the strength of SRER network relationships for 40,186 sets of city pairs and the SRER levels of 284 cities in China from 2011 to 2021. Subsequently, the evolution characteristics of the SRER network, the spatial and temporal characteristics of urban SRER levels, and the driving factors of the SRER network evolution are further analyzed. The results are as follows: SRER network is shifting from a fan-shaped network pattern of “three-region clustering and sporadic coverage” to a shuttle-shaped nested network pattern of “east-west linkage and multi-region support”. Time series analysis found that urban SRER level in China showed fluctuating growth characteristics during the study period. Spatial analysis reveals that the spatial autocorrelation of urban SRER level is decreasing, and the spatial distribution range is expanding towards the southwest. Emission reduction comparative advantages (CAER), green innovation division of labor (GIDL) and urban economic linkages (LCEL) are driver factors of SRER network evolution. This study proposes an evaluation method for the urban SRER level from network relationships perspective. Subsequently, network evolution characterization, spatial and temporal characterization, and driving factor analysis were conducted. The results of the study provide important theoretical guidance and inspiration for grasping the current status and enhancement paths of China's urban SRER level.

Sexual violence against children and adolescents in Paraná State: geospatial analysis and main socioeconomic indicators

ObjectiveChild sexual violence is a multidimensional problem of many contemporary societies, affecting people of all sexes, social stratum and age groups. Offenses involving children and adolescents are more serious, given their total or partial dependence on parents and caregivers. Information on child sexual violence in Brazil is found in raw form and without detail. The objective was to compare the information with social and economic data in the state of Paraná. MethodsThe authors conducted a retrospective study of secondary data from the Notifiable Diseases Information System (SINAN) on cases of sexual violence involving victims aged 0 to 19 years. Results are presented according to notification characteristics. The authors applied exploratory spatial data analysis to assess spatial autocorrelations and investigated relationships by the ordinary least squares regression model. ResultsBetween 2017 and 2021, there were 13,403 reports of child sexual violence in Paraná State, Brazil. Most victims (82.8%) were female and aged between 10 and 14 years. The majority of sexual violence cases (67.8%) occurred in the home environment. The highest rates on a population basis were observed in the North Central and Greater Curitiba regions, mainly in cities with higher population density and with higher rates of other types of violence. ConclusionThe results provide data that can promote a broader understanding of the distribution of sexual violence and the state and associated variations. It is expected to improve the provision of care for victims of child sexual violence and assist in strategic planning to prevent future offenses.

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 > 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

Refining Ixodes scapularis (Acari: Ixodidae) distribution models: a comparison of current methods to an established protocol.

Blacklegged ticks (Ixodes scapularis Say) pose an enormous public health risk in eastern North America as the vector responsible for transmitting 7 human pathogens, including those causing the most common vector-borne disease in the United States, Lyme disease. Species distribution modeling is an increasingly popular method for predicting the potential distribution and subsequent risk of blacklegged ticks, however, the development of such models thus far is highly variable and would benefit from the use of standardized protocols. To identify where standardized protocols would most benefit current distribution models, we completed the "Overview, Data, Model, Assessment, and Prediction" (ODMAP) distribution modeling protocol for 21 publications reporting 22 blacklegged tick distribution models. We calculated an average adherence of 73.4% (SD ± 29%). Most prominently, we found that authors could better justify and connect their selection of variables and associated spatial scales to blacklegged tick ecology. In addition, the authors could provide clearer descriptions of model development, including checks for multicollinearity, spatial autocorrelation, and plausibility. Finally, authors could improve their reporting of variable effects to avoid undermining the models' utility in informing species-environment relationships. To enhance future model rigor and reproducibility, we recommend utilizing several resources including the ODMAP protocol, and suggest that journals make protocol compliance a publication prerequisite.

Analysis of the Coupling Coordination between the Ecosystem Service Value and Urbanization in the Circum-Bohai-Sea Region and Its Obstacle Factors

In light of the tremendous pressure for improving urbanization levels and expanding construction land on ecosystems, regional sustainable development is premised on the coordinated development of the ecological environment and urbanization. This paper focuses on the Circum-Bohai-Sea Region and assesses the ecosystem service value (ESV) and urbanization level using the equivalence factor and entropy-weighted TOPSIS methods. Based on these assessments, it examines the coupling coordination between the ESV and urbanization as well as the obstacles to this coordination, utilizing the coupling coordination degree model, spatial autocorrelation model, and obstacle model. The results revealed several key findings. First, the Circum-Bohai-Sea Region experienced a continuous improvement in the urbanization level from 2005 to 2020, which presented a “center–periphery” spatial pattern. Overall, the ESV exhibited an N-shaped trend, namely, initially increasing, then decreasing, and then rapidly increasing. The ESV per unit of area exhibited a spatial difference, with the boundary of Baoding and Cangzhou as the dividing line. The ESV per unit of area in the northern parts was higher than that in the southern parts. Second, the degree of the coupling coordination between the ESV and urbanization exhibited an inverted U-shaped trend, initially increasing and then slightly decreasing. It also exhibited significant spatial heterogeneity, with the degree of the coupling coordination in the northern parts being higher than that in the southern parts and that in the central parts being higher than those in the eastern and western parts. Additionally, the ESV showed a significant spatially positive correlation with urbanization. H-H clusters were predominantly found in central and southern Liaoning Province, while L-L clusters were primarily located in southern Hebei Province and western Shandong Province. Third, the obstacle factors remained stable during the study period. The top five obstacles in the ESV system were the water resource supply, nutrient cycling maintenance, raw material production, aesthetic landscape, and food production. Moreover, the top five obstacles in the urbanization system were the number of college students per 10,000 people, population density, number of health technical personnel per 10,000 people, per capita road area, and proportion of secondary and tertiary industry output values.

Spatial Heterogeneity in Determinants of No Condom Use at First Sex in South Africa

Background Condom use, especially at first sex for unmarried people, is an important behaviour that reduces the risk of HIV transmission, and other Sexually Transmitted Diseases (STDs), as well as unwanted pregnancies. In reviewing the literature, little information was found to elucidate the spatial context of condom use among people who had an early sexual debut. Understanding factors that influence no condom use at first sex in different local or geographical areas can add value in addressing these risky first-sex practices and minimise the risk of getting HIV, STDs, and unwanted pregnancies in South Africa, especially among young people. Therefore, this paper aimed to determine the spatial heterogeneity of factors associated with no condom use at first sex in South Africa using Geographically Weighted Regression (GWR) with Poisson distribution and local bivariate relationships. Methods This article used data from the fifth South African National HIV Prevalence, HIV Incidence, Behaviour and Communication Survey (SABSSM V) of 2017. Spatial autocorrelation, GWR using Poisson distribution, and local bivariate relationships were undertaken using ArcGIS Pro. Results Geographically weighted regression using Poisson distribution results highlighted being female, being young, having never married, being unemployed, having sexual debut at a younger age, having two or more sexual partners, having no risk of being infected with HIV, and having good Self-perceived Health (SPH) to be significant factors influencing no condom use at first sex, with pseudo R2 = 0.96 and AICc = 692. Local bivariate relationships showed the relationship between no condom use at first sex and most explanatory variables to exhibit a positive linear relationship in most districts with the exception of having never married, which showed a positive linear relationship in only 18 districts, while 33 districts showed a concave relationship. These relationships were all significant with p < 0.05. Conclusion This localised analytical approach may contribute to the development of geo-targeted interventions and policies tailored to specific regions within South Africa, thereby improving the effectiveness of efforts to promote safe first sexual practices. Educational programs targeting youth should be developed to further raise awareness of the risks associated with unprotected sex and the importance of condom use. More efforts should be made to break the barriers, such as sexual relationship power, gender, and sociocultural and religious influences, which tend to put young, never-married, and unemployed women at risk of no condom use at first sexual debut. It is further recommended that the type of relationship between no condom use at first sex and each explanatory factor at the local region be considered during geo-targeted interventions as this will assist in the type of intervention and duration of intervention.

A spatiotemporal comprehensive constraint interpolation method for missing values in monthly composite Suomi-NPP VIIRS Cloud Mask images

ABSTRACT The Earth’s nighttime light imaging data collected by the Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB) are severely lost in summer, hindering the further application of VIIRS DNB data. In this article, a spatiotemporal comprehensive constraint interpolation method (SCIM) that accounts for the relative stationarity of temporal nighttime light, spatial correlations and heterogeneity among pixels is designed. Meanwhile, the method considers the seasonal fluctuations in nighttime light radiation, introduces spatial autocorrelation coefficients and spatial heterogeneity coefficients, and solves the problem of missing VIIRS DNB data. First, 8 widely used temporal interpolation methods (TIM) are selected to perform temporal interpolation for missing image data. Second, the existing VIIRS monthly images are used to construct the constraint relationship between time and space, and the pixels that do not meet the constraint conditions in the TIM simulation image are removed. The inverse distance weight (IDW) method is applied to the removed null pixels to obtain the image simulated by the SCIM. Finally, the accuracy of the TIM and SCIM is evaluated. The results show that the accuracy of the SCIM-simulated image is better than that of the TIM-simulated image. The proposed approach improves the quality of the VIIRS DNB dataset.

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.

Wildfires and climate justice: future wildfire events predicted to disproportionally impact socioeconomically vulnerable communities in North Carolina

Wildfire events are becoming increasingly common across many areas of the United States, including North Carolina (NC). Wildfires can cause immediate damage to properties, and wildfire smoke conditions can harm the overall health of exposed communities. It is critical to identify communities at increased risk of wildfire events, particularly in areas with that have sociodemographic disparities and low socioeconomic status (SES) that may exacerbate incurred impacts of wildfire events. This study set out to: (1) characterize the distribution of wildfire risk across NC; (2) implement integrative cluster analyses to identify regions that contain communities with increased vulnerability to the impacts of wildfire events due to sociodemographic characteristics; (3) provide summary-level statistics of populations with highest wildfire risk, highlighting SES and housing cost factors; and (4) disseminate wildfire risk information via our online web application, ENVIROSCAN. Wildfire hazard potential (WHP) indices were organized at the census tract-level, and distributions were analyzed for spatial autocorrelation via global and local Moran’s tests. Sociodemographic characteristics were analyzed via k-means analysis to identify clusters with distinct SES patterns to characterize regions of similar sociodemographic/socioeconomic disparities. These SES groupings were overlayed with housing and wildfire risk profiles to establish patterns of risk across NC. Resulting geospatial analyses identified areas largely in Southeastern NC with high risk of wildfires that were significantly correlated with neighboring regions with high WHP, highlighting adjacent regions of high risk for future wildfire events. Cluster-based analysis of SES factors resulted in three groups of regions categorized through distinct SES profiling; two of these clusters (Clusters 2 and 3) contained indicators of high SES vulnerability. Cluster 2 contained a higher percentage of younger (<5 years), non-white, Hispanic and/or Latino residents; while Cluster 3 had the highest mean WHP and was characterized by a higher percentage of non-white residents, poverty, and less than a high school education. Counties of particular SES and WHP-combined vulnerability include those with majority non-white residents, tribal communities, and below poverty level households largely located in Southeastern NC. WHP values per census tract were dispersed to the public via the ENVIROSCAN application, alongside other environmentally-relevant data.

Temporal changes in spatial scale and autocorrelation structure of forest openings based on taxonomic, phylogenetic, and functional turnover

ContextDuring European settlement, Illinois grasslands were converted for agricultural purposes. Remaining natural areas in southern Illinois include natural xeric forest openings, with communities representative of remnant grasslands and adjacent hardwood forests. Previous research in these openings shows plant communities are driven by edaphic conditions. ObjectivesThe first objective aimed to characterize spatial scale and autocorrelation structure of these openings based on climatic, environmental, and diversity variables. The second objective was to predict taxonomic, phylogenetic, and functional turnover between 1988 and 2019, using climatic and environmental variables. MethodsSurveys were conducted to calculate taxonomic, phylogenetics and functional trait metrics and analyses of these dimensions of diversity. Randomization tests were used to assess phylogenetic and functional clustering and over-dispersion at each site. Spatially-explicit climatic and environmental variables were included from earlier surveys and data repositories. Global Moran's I and spatial autocorrelograms were used to assess spatial structure of climatic, environmental, and diversity variables and generalized dissimilarity modeling was used to characterize taxonomic, phylogenetic, and functional turnover based on environmental variables. ResultsSoil depth was the only environmental variable which exhibited significant global spatial autocorrelation. Overall, sandstone sites were phylogenetically over-dispersed while loess sites were phylogenetically clustered. Climate variables and diversity metrics exhibited significant spatial structure during surveys. Generalized dissimilarity models showed that geographic distance between openings was the most influential driver of turnover across surveys. ConclusionsPrevious glacial events explained the spatial structure of soil depth across sites, due to Quaternary loess deposition in loess sites. High diversity values were clustered in the southeastern portions of the study area. Functional generalized dissimilarity models best predicted turnover in these openings compared to taxonomic and phylogenetic models.