Spatial Regression Research Articles

Sign‐flip inference for spatial regression with differential regularisation

SummaryWe address the problem of performing inference on the linear and nonlinear terms of a semiparametric spatial regression model with differential regularisation. For the linear term, we propose a new resampling procedure, based on (partial) sign‐flipping of an appropriate transformation of the residuals of the model. The proposed resampling scheme can mitigate the bias effect induced by the differential regularisation. We prove that the proposed test is asymptotically exact. Moreover, we show, by simulation studies, that it enjoys very good control of Type‐I error also in small sample scenarios, differently from parametric alternatives. Additionally, we show that the proposed test has higher power with respect than recently proposed nonparametric tests on the linear term of semiparametric regression models with differential regularisation. Concerning the nonlinear term, we develop three different inference approaches: a parametric one and two nonparametric alternatives. The nonparametric tests are based on a sign‐flip approach. One of these is proved to be asymptotically exact, while the other is proved to be exact also for finite samples. Simulation studies highlight the good control of Type‐I error of the nonparametric approaches with respect the parametric test, while retaining high power.

Crime against women in India: district-level risk estimation using the small area estimation approach.

The global prevalence of crimes against women has made it an enduring public health challenge that has persisted over time. The achievement of the 2030 Sustainable Development Goal (SDG) is intricately tied to the actions taken to prevent these crimes as their repercussions directly affect progress across various SDGs. This study aimed to provide a comprehensive examination of the prevalence of crimes against women across districts and states in India, analyzing changes from 2020 to 2022, and subsequently identifying associated factors. The study is an ecological analysis conducted across all districts of India using the data on crimes against women for the period 2020 and 2022 obtained from the National Crime Records Bureau (NCRB) of India. A small area estimation method was used to obtain district-level relative risks of crime against women for both periods. Hotspot analysis was carried out to identify the current hotspots and coldspots. Further spatial regression was used to identify the factors associated with crimes against women in the year 2022. The results indicated a rise in the reported crime against women cases between 2020 and 2022. The rate of crimes against women at the national level was found to be 57 in the year 2020, whereas, in 2022, it increased to 67. The highest crime rate in the year 2022 was found to be 145 in Delhi, while Nagaland had the lowest crime rate of 5. The relative risk of crime against women varied from 0.046 to 4.68 in 2020, while in 2022, it spanned from 0.02 to 6.10. Significant hotspots were found in parts of Rajasthan, Madhya Pradesh, Haryana, Telangana, and Odisha. The results of the spatial error regression model showed that the sex ratio and the population density of the district have significant associations with the occurrence of crimes against women. The rise in the incidence of crime against women emphasizes the importance of tackling the spatial inequality in relative risk across Indian districts. By thoughtfully addressing this variation and conducting targeted studies in high-risk areas, we can enhance our understanding of the obstacles to implementing effective measures against violence targeting women.

The Impact of Green Innovation on CO2 Emissions in China: Evidence from Spatial Regression Model

Using data from USPTO, CEADs, the Chinese Statistical Yearbook, and spatial econometric models for the panel of the 30 Chinese provinces for the period 2000∼2019, this study explores and compares the impact of green and non-green innovation on CO2 emissions. The findings reveal a significant spatial correlation in CO2 emissions across provinces, validating the appropriateness of spatial econometric models for this analysis. The results indicate that both green and non-green innovations significantly reduce CO2 emissions, with green innovation having a more pronounced effect despite its lower prevalence. Additionally, patents and their citations notably enhance environmental quality in neighboring provinces through indirect effects. The empirical results highlight that China should focus on: (1) spatial spillover effects of technological, especially green, innovation; (2) removing barriers to green patent invention and citation; (3) transitioning from an energy-driven to a green innovation-driven economy; and (4) enhancing environmental quality by mitigating CO2 emissions.

Impact of Spatial Characteristics on Gendered Retail Consumption in Seoul: A Gender-Sensitive Urban Planning Perspective

This study examines the impact of spatial characteristics on gendered retail consumption in Seoul, South Korea, providing empirical evidence for gender-sensitive urban planning. Gender-sensitive urban planning integrates gender perspectives into all stages of urban development, aiming to address the diverse needs and experiences of all genders spatially. While existing research has predominantly focused on gender differences in labor participation, this study shifts the focus to retail consumption, which is a critical aspect of daily life. Our research analyzes the spatial attributes of urban spaces at the neighborhood scale and their influence on aggregated retail consumption by gender. The aggregated retail sales by census output area (jipgyegu) represent the aggregated retail consumption. Utilizing spatial regression methods, this study identifies significant spatial autocorrelations and clustering patterns in retail sales data. The findings reveal that traditional markets, less-developed commercial areas, and specific retail sector (retailing, medical, and educational services) densities positively impact SMW (subtraction of men’s retail sales from women’s retail sales), while city center areas, developed commercial districts, special tourism zones, and specific retail sector (restaurants and entertainment) densities have negative impacts.

Local Walsh-average regression for spatial autoregression single index varying coefficient models

. In real life, a large number of variables are spatially correlated in adjacent regions in various fields, such as finance, sociology, ecology, and geographic information systems. And the factors affecting these variables often have both linear and non linear relationships with them, how to accurately depict the relationship between them with a spatial regression model is a significant work. In order to further accurately describe the relationship between explanatory variables and response variables, we establish a spatial autoregressive single index varying coefficient model. The least squares method is the main method of model estimation but it is not robust enough when the parameter distribution is not normal. We present a new estimation method based on local Walsh average-regression. Under some general assumptions, we establish the asymptotic properties of the parametric and non parametric estimators. We further verify the accuracy and efficiency of the estimators under the condition of non normal error distribution through numerical experiments. In addition, the robustness of the proposed method is verified when the spatial weight matrix is interfered and when the bandwidth is changed within a certain range. Finally, we demonstrate the accuracy of forecasting by our proposed model compared with traditional spatial autoregressive model through real data analysis.

Spatial spillover effect of the enterprise sector on local residents’ income in Vietnam

AbstractThis study uses a panel data set from all 63 provinces in Vietnam and spatial regression models to investigate the spatial spillover effect of the enterprise sector on local residents’ income. The results show that expanding the enterprise sector can directly and indirectly increase local income. Although this spillover effect is positive and significant, it is smaller than the direct effect and gradually diminishes when the distance between provinces increases. For example, the estimation results applying the spatial Durbin model holding other variables constant show that a 10% increase in the enterprise capital volume in Ho Chi Minh City can raise local total income in this city, and first- and second-order neighbours by 0.53%, 0.22%, and 0.17%, respectively. This study provides novel evidence on the spatial spillover effect of the enterprise sector on local incomes using spatial econometric models. The findings offer valuable information for policymakers in planning enterprise sector development to improve local residents’ income in Vietnam.

Analyzing effects of environmental indices on satellite remote sensing land surface temperature using spatial regression models

Analyzing effects of environmental indices on satellite remote sensing land surface temperature using spatial regression models

Optimizing Urban Green Spaces for Air Quality Improvement: A Multiscale Land Use/Land Cover Synergy Practical Framework in Wuhan, China

Air pollution, particularly fine particulate matter (PM2.5), poses a significant health risk, especially in high-density urban areas. Urban green space (UGS) can effectively mitigate this pollution. Despite their potential, strategies for effectively leveraging Land Use/Land Cover (LULC) optimization to combat PM2.5 remain largely unexplored. Ordinary least squares (OLS), geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR) were employed to investigate the spatial heterogeneity relationship between UGS conversion and PM2.5 fluctuations across various scales and evolutionary stages, developing a multiscale practical framework for LULC synergy in combating air pollution. The areas of UGSs to/from other LULCs, PM2.5 concentrations and corresponding variation zones exhibited significant spatial clustering. These UGS conversions explained more than 65% of the PM2.5 changes in the study area, peaking at 76.4% explanatory power in the fourth stage. Compared to global spatial analysis (OLS: 0–0.48), local spatial regression analysis significantly improved the R2 value (GWR: 0.32–0.75, MGWR: 0.48–0.90), but the fitting quality of local spatial regression analysis decreased with increasing scale, highlighting the importance of scale diagnosis. A 2 km scale was identified as optimal for assessing the spatial heterogeneity impact of UGS and other LULC conversions on PM2.5 changes. Conversion areas from water bodies and bare land to UGSs maintain stable local spatial properties at this scale (bandwidths: 44–99). Our research provides new insights into LULC management and planning, offering a coordinated approach to mitigating urban air pollution. Additionally, a practical framework was established for addressing spatially continuous variables such as PM2.5, revealing effective approaches for addressing urban environmental issues.

Selection of optimal knot point and best geographic weighted on geographically weighted spline nonparametric regression model

This study proposes the development of a nonparametric regression model combined with geographically weighted regression. The regression model considers geographical factors and has a data pattern that does not follow a parametric form to overcome the problem of spatial heterogeneity and unknown regression functions. This study aims to model provincial food security index data in Indonesia with the GWSNR model, so finding the optimal knot point and the best geographic weighting is necessary. We propose the selection of optimal knot points using the Cross Validation (CV) and Generalized Cross Validation (GCV) methods. The optimal knot point will control the accuracy of the regression curve as we also consider the MSE value in showing the ability of the model. In addition, we determine the best geographic weighting and test the significance of the model parameters. We demonstrate the GWSNR model on food security index data. The best GWSNR model uses the Gaussian kernel weighting function and selects the optimal knot point as one-knot point based on the lowest CV and GCV values. Simultaneous and partial parameter test results show that there are 10 area classifications with different effects on each group of classification results. Some of the highlights of the proposed approach are:•The method is the development of a nonparametric regression model with geographic weighting, which combines nonparametric and spatial regression in modeling the national food security index.•There are three-knot points tested in nonparametric truncated spline regression and three geographic weightings in spatial regression. Then the optimal knot point and best bandwidth are determined using Cross Validation and Generalized Cross Validation.•This article will determine regional groupings in Indonesia in 2022 based on significant predictors in modeling the national food security index numbers.

The impact of regional disparities on the availability of meningococcal vaccines in the US

BackgroundIn the United States (US), three types of vaccines are available to prevent invasive meningococcal disease (IMD), a severe and potentially fatal infection: quadrivalent conjugate vaccines against serogroups A, C, W, Y (MenACWY), and monovalent vaccines against serogroup B (MenB) as well as a newly licensed pentavalent vaccine (MenABCWY) protecting against serogroup A, B, C, W, and Y. The CDC’s Advisory Committee on Immunization Practices (ACIP) routinely recommends MenACWY vaccine for all 11- to 12-year-olds with a booster dose at 16 years. MenB vaccination is recommended based on shared clinical decision-making (SCDM) for 16- to 23-year-olds. Recently, the pentavalent meningococcal vaccine (MenABCWY) was recommended by the ACIP. Meningococcal vaccine uptake is suboptimal across the country, particularly among individuals with lower socioeconomic status (SES), despite these recommendations. The objective of the spatial analyses was to assess the relationship between stocking of MenACWY and MenB vaccines, area-level SES, and state-level policies.MethodsThe number of MenACWY and MenB doses stocked by vaccinators was obtained from IQVIA and the CDC’s Vaccine for Children (VFC) program and compiled into a county-level dataset from 2016 to 2019. SES, as measured using the CDC’s Social Vulnerability Index (SVI), state-level school recommendations, and universal purchasing programs were among the main county-level covariates included to control for factors likely influencing stocking. Data were stratified by public and private market. Bayesian spatial regression models were developed to quantify the variations in rates of stocking and the relative rates of stocking of both vaccines.ResultsAfter accounting for county-level characteristics, lower SES counties tended to have fewer doses of MenB relative to MenACWY on both public and private markets. Lower SES counties tended to have more supply of public vs. private doses. Universal purchasing programs had a strong effect on the markets for both vaccines shifting nearly all doses to the public market. School vaccination strategy was key for improving stocking rates.ConclusionsOverall, the results show that MenACWY has greater stock relative to MenB across the US. This difference is exacerbated in vulnerable areas without school entry requirements for vaccination and results in inequity of vaccine availability. Beyond state-level policy and SES differences, SCDM recommendations may be a contributing factor, although this was not directly assessed by our model.

Rapid land cover change in agricultural valleys of the Atacama Desert, Chile

The profound landscape modification produced by agriculture is considered one of the main threats to biodiversity worldwide. Although the main ecological impacts are likely due to agricultural expansion into natural areas, changes in crop types and production intensification also induce significant ecological changes. We studied the land cover change process in four agricultural valleys (Lluta, Azapa, Vítor and Camarones) in the Atacama Desert of Chile between 2003 and 2019, divided into four equal periods. Using spatial data and logistic regression, we assessed the driving factors of land cover change. The Azapa valley showed the highest rate of land cover change, reaching 36.4% of netting and greenhouses landscape cover by the end of the study, and 50.4% annual rate of increase during the 2007–2011 period. Intensification was observed as the anti-aphid netting cover expanded mainly in Azapa and in the last four years in Vítor and Lluta valleys. Most land cover change was related to low slopes and elevation or sites closer to roads or surrounded by already cultivated land, like agriculture expansion on barren soil or native shrubland loss. High population density was significantly correlated with land cover change (p < 0.01). If land cover change into netting continues as observed, it is possible that landscape homogenization occurs in the short term in almost all valleys. Anyway, disentangling how landscape change works and the involved drivers, could be useful to understand landscape simplification in arid lands that are under an agricultural intensification process.

Housing price determinants in Ecuador: a spatial hedonic analysis

Purpose This paper aims to compare the efficiency of spatial and nonspatial hedonic price models in capturing housing submarkets dynamics for cities in developing countries. This study expects to contribute to a better understanding of the housing price determinants from both nonspatial and spatial perspectives. In addition, this paper fills a gap in the literature on the study of housing prices from a spatial perspective in Latin American cities. Design/methodology/approach This study uses a comparative analysis between an ordinary least squares regression and a geographical weighted regression, GWR. The study also assesses the performance of two distinct data sources: the city’s cadastral records and a real estate sales web portal. Findings The results suggest that compared to the traditional regression model, the spatial regression models are more effective at capturing housing market variations on a fine scale. Moreover, they reveal interesting findings on the spatial varying, sometimes contradictory effects of some housing attributes on housing prices in different areas of the city, suggesting the potential impact from segregation. Research limitations/implications The availability of data on housing prices and characteristics in Latin American cities is fragmented and complex. The level of detail, granularity and coverage is not consistent over time. For this reason, this study combines and compares data sets from official and unofficial sources in an effort to close this gap. Likewise, the socioeconomic variables that come from the census must be carefully analyzed, knowing the historical context in which they were constructed, what they represent and their interpretation. Practical implications This paper suggests that despite the improvement on the spatial models, the selection of a specific one should always be based on the diagnosis of it as it highly depends on the data used and the objectives of the study. Originality/value This study enriches the limited body of literature on spatial hedonic price models of housing in Latin American cities. It also shed light on the importance of spatial approaches to identify complex housing submarkets.

Incorporating spatial autocorrelation in dasymetric mapping: A hierarchical Poisson spatial disaggregation regression model

The growing demand for spatially detailed population products in various fields continues to rise, as users shift their focus from aggregated areal totals to high-resolution grid estimates. Aggregating demographic data to areas, such as census tracts or block groups, can mask localized heterogeneities within those areas. This paper presents a new pycnophylactic (density-preserving) geospatial model for disaggregating population to high-resolution grids. We describe a Bayesian Hierarchical Poisson Spatial Disaggregation Regression Model (HPSDRM), which incorporates land cover covariates and two levels of spatial autocorrelation. We evaluated the model's predictive ability first with simulation studies, and then by disaggregating census population data for Davidson County, TN, from the census tract-level to a fine grid and comparing predicted to actual block-level population counts. The interpolated population map successfully identified spatial heterogeneities, such as hot- and cold-spots within census tracts. The HPDSRM model out-performed three other types of disaggregation modeling, which suggests the value of incorporating spatial autocorrelation. Based upon this study, HPSDRM has potential for disaggregating other demographic data, such as socioeconomic indicators.

Research on the carbon reduction effect of regional industrial transfer in China -- based on the perspective of factor flow

Industrial transfer plays a crucial role in regional carbon reduction efforts in China. This study conducted a systematic analysis of the carbon reduction impact of industrial transfer, focusing on factor flow, and performed empirical tests using Chinese provincial data from 2003 to 2019 with a spatial regression model. The key findings are as follows: (1) Industrial transfer significantly influences carbon emissions, with varying effects depending on the factor flow. Capital and technology flows contribute to carbon emission reduction, while labor flow generally has a negligible impact. (2) Over time, the environmental impact of capital and technology flows intensifies, whereas the influence of labor flow diminishes. (3) Regional variations exist in the impact of industrial transfer on carbon emissions based on the factor flow perspective.

Driving Factors of NOx Emissions in China: Insights from Spatial Regression Analysis

China’s rapid industrialization and urbanization have led to significant nitrogen oxide (NOx) emissions, contributing to severe atmospheric pollution. Understanding the driving factors behind these emissions is crucial for effective pollution control and environmental management. Therefore, this study is an attempt to provide insights into the influence of socioeconomic factors and explore spatial dependencies of NOx emissions in China in 2022 employing spatial regression models (SRMs). Among the SRMs considered, the spatial Durbin model (SDM) is identified as the most suitable for analyzing regional NOx emissions. The study highlights the importance of controlling electricity consumption and vehicle emissions for addressing air pollution in Chinese regions. Specifically, a one billion kilowatt-hour increase in electricity consumption leads to approximately 549.6 tons of NOx emissions, and an increase of 1000 vehicles in a region results in an average increase of 7113.4 tons of NOx emissions in the same region. Furthermore, per capita consumption expenditure (PCEXP) and research and development (R&amp;D) expenditure exhibit negative direct and spillover impacts. Contrary to previous studies, this research finds that changes in urban population density do not have a significant direct or indirect effect on NOx emissions within the studied areas. Moreover, we conducted additional investigations to assess the effectiveness of government action plans in reducing NOx emissions. Specifically, we evaluated the impact of Phases 1 and 2 of the Clean Air Action Plan, launched in 2013 and 2018, respectively, on the socioeconomic drivers of NOx emissions. Therefore, the data were modeled for the years 2013 and 2017 and compared to the results obtained for 2022. The findings indicate that over the entire period (2013–2022), the emission controls mandated by the action plan resulted in significant reductions in the impact of many of the studied NOx drivers. In conclusion, based on the results, this study presents recommendations to mitigate NOx emissions.

Spatial Analysis of Regional Poverty Rates in Bali for the period 2016-2020

Poverty is a severe issue for local governments, particularly in Bali. This study aims to investigate factors influencing the regional poverty rate in Bali Province between 2016 and 2020. In the primary data obtained from BPS Bali Province, spatial regression data approaches and analyses were used. The findings show that housing, access to technology, and availability of natural resources affect poverty rates. The poverty line in Bali in the period 2016–2020 fluctuated significantly. Urban and rural poverty levels are affected by the availability of rice. At the same time, non-food commodities are higher in rural areas regarding housing, religious ceremonies, and customs. The contribution of the food poverty line to the urban poverty line was 68.76% less than the contribution of the food poverty line to the rural poverty line, which was 69.74%. From 2016 to 2020, the districts of Buleleng, Karangasem, Klungkung, and Tabanan each had the highest poverty depth index. The poverty depth index in urban areas reached 0.653, and in rural areas, 0.753. The severity index for urban poverty is 0.154, which is lower than the severity index for rural poverty, which is 0.156.

Testing for spatial correlation under a complete bipartite network

This note shows that for a spatial regression with a weight matrix depicting a complete bipartite network, the Moran I test for zero spatial correlation is never rejected when the alternative is positive spatial correlation no matter how large the true value of the spatial correlation coefficient. In contrast, the null hypothesis of zero spatial correlation is always rejected (with probability one asymptotically) when the alternative is negative spatial correlation and the true value of the spatial correlation coefficient is near −1.

Sorrow under prosperity: how downtown residence influences the welfare of disabled people in China

ABSTRACT While the welfare divergence between rural and urban disabled residents in China has attracted great scholarly attention, little to no research has examined the relevant disparities within the city. Due to the administrative-led resource allocation model in China, disabled individuals residing in the urban centre are expected to have a greater advantage in their daily lives compared to their counterparts in the suburbs. We use Tianjin, a Chinese municipality, as an example to explore the impact of disabled individuals’ downtown residence on their employment, education, marriage, and access to public services. Our spatial regression discontinuity estimates reveal that compared to suburban disabled residents, downtown residents have easier access to education but face difficulties in employment and marriage. A follow-up survey indicates that disabled residents in downtown areas face employment and marriage disadvantages due to implicit exclusion of low-skilled groups and high marriage burdens in urban centres. This paper sheds light on the welfare of disabled groups in cities and the latent impact of urban development on social minorities.

Spatial and Temporal Dynamics of Chikungunya Incidence in Brazil and the Impact of Social Vulnerability: A Population-Based and Ecological Study.

To assess the temporal and spatial dynamics of chikungunya incidence and its association with social vulnerability indicators in Brazil, an ecological and population-based study was conducted herein, with confirmed cases of chikungunya and based on clinical and clinical-epidemiological criteria from 2017 to 2023. Data were obtained from the Notifiable Diseases Information System and social vulnerability indicators were extracted from the official platform of the United Nations Development Program and the Social Vulnerability Atlas. Temporal, spatial, and global spatial regression models were employed. The temporal trend showed that in 2017, the incidence increased by 1.9%, and this trend decreased from 2020 to 2021 (-0.93%). The spatial distribution showed heterogeneity and positive spatial autocorrelation (I: 0.71; p < 0.001) in chikungunya cases in Brazil. Also, the high-risk areas for the disease were concentrated in the northeast and north regions. The social vulnerability indicators associated with the outcome were those related to income, education, and housing conditions. Our analyses demonstrate that chikungunya continues to be a serious health concern in Brazil, but specially in the northeast and north regions. Lastly, mapping risk areas can provide evidence for the development of public health strategies and disease control in endemic regions.

Pemodelan Regresi Global (Glm) dan Regresi Spasial (Sar Dan Sdm) Pada Kasus Indeks Pembangunan Manusia Di Provinsi Kalimantan Selatan

The Human Development Index (HDI) is a parameter that functions to assess the success of the quality of human life. The factors used in the research are the severity of poverty, population density and net participation rate. The research carried out aims to see what factors influence the HDI in South Kalimantan Province in 2022, the HDI value for South Kalimantan Province is below the Indonesian HDI value and quite a few regencies/cities in South Kalimantan Province have HDI values below the HDI value. Indonesia. The statistical analysis used is a spatial approach, where the SAR and HR spatial regression models will be searched. The Global Regression Model (GLM) obtained in this study is , while the Spatial Autoregressive (SAR) model is and Spatial Durbin Model is The best model that can be obtained is the Spatial Durbin Model (SDM) with an AIC value of 52,82654 and an value of 95,86%.