Spatial Regression Research Articles

Economic Decentralization and High-Quality Urban Development: Perspective from Local Effect and Spatial Spillover in 276 Prefecture-Level Cities in China

The advancement of high-quality urban development is of paramount importance for the enhancement of sustainable development and competitiveness at the city level. The economic decentralization system represents a pivotal institutional driving force in this regard. This paper examines the impact of decentralization on the high-quality development of Chinese cities. It constructs the high-quality urban development index (HUDI) through the entropy weight method and analyzes the mechanisms and spatial correlations between fiscal and financial decentralization on the high-quality development of cities through the establishment of panel and spatial regression models. The findings indicate that fiscal and financial decentralization exert a positive influence on urban high-quality development. However, the two forms of decentralization do not exhibit synergies but rather exert an inhibitory effect on one another. Furthermore, decentralization has a considerable positive spatial spillover effect on urban high-quality development. Heterogeneity analyses demonstrate that the impact of the economic decentralization system varies across different regions, reform periods, and cities with varying administrative levels. The robustness test of this paper provides further evidence of the reliability of the research findings. This paper offers theoretical support and policy recommendations for optimizing economic decentralization systems and promoting high-quality urban development.

Slow motion in corona times: Modeling cyclists’ spatial choice behavior using real-time probe data

The recent COVID-19 pandemic has provided a renewed impetus for empirical research on slow and active modes of transportation, specifically bicycling and walking. Changes in modal choice appear to be sensitive to the actual quality of the environment, the attractive land use and built environment conditions, and the ultimate destination choice. This study examines and models the influence of cyclists’ health concerns during the pandemic on their spatial destination and route choices. Using a large real-time dataset on the individual daily mobility of cyclists in the province of Utrecht, the Netherlands, collected through GPS-linked sensors on bikes (VGI, or volunteered geographical information), the analysis employs spatial regression models, Shapley decomposition techniques, and spatial autocorrelation methods to unveil the backgrounds of changes in spatial behavior. The results reveal that the perceived wellbeing benefits of bicycling in green areas during the pandemic have significantly influenced cyclists’ choice behavior, in particular route and destination choice.

Study on the Influence of the Built Environment and Personal Attributes on Commuting Distance: A Case Study of the Tianjin Central Area Divided by TAZ Units

Long commuting distances pose a significant challenge for many large cities, undermining the principles of sustainable urban development. The factors influencing urban commuting distances among residents are complex and necessitate hierarchical analysis. This study uses Tianjin, one of China’s four municipalities, as a case study, employing transportation analysis zones (TAZ) as research units. We classify these units based on resident and working populations, extracting multiple built environment and personal attribute factors to establish a model that examines the influence of the job–housing balance. The analysis identifies 12 sub-items across two categories of influencing factors, with correlations tested through spatial analysis and linear regression. We found 28 positive associations and 35 negative associations. Notably, the job–housing relationship for the working population was generally more sensitive to changes than that of the resident population. At the TAZ level, personal attributes exerted a more significant influence on the job–housing balance than built environment factors, with commuting mode, life stage, age, and income level notably affecting commuting distances.

Cultural and creative industries and local economic growth: exploring the urban channel

ABSTRACT The role of cultural and creative industries (CCIs) in driving regional growth is a topic of ongoing debate, with conflicting empirical findings. This study investigates CCIs’ impact on regional dynamics from two angles. First, it recognises heterogeneity within CCIs, including technological, symbolic and artistic forms, as well as varying levels of creativity across space. Second, it considers the influence of urbanisation on the growth potential of CCIs. Using an original regional-level database on CCIs employment, the research analyses European regions’ growth patterns in relation to different forms of CCIs. Panel and spatial regression methods are employed, and endogeneity is addressed by an instrumental variable approach. The study uncovers effects of CCIs in different urban contexts, adding insightful elements for the design of regional policies.

Assessing the impact of socio-economic and environmental factors on COVID-19 spread in urban neighborhoods: evidence from Urmia, Iran

ABSTRACT The majority of the world’s population has been living in cities for the past two decades, making these areas particularly vulnerable to various stressors, including natural and man-made disasters. A growing literature suggests that socio-economic and environmental indicators significantly affect the epidemic. As a result, this research aims to investigate the effect of these indicators on the rate of infection with COVID-19 in the neighborhoods of Urmia City in Iran. This study uses Moran’s analysis, cluster and outlier analysis to identify high-risk and low-risk areas, as well as weighted spatial regression analysis. The results indicate that the number of employees, the number of elderly people, construction density, and road density have a significant relationship in predicting the epidemic. Based on the study of the effect of variables on the number of COVID-19 patients in Urmia City in Iran, the predicted values for the number of coronavirus patients indicate that the risk of contracting COVID-19 in the southern and part of the western areas is higher than in the northern and northeastern areas.

Mexico city’s decline in per capita domestic water use: a comprehensive spatial-temporal study

ABSTRACT Facing the growing challenges in water supply in urban areas, characterized by high demand and water scarcity, Mexico City, with its high population density and limited water resources, exemplifies the challenges in ensuring equitable management and water supply in complex urban environments. This study combines GIS tools, statistical analysis and spatial regression to analyze the spatiotemporal changes in per capita water consumption in Mexico City households between 2010 and 2019. The results show a 40% decrease in average consumption and a spatial reconfiguration that reflects an increase in inequalities in access to drinking water. The influence of five key factors in the reconfiguration is identified: intermittent supply, economic income, water feasibility, households without piped water, and an increase in dwellings. The findings are essential for developing equitable and effective water policies, emphasizing the need to address inequalities in water access in urban planning to achieve the Sustainable Development Goals.

Road Traffic Infrastructure Construction and Air Pollution Based on the Perspective of Spatial Spillover

Road traffic infrastructure construction is widely regarded as a solution to urban congestion and air pollution. Given the frequent use of non-highway roads for inter-city travel, it is plausible that such infrastructure projects could also improve air quality in neighboring regions. However, the spatial spillover effects of these projects on air pollution remain underexplored. This paper investigates the spatial impacts of road traffic infrastructure construction on urban air pollution. Using a spatial panel regression model, we analyze data from 273 cities in a period spanning 2008 to 2021. Our findings show that road infrastructure construction significantly reduces local air pollution, with results robust to concerns about endogeneity. Furthermore, we identify notable spatial spillover effects, primarily concentrated in adjacent cities. These findings suggest that the accessibility between central cities and their neighboring areas plays a critical role in shaping traffic patterns, air quality, and sustainable urban development. As a result, coordinated regional planning for road infrastructure could serve as an effective strategy to mitigate urban air pollution and promote sustainable growth.

A longitudinal study of evolving polycentric pattern in Shanghai

In the context of technological progress and economic transition, the urban spatial structure has consistently undergone dynamic evolution. However, the trajectories of polycentric patterns display significant national and regional variations. It is crucial to examine the dynamic evolution of spatial structures within Chinese metropolitan areas amidst economic transitions. Focusing on the Shanghai Metropolitan Area (SMA), this study investigates the temporal dynamics of job distribution and employment centres and considers industry differentiation to understand the evolution of polycentric patterns. Additionally, spatial regression models are utilized to probe the intrinsic mechanisms through which adjustments in industrial structure influence spatial evolution. The findings indicate that both the service and manufacturing sectors have the potential to concentrate employment and form subcentres in the SMA. Nevertheless, within the economic transition context, the downtown area, dominated by the service sector, underwent specialized development, fostering continuous prosperity and the emergence of employment subcentres in the periphery. In contrast, employment centres in suburban areas, dominated by the manufacturing sector, experienced a declining trend. Consequently, the locations of employment clusters in the SMA shifted, concurrently reshaping the relationships between the main centre and subcentres and leading to a transformation from a weaker separated polycentric pattern to a stronger continuous polycentric pattern. This paper elucidates the underlying mechanisms through which industrial transition influences urban spatial structure, offering insights into the evolution of polycentric patterns in the postindustrial era from a local Chinese perspective.

Spatial regression analysis of political gender gap in Japan

AbstractInternationally, it has been repeatedly pointed out that the proportion of female members in the Diet and the local assemblies is extremely low in Japan. This is called a political gender gap. We analyzed the relationships between socioeconomic indicators and this political gender gap, and underlying causes using several spatial regression models. Prefectures are geographically connected; so, they can influence each other. Therefore, we incorporated geographical relationships between prefectures into the model. We found that a high proportion of women in a particular prefectural assembly was associated with a low proportion of women in adjacent prefectural assemblies. Thus, competition and repulsion exist between the adjacent prefectural assemblies. Furthermore, several series of analyses using spatial regression models showed that the impact of the political gender gap in a particular prefecture spread to adjacent prefectures, but not further. We found that considering prefectural spatial autocorrelations was important while examining the political gender gap, and previous studies that did not consider spatial autocorrelation might have been biased. However, here we were able to better explain the background of real-world phenomena occurring in the real world by applying spatial regression models.

A Geospatial Analysis of the Association between Access to Tanning Bed Facilities and Melanoma in the United States New England Region.

Substantial geographic disparities in melanoma incidence and access to tanning bed facilities exist. In this spatial ecological study, we evaluate the relationship between access to tanning bed facilities and melanoma incidence. New England county-level melanoma incidence rates (2014-2018), tanning bed facilities location, demographic data, socioeconomic data, and geographic data were obtained from the National Cancer Institute State Cancer Profiles, Data Axle, American Community Survey, and United States Geological Survey. Spatial regression models were utilized to determine the association between melanoma incidence and access to tanning beds, adjusting for confounding factors. Adjusted spatial regression identified that for every 1-minute increase in average travel time to tanning facilities within 30 minutes travel time threshold, the melanoma incidence rate decreased by 3.46% in the same county and by 1.92% across New England. We found a negative association between average travel time to tanning facilities and county-level melanoma incidence rate, suggesting that policy aimed at decreasing access to indoor tanning facilities has the potential to reduce melanoma rates. High-risk clusters, with respect to county-level melanoma rate, were also identified. Targeting of interventions and resources to high-risk clusters may reduce geographic melanoma rates disparities.

Investigating the influence of land cover on land surface temperature

The increasingly serious urban heat island (UHI) effect is unfavorable to urban development. This study utilized land cover data and land surface temperature (LST) data of China in 2020 by using correlation analysis and spatial regression models to analyze the relationships between LST and two influencing factors (land cover and digital elevation model (DEM)). The results showed the following: (1) The correlation between LST and forest was highest in the Northeast China Plain (NCP), Huang-Huai-Hai Plain (HHP), Qinghai Tibet Plateau (QTP), and Loess Plateau (LP). DEM mean displayed its highest correlation in the Northern arid and semiarid region (NAR), Sichuan Basin and surrounding regions (SCR), Yunnan-Guizhou Plateau (YGP), and Middle-lower Yangtze Plain (MYP). Southern China (SC) had the highest correlation between LST and construction land. (2) There was spatial heterogeneity between land cover and LST. Unused land on LP had larger impact on LST. For every 1% increase in the proportion of unused land area, the LST increased by 0.250 °C. LST in some central and western regions of China (the NAR, the LP, the SCR, and the YGP) was mainly affected by local land cover; LST in eastern coastal regions (the HHP, MYP, NCP, SC) and QTP was not only affected by local land cover, but also by LST or land cover of neighboring regions. The warming effect of construction land on LST was more significant, with LST increasing by 0.079 °C to 0.338 °C for every 1% increase in the proportion of construction land area. Coordination of land use planning and synergistic remediation in different regions and rational planning of construction land are essential to mitigate the UHI effect. (3) Water bodies in the NCP, NAR, and MYP had the greatest cooling impact on LST, with LST decreasing by 0.277 °C, 0.246 °C, and 0.079 °C, respectively, for every 1% increase in the proportion of water bodies area. Forest on the QTP, LP, SC, and YGP had the greatest cooling impact on LST, and for every 1% increase in the proportion of forest area, LST decreased by 0.144 °C, 0.089 °C, 0.086 °C, and 0.038 °C, respectively. Actively planting trees and increasing the area of forests and water bodies are of positive significance in alleviating the UHI effect and improving the ecological environment.

Trends in socioeconomic disparities in urban heat exposure and adaptation options in mid-size U.S. cities

There is ample evidence that Environmental Justice communities experience high levels of extreme heat. However, it is unknown how disparities in urban heat exposure and adaptation options change over time. This study investigates socioeconomic disparities over time in urban heat exposure and adaptation options in eight mid-sized Northeastern cities. We ask: How were socioeconomic factors associated with heat exposure and adaptation options over time? We analyzed disparities at the census block group level and census block level, respectively. At the census block group level, we ran spatial regression models between socioeconomic variables, including race, income, gender, and age, and heat exposure and adaptation variables, including land surface temperature, normalized different vegetation index (NDVI), tree cover, and air conditioning ownership rate. We found that: Low median household income is always associated with high LST and low NDVI from 1990 to 2020; Low percentage of females is always associated with high LST and low NDVI from 1990 to 2020. High percentage of POC is associated with high LST in 2010 and 2020, but not in 1990 and 2000; Low median household income and low percentage of elderly are associated with lower tree cover; High percentage of POC, low percentage of elderly, and low median household income are associated with lower AC rates. In analysis at the census block level by city, we found that disparities in urban heat exposure between predominantly POC and predominantly white communities increased in most cities during 1990-2020. Predominantly POC communities consistently have lower vegetation cover over time in most cities. Disparities in vegetation cover per unit area increased in most cities, whereas disparities in vegetation cover per capita decreased in most cities. Our findings of the trends in disparities in heat exposure and adaptation are useful for forecasting disparities in the future. These findings also suggest that interventions should prioritize cities with increasing disparities in heat exposure and adaptation.

Mortality from type 2 diabetes mellitus across municipalities in Mexico

BackgroundOne in six Mexican adults’ lives with type 2 diabetes mellitus (T2DM), which is the third leading cause of death in the country. Analyzing the geographic distribution of T2DM mortality helps identify regions with higher mortality rates. This study aimed to examine the spatial patterns of mortality from type 2 diabetes mellitus (T2DM) across municipalities in Mexico and to analyze the main contextual factors linked to this cause of death in 2020.MethodsWe employed a spatial Bayesian hierarchical regression model to estimate the risk and probability of death from type 2 diabetes mellitus (T2DM) across Mexico’s municipalities.ResultsThe SMR results revealed geographic and age-specific patterns. Central Mexico and the Yucatán Peninsula exhibited the highest excess mortality rates. For the population under 50 years of age, municipalities in Oaxaca had the highest T2DM mortality rates, whereas those aged 50 years old and older had the highest rates in Tlaxcala and Puebla. Socioeconomic factors such as low levels of educational attainment, lack of health services, dietary deficiency, and marginalization were positively associated with increased T2DM mortality risk. By contrast, GDP per capita showed a negative association. High-risk areas for T2DM mortality were prominent along the south of the Pacific Coast, the Bajío, Central Mexico, and southern Yucatán for those under 50, and along a central strip extending to the Yucatán Peninsula for the older population. Significant uncertainties in mortality risk were identified, with Central Mexico, Oaxaca, Chiapas, and Tabasco showing high probabilities of excess risk for those under 50 years of age and extended risk areas along the Gulf of Mexico for those 50 years old and older.ConclusionsThe assessment and identification of spatial distribution patterns associated with T2DM mortality, and its main contextual factors, are crucial for informing effective public health policies aimed at reducing the impact of this chronic disease in Mexico.

Disparities in ridehailing travel times for accessing non-work destinations

We contribute to the literature on new mobilities by measuring spatial disparities in travel times for accessing essential non-work destinations via ridehailing. We focused on healthcare, restaurants, and grocery destinations in Chicago. Data from Chicago ridehailing providers, which included detailed information about all realized ridehailing trips in Chicago, were used to derive mean travel times by ridehailing for each census tract. Inspired by the gravity-based model, we calculated an inverse travel time index based on cumulative travel times for each census tract where ridehailing trips occurred. To understand the disparities in travel times, we compared the inverse travel time index for ridehailing and transit in the same census tracts. Then, we applied spatial autoregressive regression to examine the effects of various sociodemographic factors. The results suggested that the inverse travel time index was preferable in tracts with a higher household income and a lower percentage of minority populations. Also, disparities in travel times tended to be greater via ridehailing than transit. This study sheds light on disparities related to ridehailing and how we could improve access to essential destinations for underserved and underrepresented populations and communities. Policy implications include subsidizing disadvantaged users who lack reliable transportation options, regulating ridehailing prices, increasing the provision of essential destinations for underserved areas, and maintaining the quality of public transit services.

A bayesian shared component spatial modeling approach for identifying the geographic pattern of local associations: a case study of young offenders and violent crimes in Greater Toronto Area

Background settingTraditional spatial or non-spatial regression techniques require individual variables to be defined as dependent and independent variables, often assuming a unidirectional and (global) linear relationship between the variables under study. This research studies the Bayesian shared component spatial (BSCS) modeling as an alternative approach to identifying local associations between two or more variables and their spatial patterns.MethodsThe variables to be studied, young offenders (YO) and violent crimes (VC), are treated as (multiple) outcomes in the BSCS model. Separate non-BSCS models that treat YO as the outcome variable and VC as the independent variable have also been developed. Results are compared in terms of model fit, risk estimates, and identification of hotspot areas.ResultsCompared to the traditional non-BSCS models, the BSCS models fitted the data better and identified a strong spatial association between YO and VC. Using the BSCS technique allowed both the YO and VC to be modeled as outcome variables, assuming common data-generating processes that are influenced by a set of socioeconomic covariates. The BSCS technique offered smooth and easy mapping of the identified association, with the maps displaying the common (shared) and separate (individual) hotspots of YO and VC.ConclusionsThe proposed method can transform existing association analyses from methods requiring inputs as dependent and independent variables to outcome variables only and shift the reliance on regression coefficients to probability risk maps for characterizing (local) associations between the outcomes.

Spatial Regression Analysis of Environmental and Structural Factors Influencing Shophouse Values in Makassar City, Indonesia

This study investigates the factors that influence shophouse values in Makassar City, Indonesia, using a spatial regression model. The research addresses the lack of prior studies that consider spatial dependencies in property value assessments. By analysing data from 101 shophouses across four distinct city strata, factors such as building area, certificate type, and road width were found to significantly impact property values. Additionally, spatial relationships between neighboring properties were observed, indicating a 0.3 positive correlation. The study employed Structural Equation Modeling (SEM) with the R Project for Statistical Computing. The results provide insights into the spatial dynamics of urban property values, which are valuable for urban planners, investors, and policymakers aiming to optimize real estate development in rapidly growing urban centres.

Spatial disparities in cancer incidence and mortality, indicating gaps in prevention and/or care

Abstract Background There is a rich literature on the distribution of cancer incidence and mortality in socioeconomically different regions of the world, but none of the studies has compared the spatial distribution of mortality and morbidity to see if they are consistent with each other. Methods All malignant neoplasms and separately by cervical, colorectal, breast, pancreatic, lung and oral cancers were studied in the Hungarian population aged 25-64 years for the period 2007-2018 at the municipality level by sex. In each case, the spatial distribution of morbidity and mortality and their clusters were compared with each other and with the level of deprivation using disease mapping, spatial regression, risk analysis and spatial scan statistics. Results All-cause cancer morbidity and mortality showed a significant association with deprivation. However, the slope of the increase in mortality with increasing deprivation was much steeper than for morbidity in both sexes. The association between deprivation and morbidity was not consistent across all cancers. No significant association was found for male colorectal cancer (RR: 1.003; 95%CI 0.986-1.019), pancreatic cancer (1.014; 95%CI 0.983-1.045) and female colorectal cancer morbidity (RR: 0.977; 95%CI 0.991-1.029), whereas a negative association was found for breast cancer morbidity (RR: 0.977; 95%CI 0.963-0.991). In contrast, a positive association between deprivation and mortality was found for each type of cancer, although with different RRs. However, disease mapping analyses showed only partial overlap between high-risk morbidity and mortality areas, often independent of deprivation. Conclusions Our results highlight not only the diverse relationship between the epidemiological situation and deprivation, but also the disproportionate relationship between cancer morbidity and mortality, thus identifying geographical regions and sub-regions with populations that require special public health attention. Key messages • Comparative spatial analysis of cancer morbidity and mortality at the municipality level is strongly recommended to identify populations in need of targeted cancer prevention and/or care. • The usefulness of comparative spatial analysis of cancer morbidity and mortality is demonstrated in Hungary, one of the countries with the most unfavourable cancer burden in international comparison.

Exploring demographic, healthcare, and socio-economic factors as predictors of COVID-19 incidence rate: A spatial regression analysis.

This study investigated the relationship between demographic, healthcare, and socio-economic factors, and COVID-19 incidence rate per 100,000 population in Thailand at the province level between January 2020 and March 2022, using a five-phase approach by spatial analysis. OLS models were initially used with significant variables: household, hospital, and industry density, nighttime light index (NTLI). Spatial dependency led to spatial error (SEM) and spatial lag models (SLM), performing better with similar significant variables being applied. SEM explains 58, 65 and, 70 percent in Wave 1, 4 and 5 of COVID-19 variation. SLM explains 25 and 76 percent in Wave 2 and 3 of incidence rate. Positive associations were found between incidence and household density, hospital/medical establishments with beds, Nighttime Light Index (NTLI), and negative with population, hospital, and industry density. Wave 5 showed significant changes with negative for household, hospital, and industry density, urban population; positive for hospital/medical establishments with beds, internet access, NTLI. The study showed that significant predictors of COVID-19 incidence rate vary across waves. Population, household and hospital density, urbanization, access to medical facilities, industrialization, internet access, and NTLI all play a role. The study suggests SEM and SLM models are more appropriate, providing useful information for policymakers and health officials in managing pandemic in Thailand.

Demographic Change and Regional Specialisation: The Case of Greek NUTS II Regions

This paper investigates the impact of demographic changes on regional specialisation across Greek NUTS2 regions from 2001 to 2021. It explores the relationship between age-specific variations in the economically active population and gross value added (GVA) across different economic sectors. Using demographic and GVA data alongside spatial analysis techniques such as spatial regression models, median Local Moran’s I, and location quotient, the study identifies spatial autocorrelation patterns. Findings reveal strong correlations between the economically active population and GVA, highlighting demographic factors’ crucial role in regional economic performance. Over the period 2001 to 2021, the 15–24 and 25–34 age groups generally experienced declining growth rates in total, male, and female economically active populations, while the 45–54 and 55–64 age groups saw increases. Regions adjacent to those with higher economically active populations showed high–high clustering. Especially, Attiki and Kentriki Makedonia, the two metropolitan regions of Greece, influence neighbouring regions through economic spillover effects, fostering economic sector clustering and emphasising regional specialisation. These findings highlight the complex interplay between demographic shifts and regional economic outcomes, noting spatial disparities and the need for targeted policy interventions. The study provides empirical evidence demonstrating how demographic changes influence regional economic development across different regions.

Spatial evolution characteristics of the mechanical properties of gangue-cemented paste backfill with high-water-content materials: an experimental investigation

ABSTRACT The mechanical properties of cemented paste backfill (CPB) directly determine the effect of backfilling in coal mining. However, the current studies on the mechanical properties of CPB are all based on standard cylinder or cube specimens, which cannot accurately characterise the distribution of mechanical properties in different spatial positions of CPB in situ. In addition, high-water-content material (HWM) has developed rapidly as a new binder in recent years. Therefore, in this paper, the uniaxial compressive strength (UCS) and elastic modulus of CPB with gangue as the aggregate and HWM as the binder (GHW-CPB) were investigated under different gangue particle sizes. The following conclusions were drawn: (1) The spatial evolution and distribution characteristics of mechanical properties of GHW-CPB are analysed. (2) The spatial evolution regression models of mechanical properties of GHW-CPB are established. (3) We improved the GHW-CPB spatial evolution regression models for mechanical properties due to their limitations and confirmed its validity and rationality. Through the research results of this paper, the failure position of the CPB in the goaf can be accurately predicted, so that manual interventions can be carried out in the vulnerable areas in advance to reduce the risk of coal mining production.