Applied Spatial Analysis and Policy最新文献

High-quality geospatial data are required to examine how the places in which we reside, work and play determine health outcomes; however, seldom is the quality of nationwide geospatial data reported. We examined the quality of geospatial data of public drinking water distribution zones (WDZ) across all territorial authorities in Aotearoa New Zealand to investigate structural inequities in data quality. In our national dataset of WDZ, we identified several differences in the quality of geospatial information that are associated with the population, area-level deprivation, ethnicity and most of all urban/rural classification. Our research highlights structural data inequity, which may undermine efforts to reduce health inequity.

This paper analyzes the relationship between informal housing and labor informality at the intraurban level, considering these two phenomena' simultaneity and spatial dimensions. Our analysis focuses on the context of a city in a developing country, Medellín (Colombia), characterized by significant housing precariousness and low employment quality, where space seems to play an essential role in understanding this relationship. Using data from 176 analytical regions in Medellín for 2017, we estimate a series of spatial simultaneous equation models that consider the potential cross-equation correlations in the error terms. The results show that these two types of urban informality are highly persistent in space, with noticeable spatial clusters observed in the peripheral and marginalized areas of the city. Additionally, the estimated econometric models reveal that precarious working conditions are key to explaining the spatial choice of housing and its characteristics, and vice versa. These findings emphasize the need for place-based policies that specifically target disadvantaged areas and help improve residents' working and housing conditions to address urban informality.

Different regions of Iran have suffered for a long time from spatial inequalities because of the shortage of infrastructure and educational services. Educational activities play an important role in social and economic mobility and development of regions. The closing of educational centers due to the spread of the Covid-19 epidemic affects the spatial and social inequalities of the regions in Iran. The current study aims to examine spatial inequality of educational indicators during two periods: “before” the outbreak of Covid-19 (2018) and “during” its outbreak (2021). Then the provinces of Iran are stratified in terms of educational indicators by using the combined method of TOPSIS and Shannon's entropy, cluster analysis in Spss and using ArcGis. The most important factors of educational inequality during the Covid19 period were found by using Pearson's correlation coefficient and multivariate regression. The results showed that in the pre-Covid period, the distance between the most privileged province (Bushehr) and the most deprived province (Tehran) was 4.5. This amount has increased to 5.5 during the period of Covid-19, which shows the increase of inequalities due to the conditions of Covid-19. Further investigation of the results shows that the inequality of educational indicators among the provinces of the country has a clear relationship with the changes in population movements in different regions of Iran. On the other hand, due to the digital education without need for educational space in the conditions of Covid-19, the importance of the existence of physical infrastructure has been reduced. And two key variables as population density and internet penetration rate play an important role in the provinces’ educational indicators during the period of covid-19. These results could help the spatial planning to reduce inequalities in different regions of the country.

Ecological inference has had primarily political science applications, but this study considers an application to assess variations in neighbourhood mental health between population sub-groups. The methodology used has particular utility when neighbourhood health data are available only in aggregate form, without disaggregation (e.g. by ethnic or socioeconomic group). The ecological inference approach is shown to provide insights into contextual effects, where neighbourhood features influence disease variations between sub-groups (e.g. the ethnic density effect on psychosis among non-white groups). The present study also highlights important issues raised by the data’s geographic framework, namely strong spatial clustering in the outcome, and compares spatial error and spatial lag methods to represent this spatial patterning appropriately. The study considers neighbourhood variations in psychosis between four ethnic groups, with a spatial framework provided by 32,844 small areas (Lower Super Output Areas, LSOAs) in England.

Despite the importance of leisure on life quality, limited research has investigated the spatial and temporal changes and constraining factors of residents’ leisure participation in developing countries. Based on nationwide data from China General Social Survey (N = 40,908), this study used a fuzzy set approach and global spatial autocorrelation to analyse the spatial variations of residents’ leisure participation index (RLPI) in China from 2010 to 2017. Then, the determinants of spatial differentiation of RLPI were discussed based on the Geodetector method. Results show that (1) traditional media activities have always been the primary leisure activity for residents, whereas online leisure has the fastest growth during the research period. (2) The RLPI in China presents the geographical characteristics of ‘high in the east, medium in the middle and northwest, and low in the southwest’. (3) Family conditions are the most influential factor of RLPI spatial disparities, followed by leisure facilities, economic development, leisure time, and transportation. The findings provide insights into the understanding of regional disparity of leisure participation and shed light on improving leisure engagement in developing countries.

This study highlights the importance of green investment in achieving the goal of sustainability. Several studies have been conducted to explore the factors affecting sustainable development. However, research on the importance of green investment in achieving sustainable development from the perspective of enterprise cost and government guidance is scant, and the moderating effect of financial efficiency has not been considered. Thus, this study is based on the social and economic data of 31 provinces in China from 2009 to 2021; it evaluates the level of sustainable development of each province and combines green investment, financial efficiency, and sustainable development into the research framework from the dual perspectives of government and enterprises to explore the relationship among the three, which can result in efficient and balanced sustainable development. The results show the following: (1) government green investment has a “U-shaped” spillover effect on sustainable development; (2) corporate green investment has an inverted U-shaped nonlinear effect on sustainable development; and (3) under the adjustment of financial efficiency, government green investment has a “two-stage” promoting effect on sustainable development, and corporate green investment has an inverted U-shaped nonlinear effect on sustainable development. Based on these findings, an increase the amount of government green investment can ensure the sustainable amount of corporate green investment and promote coordinated financial and economic development.

In many West African countries, ongoing rapid urbanization urges reliable and proactive land use plans for sustainability purposes, which hinges on a complete assessment of land suitability. GIS-based multi-criteria decision analysis (GIS-MCDA) is one of the most widely applied techniques in land-use suitability analysis. It is pivotal that planners and analysts in the developing world have adequate support in conducting such analysis. To lower the financial and technological barriers, a new free and open-source software (FOSS) for GIS-MCDA is developed, called PyLUSATQ. It is designed as a QGIS plugin following a tight-coupling integration strategy, where analytic tools for GIS-based suitability analysis and MCDA are interconnected and seamlessly integrated into QGIS’s processing framework. With this implementation, users can create customized models with the PyLUSATQ tools using QGIS’s Graphical Modeler to automate the workflow for suitability analysis. PyLUSATQ is the first of its kind amongst all plugins published on the QGIS Python Plugin Repository, offering a range of tools for GIS-MCDA within the context of land use planning. To demonstrate its practical application, we created two 2050 future land-use scenarios in the Ashanti Region of Ghana. The first scenario depicts a “business-as-usual” model, whereas the second shows an “alternative” scenario if a higher development density was adopted. Comparisons of such scenarios provide evidential support for making informed decisions on land-use policies. Additionally, the methodology introduced here is easily replicable for developing new QGIS plugins based on third-party Python libraries.

China has been experiencing below-replacement fertility rates for decades prompting concerns about the rapid aging of the population and a dwindling labour supply. This paper examines the effects of micro-level and macro-level factors on fertility intentions in China from 2010 to 2021. The analyses utilize repeated cross-sectional data from the nationally representative Chinese General Social Survey, focusing on adults of childbearing age. A multilevel logistic regression approach is employed. The findings indicate that economic status, at both the individual and provincial levels, significantly shapes individuals’ intentions regarding having two or more children. Those with higher individual economic status are more inclined to have multiple children, while individuals residing in provinces with higher mean incomes are less likely to have two or more children compared to those in provinces with lower mean incomes. Notably, the long-term impact of the one-child policy on intentions to have two or more children is particularly pronounced for urban residents and individuals from provinces that rigorously implemented the one-child policy. Additionally, the study identifies spatial differentiation in fertility intentions.

The study of access to health centers is gaining global attention due to it is critical role in ensuring equitable and easily accessible healthcare services for all citizens, leading to long-term social equity and an improved quality of life. This paper aims to assess the efficiency of healthcare facility locations in Istanbul from two perspectives: accessibility of healthcare facilities and sufficiency of healthcare capacity. The analysis of accessibility considers the geographic locations of health services, the road network, and traffic conditions, particularly during two peak travel times in Istanbul. The evaluation of healthcare capacity utilizes the ratio of the number of beds to the population in each district. According to the findings, while the majority of the population has reasonable access to the nearest hospital, there exists a significant disparity in hospital location and bed availability in Istanbul. This indicates notable challenges in achieving a balanced distribution of hospitals based on their bed capacity. Addressing this imbalance is crucial in selecting suitable locations for new health facilities. To enhance the accessibility and effectiveness of public and private hospitals, decisions regarding site selection should aim to mutually support one another. Additionally, it is recommended to plan new healthcare facilities based on the population and density distribution of the city to increase public interest and optimize the efficiency of health services.

This paper presents a modelling framework which can detect the simultaneous presence of two different types of spatial process. The first is the variation from a global mean resulting from a geographical unit’s ‘vertical’ position within a nested hierarchical structure such as the county and region where it is situated. The second is the variation at the smaller scale of individual units due to the ‘horizontal’ influence of nearby locations. The former is captured using a multi-level modelling structure while the latter is accounted for by an autoregressive component at the lowest level of the hierarchy. Such a model not only estimates spatially-varying parameters according to geographical scale, but also the relative contribution of each process to the overall spatial variation. As a demonstration, the study considers the association of a selection of socio-economic attributes with voting behaviour in the 2019 UK general election. It finds evidence of the presence of both types of spatial effects, and describes how they suggest different associations between census profile and voting behaviour in different parts of England and Wales.