Applied Spatial Analysis and Policy最新文献

How to govern relative rural poverty is the key and difficulty in eliminating poverty and achieving common prosperity in China. With the rapid development of digital economy, digital inclusive finance is playing an increasingly fundamental role in poverty alleviation. As an important new financial form, whether and how digital inclusive finance affects relative rural poverty is not yet known. Based on new economic geography, this paper empirically tests the direct and spatial impacts of digital financial inclusion on relative rural poverty alleviation by constructing spatial econometric models and using panel data from 31 provinces (autonomous regions and municipalities directly under the central government) in China from 2012 to 2019. The study found that there is a significant positive spatial correlation in relative rural poverty; the development of digital inclusive finance has a significant inhibitory effect on relative rural poverty. Meanwhile, the development of digital inclusive finance in the local province also has a negative spatial spillover effect on rural relative poverty in surrounding areas. Therefore, it is necessary to boost the development of digital inclusive finance, improve the coordination of inclusive finance between regions, and promote inter-regional economic cooperation in the future. Poverty alleviation remains a challenge in the world, especially in developing countries. Digital inclusive finance, which is a new form of inclusive finance and digital economy widely applied in China, could play an increasingly fundamental role in poverty alleviation in rural areas. In this study, a spatial econometric model (SAR) is constructed based on the new economic geography, and the digital financial inclusion index is integrated with macro-economic data at a provincial level in China from 2012 to 2019. The direct and spatial impacts of digital inclusive finance on poverty reduction in rural areas were accessed using the developed model. Results show that digital inclusive finance can significantly reduce relative poverty in rural areas in China. More importantly, it is indicated that there is a significant positive spatial correlation in relative rural poverty, and digital inclusive finance has a negative spatial spillover effect on relative rural poverty, which is supported by a series of endogeneity and robustness tests, such as substitution of relative poverty, replacing models, and using alternative specifications. Recommendations on implementations in poverty alleviation are proposed based on the results of this study. This paper further complements the hot research field on finance development and income inequality. Our findings offer insights into the development of inclusive financial policies for relative rural poverty alleviation in other countries, especially in developing countries with similar backgrounds to China.

This article’s main focus has been the Rink Road Project in Konya, which is slated for construction despite serious urban growth issues. The study looked at how this project may impact urban growth in order to provide knowledge that would guide future land use policies and plans for sustainable urban development. The urban growth trend 2043 which is the target year of the environmental plan, and 2055 were examined using the SLEUTH Urban Growth model. Six different SLEUTH model scenarios that take a different tack on urban growth were assessed in alternative futures. This study also tries to figure out; assesses a range of potential spatial transformations around the planned Konya Ring Road (KRR), incorporating the first Environmental Plan established post the enactment of Turkey's Law No. 6360 into the scenario planning. According to future predictions of urban growth, a flexible Konya Environmental Plan (KEP) in the short term could potentially intensify the adverse effects of urban expansion associated with the KRR in the absence of a stringent KEP. Long-term observations from the examined scenarios reveal a significantly higher projected urban growth in cases where an KEP is absent. If current land policies continue without significant modifications, the completion of the KRR is expected to accelerate urban growth in Konya, leading to a substantial transformation of natural areas into urban zones.

Understanding the inequity in spatial accessibility to COVID-19 vaccination sites is beneficial for the government to optimize vaccine resources. Nevertheless, the result of evaluating spatial accessibility to COVID-19 vaccination sites may be directly affected by the lack of fine-scale and accurate population distribution information in both urban and rural areas. In addition, the choice of travel modes related to different age groups has not been incorporated into existing methods well. Consequently, this study proposes a framework to evaluate spatial accessibility to COVID-19 vaccination sites based on fine-scale population distributions and heterogeneous travel modes. A divide-and-conquer strategy is first presented to estimate the fine-scale populations in both urban and rural areas. A hybrid time distance considering heterogeneous travel modes and a population-weighted proximity indicator are then defined to evaluate spatial accessibility for each age group. The experimental results from Xiangtan, China, show that (1) the divide-and-conquer strategy can effectively predict the urban and rural populations. (2) Similar clustering patterns of spatial accessibility are observed for each age group. (3) Inequities have been confirmed at the county level. The main findings in this study can provide valuable information supporting the spatial optimization of existing COVID-19 vaccination sites.

Amidst the spectrum of medical facilities, community health service centers (CHSCs) play an important role in alleviating the burden on hospitals. However, the intricate interaction between hospitals and CHSCs in accessibility analysis is often overlooked. To fill this gap, this study employs an enhanced gravity model to evaluate spatial accessibility of hospitals, focusing on both subdistrict and CHSC levels. The findings show that tertiary hospitals, providing the highest quality medical services, are concentrated in the central districts of Nanjing. Moreover, prevailing CHSC deployment plans risk an inequitable allocation of medical resources at the subdistrict level. In response, we proposed a collaborative mechanism that synergizing hospitals and CHSCs, built on a reciprocal medical alliance referral system. Specifically, our recommendations include: (1) promoting CHSCs to engage in alleviating the patient treatment load borne by tertiary hospitals in the central districts; and (2) balancing between the quality and quantity of CHSCs in the peripheral districts to increase accessibility for small-sized CHSCs in remote locations. This study contributes to the existing literature by incorporating CHSCs into hospital accessibility analysis and refining the framework for the spatial distribution of medical facility in alignment with established policies.

The benefits of green spaces on individuals’ health have been widely acknowledged due to their inherent natural qualities. Currently, university students are experiencing significantly higher levels of mental health problems than other social groups. There is a scarcity of studies examining the association between built environment factors and mental health issues among university students, particularly in the Chinese context. University campuses in China are physically isolated, secluded communities, and in this respect, they differ markedly from the spatial organisation patterns of Western universities. Therefore, this study focuses on the correlation between the extent of green space exposure within closed university campuses and the occurrence of mental health issues among resident students. A deep-learning methodology incorporating streetscape images, remote sensing data, and multilevel linear modelling is employed in order to facilitate a comprehensive analysis. The results demonstrate a negative correlation between green space exposure on campus and the level of mental health issues among university students. Individual socio-demographic characteristics, such as whether a person has a partner, are also found to influence the level of mental health issues that they experience. In addition, a significant relationship is found between travel patterns and mental health issues, with students who walked regularly having a lower incidence of mental health issues than those who drove. Our research indicates that, in order to foster healthier communities and enhance social inclusion, urban planners should prioritise the development of greener campuses and urban transport services to improve accessibility to green spaces.

Empirical data from several cities worldwide show that low consideration rates of bus transit constitute an important reason for declining transit mode share. This trend raises a critical question: why do not more commuters consider buses for travel? The aim of this paper is to investigate the key deterrents to bus transit consideration among commuters. Consideration refers to the decision of an individual to include a given mode in his/her choice set. Along this line, this study focuses on two important directions. The first is to gain an understanding of how the key factors and their influence on consideration propensity differ across market segments based on personal vehicle availability and accessibility. The second is to quantify the spatial heterogeneity in the influence of those key factors across geographical locations. A new geographically weighted segmented logistic regression (GWSLR) model is proposed to address these research issues. The model is developed using household survey data from a sample of work-commuters from Chennai city, India. The findings reveal that neglecting either segmentation or spatial heterogeneity cannot only result in inaccurate model predictions and inferences, but also lead to sub-optimal policy interventions. Results show that factors influencing consideration differ significantly across segments based on captivity and accessibility. Choice users within walking distance to bus stops can benefit from direct bus service, whereas those beyond walking distance prioritise first-mile connectivity. Results also highlight significant variations in the influence of different factors across locations. Improved walkability in central business districts (CBD) could increase consideration, while non-CBD areas should focus on first-mile connectivity. Finally, this study illustrates that customised policy interventions for specific segments and locations can be more effective in enhancing bus consideration than segment-agnostic or geographically uniform policies.

The walkability is closely related to the lifestyle and health of local residents. Unreasonable community design may result in spatially segregated spaces, creating walking barriers for residents and reducing walking accessibility. If a community’s physical layout can be optimized through better walking-related planning at the community level, people would be more convenient to reach more facilities with a better walking accessibility. A microscale measurement method for walkability at the community level is necessary to assist in community planning. This study developed a new measurement of walkability using a hexagon-based adaptive crystal growth algorithm and verified its effectiveness by examining the impact of gated communities on walkability. Taking three communities in Beijing as cases, this study created two kinds of accessibility planes (with-wall and no-wall) to compare changes in walkability between gated and open communities. The findings show that gate and wall removal can partially improve community walkability and that the algorithm can accurately measure community walkability in different scenarios. The proposed algorithm provides a new perspective for walkability measurements and offers a scientific tool for people-oriented community planning.

Due to a shortage of qualified professionals, many international cities have implemented a range of policies to attract skilled migrants and retain them for permanent work in destination cities, aiming to take advantage of the globalized competition. Despite being relatively understudied, the role of housing is of utmost importance, especially if skilled migrants can be attracted to work and live in destination cities permanently. This study conceptualized housing wellbeing as subjective housing feelings and objective housing outcomes, aiming to disentangle the relationship between housing wellbeing and settlement intentions. Based on the 2019 questionnaire survey in Guangzhou, this study found that skilled migrants who have become homeowners in Guangzhou have a higher propensity to settle in Guangzhou than those without homeownership. Also, this study found an inverted U-shaped relationship between the number of rooms and settlement intentions. Moreover, when the housing size per person is between 16 and 20 m², housing size per person is positively associated with settlement intentions; nevertheless, when the housing size per person is below 16 m² or above 20 m², there is nil relation between housing size per person and settlement intentions. Finally, although skilled migrants are not satisfied with the current housing situation, they are still willing to settle in Guangzhou permanently. This study has extended the knowledge of housing wellbeing and contributed to profoundly understanding the complex settlement intentions of skilled migrants.

Crime against women (CAW) is not a present-day problem but has been prevalent in the world through the ages and since the beginning of civilizations. The cases of CAW have been increasing in almost all parts of the world and India is no exception. The distribution of CAW cases has not been found uniform across the country. The evidence of heterogeneity of cases has been a concern. Rajasthan, the largest state in India, has witnessed a very high surge in CAW in recent years. Therefore, there arises a need to study and analyze the pattern of CAW to identify the areas with high intensity for prevention and control. The CAW data from the National Crime Records Bureau (NCRB) website for the period 2014 to 2021 and the population census data of 2011 are used for the analysis. The Statistical analysis software, SaTScan, is employed for hotspot (areas with a high concentration of crimes) detection. Python programming is used to compute the data’s trend or pattern through visualization and descriptive statistics. In addition, the simple exponential smoothing method is applied for predicting the CAW for the year 2021. Our work elucidates Jhalawar, Baran, Kota, Bundi, Sawai Madhopur, and Chittorgarh districts as consistently occurring hotspots of CAW in the state. A comparative study of the hotspots found is made with the result obtained from the descriptive analysis. The trend in the data explains the years 2017 and 2019 as trough and crest of CAW cases. The hotspot detected using the forecast value of 2021 appears to be the same districts as for the period 2014 to 2020. Our work concludes that the consistency and the most likely cluster of CAW are distributed distinctly. We also found that the hotspot of CAW is not by chance but has certain man-made reasons. Most of the clusters have been identified as districts sharing boundaries with adjacent states. This further implies that if sincere efforts to collaborate with the government of the adjacent states like Madhya Pradesh, Haryana, Punjab, Uttar Pradesh, and Gujarat, the incidences resulting in detrimental effects due to CAW could reduce effectively and significantly. Thus, our study may help the government, law enforcement agencies, police organizations, judiciaries, and other stakeholders to optimize their scarce resources most effectively to curb such incidents.