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Low-sampling-rate floating car data (FCD) are more challenging than those with high-sampling-rate FCD for map matching (MM) algorithms. Some MM algorithms for low-sampling-rate FCD lack sufficient ...

Public services equalization is closely related to local economic and social development. Hence, it is crucial to explore the changing dynamics of public services equalization and its correlation with regional economic disparities. We first examine the changing spatiotemporal patterns of public services provision and local economic performance at the provincial level across China from 2003 to 2017, using a set of indicators and the Mann–Kendall test. It is found that different types of public services are divergent in both temporal trend and geographical locations. However, both income and expenditure have been significantly increased for all provinces during the study period. Second, we unravel the heterogeneous relationship between public services provision and local economy across time and space using the geographically and temporally weighted regression. Variance decomposition is further employed to quantify the relative contribution of public services provision to local economy. Results show that the impact of different types of public services on local economic system is divergent, which jointly affects local economy system together with political and other economic factors. Thirdly, we use the Theil index and traditional least square regression to further examine the relationships between public services equalization and regional economic disparities. We find that public services equalization is correlated with regional economic disparities at the national level, yet their interrelation varies significantly in different regions. Taken together, through revisiting the role of public services equalization in regional economic disparities and unpacking its geographical and temporal heterogeneity, this study fills salient research gaps and informs policymaking towards a long-term goal of social equalization.

This study conducted scientific evidence linking neighbourhood built environment to adults' leisure-time physical activity (LTPA) among adults in China. Data were obtained from a questionnaire survey conducted from April to July 2017 among 1002 adults aged 18-69 years old in Pingshan District, Shenzhen, China. Chinese Walkable Environment Scale for urban community residents and International Physical Activity Questionnaire were used to measure participants' neighbourhood built environment and leisure-time physical activity, which was categorised into leisure-time walking (LTW) and leisure-time moderate-to-vigorous physical activity (LTMVPA). A total of 986 participants (mean age = 40.7 years, 53.3% females) were included in this research. Descriptive statistics were used to describe the socio-demographic variables, LTW and LTMVPA by sex. Multivariable logistic regression models were used to examine the association between neighbourhood environment characteristics and the likelihood of engaging in active LTW and LTMVPA. Only 20.7% of participants engaged in active LTW and 17.8% active LTMVPA. Better road condition was associated with higher likelihood of active (at least 150 min/week) LTW and LTMVPA. High perceived esthetic was positively associated with LTW and LTMVPA. Active LTW was related to better perception of traffic condition as well. The improvement of the neighbourhood environment characteristics can promote active LTPA among adults living in Shenzhen, China. Our findings support the importance of considering population health effects in urban planning and development.

The design of tourist maps of historical cities sometimes fails to balance functionality and artistry or does not fully reflect cities’ cultural connotations. In this paper, we select Xi'an, China, as a study case and design a tourist map through a spatial narrative to reflect its artistic characteristics in the layout design, color design and symbol design. A few operational suggestions are proposed for improving functionality and artistry in tourist mapping, including variable scales, topological optimization, hand-painted symbols and colors extracted from the architecture of the city.

Poverty remains one of the long-term chronic dilemmas facing the sustainable development of human society during the 21st century. The spatiotemporal dynamics of poor regions, particularly in developing countries, is crucial for realizing fundamental sustainable development goals (SDGs). For decades, many scholars have sought to accurately measure, identify and alleviate poverty at different geographical scales. However, reliable data about the estimation of poverty remain scarce for developing countries, hindering efforts to accurately identify poverty. This paper utilizes the Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS) sensor nighttime light imagery to identify poor counties in China from 1992 to 2013. Using 16 statistical and spatial features extracted from this nighttime light imagery and using 96 poor counties and 96 nonpoor counties from 2010 as the classification sample, we describe the spatiotemporal dynamics of poor counties based on a random forests approach. Our study finds that the number of poor counties is decreasing in a fluctuating pattern and that contiguous poverty-stricken areas are becoming fragmented. The reduction in poor counties exhibits a manner of moving horizontally from the eastern regions to the central and western parts of China, while the number of poor counties in the central and western regions has decreased around the central cities or areas. The Aihui-Tengchong Line is not the dividing line in the distribution of poor counties in China, which means that China's poor can also be found in areas with relatively high population density. Together, the findings reveal that the key to reducing regional poverty is the development of regional economies and the implementation of national macro policies. This paper provides references for formulating antipoverty strategies for each county and offers new insights into poverty estimation and regional sustainable development for other developing countries.

A sufficient number of studies have highlighted that walkable neighborhoods can help to reduce the risk of obesity and noncommunicable diseases (NCDs). The Chinese government advocates the 15-minute (15-min) walkable neighborhoods to provide citizens with 15-min walkable access to basic public services, and ultimately to improve walking behavior and overall health. Following the Walk Score metric, this paper proposes a modified method for measuring 15-min walkable neighborhoods and applies it to Shanghai, China. Based on amenity access, the assessment considers walking demands of different pedestrian groups (i.e., the entire population, children, adults, and seniors), the amenity attributes (scale and category), and the real traffic conditions. Spatial regression is further performed to determine whether significant associations exist between community socioeconomic status and 15-min walkable neighborhoods score. Results show clear variations in 15-min walkable neighborhoods score for different pedestrian groups. Regarding the overall 15-min walkable neighborhoods, highly walkable communities are primarily concentrated in the central areas; and that poorly walkable communities are dispersed in rural areas. Senior-concentrated and adult-concentrated communities are more likely to present higher walkability, while children-concentrated communities exhibit lower walkability. Moreover, communities with inferior walkability are characterized by a high proportion of floating population (nonresidents). This research provides future studies with references for evaluating 15-min walkable neighborhoods. Social inequalities in 15-min walkable neighborhoods should be emphasized, and interventions in planning implementation for building healthy communities in China should be targeted.

National land use policies and strategies worldwide have attempted to establish a healthy housing rental market towards urban sustainability. Monitoring fine-scale housing rental prices should provide essential implications for equitable housing policies. However, doing so remains a challenge because aggregated data were traditionally collected at a coarse scale through census or social surveys. On-line housing rental websites (OHRWs) have become popular social media platforms in the housing studies. This paper attempts to demonstrate how to monitor fine-scale housing rental prices based on OHRWs using the case of Shenzhen in China. Employing hedonic model, a set of housing rental determinants are initially selected from three characteristics (neighborhood, location and structure) and at three levels (nearest accessibility, 15-minute walking distance availability and sub-district availability). Housing rent prediction models are then established (respectively for October 2017 and February 2018) using the training samples collected from the OHRWs and six machine-learning algorithms, including random forest regression (RFR), extra-trees regression (ETR), gradient-boosting regression (GBR), support vector regression (SVR), multi-layer perceptron neural network (MLP-NN) and k-nearest neighbor algorithm (k-NN). Thereafter, the relative importance of the determinants is calculated and visualized using partial dependence plots. Finally, the models are used to monitor housing rental price dynamics for all of the communities within Shenzhen. Results show that all of the algorithms except SVR generally present good performance. Among them, RFR and ETR are the best one in October 2017 and February 2018, respectively. Concerning the spatial pattern of housing rental, the high-high clusters merge in the central districts, whereas the low-low clusters are located in the outskirts, and the growth rate is the greatest in the farthest outskirts from the central districts. Each determinant affects the housing rent across different scale and sub-district availability and nearest accessibility are more important than 15-minute walking distance availability. The two most influential determinants are sub-district job opportunity and nearest accessibility to health care facilities. The case of Shenzhen shows that the demonstrated framework, which integrates machine-learning algorithms and the hedonic modeling, is practical and efficient. The approach is believed to provide an essential tool to inform equitable housing policies.

Transit Oriented Development (TOD), the integration of transport system with land use, has gained considerate priorities in planning strategies towards urban sustainability. To accrue a better overall leverage of the benefits arising from TOD practice, it is necessary to explore the variations and typology among TODs. This paper extends the classic ‘node (transport) – place (land use)’ model by incorporating the oriented characteristics that represent the morphological and functional ties between transport and land use. The model is applied to the case of Shanghai, China. Fuzzy AHP (Analytic Hierarchy Process) is used to construct the indicators system of each dimension (node, tie and place) in the model and typology among TODs is divided by SOM (self-organizing map). We find that the TOD index value declines from the urban center to the outskirts of the city obviously. Four typologies are identified among TODs, including the Integrated (all high node, tie and place index value), the Functionally place-developed (low node index value and high tie and place index value), the Morphologically node-developed (high node index value and low tie and place index value) and the Dispersed (all low node, tie and place index value). Based on the evaluation, we put forward an optimization plan for the areas with low TOD index values, which are consistent with the planned lines under construction. All the methods demonstrated in this study are easy to perform and can be widely applied to the assessment of TOD typologies worldwide. This study produces some generalized knowledge that are useful for implementing TOD practice within land use planning.

The goal of the present study is to demonstrate that high-poverty counties and robust classification features can be identified by machine learning approaches using only DMSP/OLS night-time...

Poverty remains one of the most serious chronic dilemmas facing civilization and economic development in the 21st century. How to accurately measure, identify and alleviate poverty have been urgent topics on different geographical scales for decades. Based on census data at the county level from 2000 to 2010 in China, principal component analysis was used to establish an integrated multidimensional poverty index (IMPI) for geographical identification of poverty-stricken counties using an indicators system guided by a sustainable livelihoods framework. Further cluster analysis, spatial analysis and a self-organizing map show obvious spatiotemporal heterogeneity of multidimensional poverty across the 2311 counties in China. The results demonstrate that the counties with higher IMPI are concentrated and conjointly distributed in southwest China, north of central China and southeast of northwest China in mountainous regions and plateaus. Longitudinal comparisons demonstrate that the degree of multidimensional poverty has relatively decreased across China from 2000 to 2010, but regional disparities continue to expand and new aspects are emerging. In addition, compared with 2000, the number of counties with multidimensional poverty in 2010 increased in northeast China and decreased in central China. Many counties have experienced generally increased levels in certain domains of poverty. The relative contribution of each indicator to the IMPI also provides important references for formulating and implementing poverty policy. Quantile regression was utilized to explore the application of the IMPI in assessing environmental inequality. The result indicates that many poverty-stricken and developed counties are exposed to poor air quality. The accurate identification of geographical and spatiotemporal patterns of poverty in China can lead to the implementation of anti-poverty strategies. This paper also offers new insights into poverty measurement for other developing countries.

Transport planning is gradually shifting targets towards modifying the pedestrian streetscapes that favor walking and enhance urban sustainability. We cannot be optimistic about walkability on condition that walkable streets are not evenly distributed within urban settings. Auditing the street walkability and associated social inequalities in developing countries should provide critical implications for transport planning. This paper identifies the most common indicators in existing indicator classification frameworks based on word frequency; and then establishes an indicator classification system (connectivity, accessibility, suitability, serviceability, and perceptibility) through expert panel evaluation for auditing street walkability in China. Using a case of Hangzhou metropolitan area, a set of 13 indicators are first selected by referring to the established indicator classification system and then aggregated into an integrated walkability index (IWI) through the catastrophe theory (CT) model after appropriate data treatment (standardization, normalization and correlation calculation). Camera signaling data are employed to validate the developed IWI. Results show that IWI is efficient to audit street walkability. IWI presents great heterogeneity at segment level. In general, higher walkability is observed in streets across the central and western areas. Walkability profiles of the segments are further produced by the clustering approach and the categorized groups can inform more targeted planning options. Spatial regression is finally utilized to quantify the associations between community socio-demographics and neighborhood IWI at two levels. We discover that lower neighborhood walkability would be observed in socioeconomically disadvantaged communities. These results suggest that significant social inequalities in street walkability should exist across the communities within Hangzhou metropolitan area. The developed IWI not only brings a standardized measurement of street walkability in the field of transport planning, but also paves the way for addressing the social inequalities in street design across various jurisdictions.

Poly-centrality is an essential feature of megaregions and is generally considered an important source of megaregional vitality and competitiveness. Numerous studies have highlighted the growing trends of poly-centrality across different megaregions worldwide. However, with accelerated urbanization, some observations suggest that the existing polycentric urban theories must be improved and that decision-makers require more knowledge on the potential trends, spatial characteristics and influencing factors of polycentric development to implement sustainable regional governance under this context. This paper proposes a series of hypotheses on spatiotemporal dynamics of polycentric megaregion evolution and verifies them in an empirical study on the Megaregion around Hangzhou Bay (MAHB). A new algorithm, which is applicable for long time series data, is proposed to evaluate and monitor the status of polycentric development in MAHB. In particular, landscape metrics and average nearest neighbor analysis are employed in the first stage of our algorithm which is designed to implement data compression. Then, the gravity model, minimum spanning tree (MST) and weighed betweenness centrality indicator form the second stage which aims to construct and explore the polycentric megaregional network. Results show that: (1) the speed and patterns of built-up land expansion significantly affect the number and distribution of megaregion centers; (2) the value of poly-centrality reaches a maximum in approximately 2013 and then declines, which can be well fitted by a Gaussian curve; and (3) the variation of poly-centrality is successively dominated by an increase and decrease in the number of centers with urbanization. This paper renews the assumptions in current polycentric evolution models and provides evidences for single-peaked change of megaregion center number instead of “wave-like”, “scaling-up” or monotonous change proposed in existing urban theories. The proposed algorithm and assumed mechanisms can also be applied to model and explore poly-centrality dynamics of other megaregions around the world. The results are believed to provide critical implications for polycentric policies.

The urban surface thermal environment has greatly changed in China's accelerated urbanization process, which is closely related to urban eco-security, socioeconomic activities, and residents’ comfort and health. Recent studies have focused on the effects of urban form optimization on the urban surface thermal environment, but few have been conducted from an urban polycentric structure perspective. This paper quantified the association between the urban polycentric structure and urban surface thermal environment of Chinese major cities at the prefecture level and above in 2015, 2010 and 2003 using spatial regression. The results revealed that (1) no significant association were found between the urban polycentric structure and the average land surface temperature (LST). (2) Both the increase in the urban center number (N) and the degree of polycentricity (POLY) are conducive to a uniform LST distribution and an increase in the number of high-temperature patches. In 2003, 2010 and 2015, the effect coefficients of POLY and N on the standard deviation of LST ranges from -0.126 to 0.102 and from -0.132 to -0.094, respectively, and on the number of different high-temperature patches ranges from 0.067 to 0.197 and from 0.098 to 0.502, respectively. (3) The increase in the degree of polycentricity is correlated with smaller high-temperature patches. In 2003, 2010 and 2015, a one-unit increase in POLY was associated with a 0.097- to 0.124-unit decrease in the average area of different high-temperature patches. The findings suggest that an urban polycentric structure is conducive to forming smaller and more fragmented high-temperature patches, which contributes to a uniform LST distribution and exerts positive effects on alleviating urban heat aggregation. This work enriches the research on the ecological performance of urban polycentric structure and provides a preliminary but effective basis for the formulation of polycentric development strategies from an urban surface thermal environment improvement perspective.

Poverty is a major problem that has consistently impacted human development. The spatial characteristics of poverty are nonvolatile at the regional level and represent the primary factors underlying the spatial poverty trap. Previous research on spatial poverty has frequently attempted to theoretically demonstrate the relationship between the spatial distribution of socioeconomic factors and regional poverty by drawing poverty maps, whereas less attention has focused on geographic factors. The case of Miao–Dong Autonomous Prefecture of Qiandongnan in Guizhou Province, China, is examined herein. This paper proposes a theoretical framework for the mechanisms linking the geographical environment and regional poverty and then conducts a quantitative analysis of the spatial association and degree of influence of these mechanisms. A geographically weighted regression (GWR) is employed to analyse the explanatory ability of variables in local areas. The results show that the mean slope and road network density have the most statistically significant correlations with the town-level poverty headcount ratios in Qiandongnan. Specifically, mountainous regions with steeper slopes suffer from a higher poverty headcount ratio, and towns with higher road network densities have a lower poverty headcount ratio because of the convenience of transportation. Additionally, the GWR indicates the occurrence of spatial nonstationarity, which indicates that the nature and strength of the association between each geographic indicator and the poverty headcount ratio varies across the 201 towns, with a linear growth pattern observed from north to south. Moreover, the results of a correlation analysis demonstrate that land cover with different slope classifications is more related to the regional poverty headcount ratio than land cover without a slope classification. We expect that this paper will provide useful insights for incorporating geographical factors into the formulation of poverty reduction policies.

Tea expansion, a typical process of regional land use and cover change (LUCC), has raised great concerns on regional sustainability. In this regard, exploring the determinants of tea expansion should provide critical implications for land use policy. It has been widely recognized that LUCC interacts nonlinearly with a set of determinants and their feedbacks should be rather complex. Policy makers are now facing the challenge to identify, apportion, and compare the determinants of regional tea expansion for designing more targeted political intervenes. Our paper utilizes a robust tool, the random forest (RF) regression in particular, to explore the determinants of tea expansion across two periods (1985–2007 and 2007–2016) in Anji County, a typical region of tea production in subtropical China. More specifically, tea is extracted from Landsat imageries and total tea cultivated area acts as the dependent variable. Exploratory variables include 38 potential determinants and these determinants are divided into two categories (biophysical and socioeconomic) at two levels (pixel and village). We obtain some similar findings, though the relative importance of determinants varies with the two periods. In general, biophysical determinants (e.g., topography, soil type, land use in the neighborhood) present greater relative importance than the socioeconomic determinants in both periods. In period 1985–2007, biophysical determinants at pixel level are more essential in governing tea expansion. In period 2007–2016, the relative importance of pixel level biophysical determinants is comparable with that of the village level determinants. Comparisons of the two periods indicate that relative importance of soil type and socioeconomic proximity becomes greater in period 2007–2016, while that of the total employees and non-agricultural population proportion becomes lower. Partial dependency plots are further drawn to visualize the marginal effect of each determinant. We finally propose three options for land use policy towards sustainability. Our study demonstrates that the RF regression is efficient for policy makers to understand the determinants of tea expansion with a nonlinear and complex nature.

The expansion of cash crops has raised contradicting interests between two bureaucratic bodies (the economy-oriented one that advocates cash crop production and the conservation-oriented one that focuses on natural resources protection) in many places around the world. Recent past has saw growing efforts on the theoretical linkages between cash crop production and conservation, but the solutions to the cash cropping −related land use conflicts remain as violent controversy. Using a geo-simulation approach, this paper models the tea expansion under different policy scenarios and evaluates the effectiveness of these policies in Anji County (China), as a contribution to the scientific basis for formulating sustainable cash cropping practices and alternative land use policies. In particular, a new self-adaptive cellular automaton model based on ensemble learning (EL-CA) is developed and three policy scenarios (economy-over-conservation (EOC), conversion-over-economy (COE), and economy-balance-conservation (EBC)) are set to predict the tea expansion patterns in 2025. Results show that the EL-CA model significantly outperforms the traditional CA models based on empirical statistics. We find that the tea expansion under the EOC scenario is much more intensive than that under the COE and EBC scenarios. The most outstanding ecological consequence of tea expansion is the occupation of forests. Employing an equivalent coefficient approach, we further quantify the trade-offs between economic incomes (from tea expansion) and ecological loss (due to ecosystem service value (ESV) declines) under the three policy scenarios. In the EOC scenario, the loss in ESV far exceeds the benefit of tea expansion. Net change of ESV is higher than that of economic return under the COE. The economic benefit is approximately equal to the ecological loss in the EBC scenario. The EBC should be a socially preferred scenario, since it leads to sustainable tea expansion and minimal ecological impacts. Though the EBC scenario is a desirable choice, how to enforce these policies is an important consideration. Given the complexity in the Chinese policy context, we finally propose several possible measures to promote the coherence of paradoxical policies involving the allocation of land for cash crop cultivation.

International land use planners tend to focus on walkability that could shape both residential behaviors and health outcomes. Understanding the relationships among community deprivation, walkability, and health outcomes will provide insights into appropriate land use planning that supports public health promotion. This paper develops a revised Walk Score tool for measuring community walkability in China. Under the original Walk Score methodological framework, (1) 6 principle amenities (19 items) and their weight (utilization frequency) are first selected by expert panel evaluation and questionnaire survey; (2) a tolerance time approach is then employed to determine the decay function, and the walking travel time from community to each amenity is calculated by using the Baidu Map; and (3) three pedestrian characteristic factors (intersection density, block length, and slope) are considered to adjust the score. We apply the proposed methodology to the case of Shenzhen and discover great variations in walkability among the 8117 communities within it. The high–high clusters are located in the central blocks, while the low–low clusters emerge in the outskirts. Using spatial regression, we observe significant negative associations between community walkability and three health indicators (cardiopathy, hypertension, and liver cancer). It suggests that better health outcomes would be observed in more walkable communities. We further find that children concentrated and socioeconomically disadvantaged communities exhibit lower walkability. These results evidence the significant social inequalities in walkability among the communities of Shenzhen. Path analysis identifies complex linkages among community deprivation, walkability, and public health. For cadiopathy and hypertension, three categories of significant paths are identified: (1) residents in deprived community have worse health outcomes due to lower walkability; (2) poorer health outcomes occur in deprived communities as a result of higher PM2.5 exposure; and (3) less walkable communities are exposed to greater PM2.5 concentration and deprived communities are generally characterized by lower walkability and consequently have worse health outcomes. We argue that social inequalities in walkability should attract the attention of land use planners. In order to address this pressing issue, three areas should be given priorities in future land use planning: (1) adopting urban form-based zoning schemes; (2) economic inputs for streetscape improvements; and (3) formulating affordable and low-renting housing policies. Findings of this case should generate more generalized knowledge that enhances public health promotion within the land use planning context.

Real estate premium associated with landscape amenities is a well-studied topic with a primary focus on housing prices. Presumably, the willingness-to-pay for landscape amenities should be very different between homeowners and tenants. Thus far, how landscape amenities affect residential rental prices is not well understood. This paper takes advantage of the big data of online housing advertisements to unravel how landscape amenities are capitalized into rental prices across five Chinese megacities (Beijing, Shanghai, Shenzhen, Hangzhou and Wuhan). Natural language processing, the latent Dirichlet allocation in particular, is first employed to semantically analyze the geo-textual advertisements. It reveals that ‘landscape amenities’ is a typical topic and ‘park’ is a typical component for housing advertisements in the five megacities. The lexicon-based sentimental analysis further shows that the strength of the sentiments associated with the ‘landscape amenities’ varies with cities. A series of hierarchical hedonic models based on the extracted semantic and sentimental aspects are then established for each megacity after segmenting the rental market into submarkets. The capitalization effect of landscape amenities is significant in Beijing, Hangzhou and Wuhan, while it is not significant in Shanghai and Shenzhen. Finally, variance decomposition analysis and marginal implicit price calculation unveil to what extent landscape amenities contribute to residential rental prices. Based on these findings, we discuss several major implications for urban planning. Our study unsettles the popular presumption that landscape amenities are key determinants of real estate values. It renews our understanding of the economic values of landscape amenities theoretically and methodologically.

Identifying the geographic units with restricted access to intra-urban parks has become a hot issue in transport studies. Previous literature has examined the social inequalities of park accessibility under the Western context; however, the issue has seldomly investigated against the non-Western background, especially in China. Using a case of Shenzhen (China), this paper examines the accessibility to parks of three quality levels (official standard) under four transport modes (public transit, walking, bicycle, and private car). In particular, the daily travel time from each community (8117) to each park (625) was harvested from the Baidu Map during 18:30–20:30 in July 2016. We further, based on the travel time calculations, develop four baseline indicators (the weighted average, the minimum, the maximum, and the standard deviation travel time) and three tolerance indicators of park accessibility (weighted average travel time within visit tolerance thresholds, standard deviation travel time within visit tolerance thresholds, and number of parks within visit tolerance thresholds) to measure park accessibility for each community. Results show that the seven accessibility indicators generate different estimations and the quantified accessibility varies greatly with park quality levels and transport modes. Communities present greater variations in accessibility to the first quality level and second quality level parks via walking and public transit. In addition, hierarchical regression is utilized to quantify the relationships between park accessibility and sociodemographic characteristics at two geographic levels (community and district). It is found that the associations are subjected to park quality, transport modes, and geographic levels. In particular, we discover significant social inequalities in park accessibility under the mode of public transit, walking, and bicycle. Our study should provide some new insights into accessibility research and advance the understanding of unequal park provision in developing countries.

Urban studies attempt to identify the geographic areas with restricted access to healthy and affordable foods (defined as food deserts in the literature). While prior publications have reported the socioeconomic disparities in healthy food accessibility, little evidence has been released from developing countries, especially in China. This paper proposes a geo-big data approach to measuring transit-varying healthy food accessibility and applies it to identify the food deserts within Shenzhen, China. In particular, we develop a crawling tool to harvest the daily travel time from each community (8117) to each healthy food store (102) from the Baidu Map under four transport modes (walking, public transit, private car, and bicycle) during 17:30–20:30 in June 2016. Based on the travel time calculations, we develop four travel time indicators to measure the healthy food accessibility: the minimum, the maximum, the weighted average, and the standard deviation. Results show that the four accessibility indicators generate different estimations and the nearest service (minimum time) alone fails to reflect the multidimensional nature of healthy food accessibility. The communities within Shenzhen present quite different typology with respect to healthy food accessibility under different transport modes. Multilevel additive regression is further applied to examine the associations between healthy food accessibility and nested socioeconomic characteristics at two geographic levels (community and district). We discover that the associations are divergent with transport modes and with geographic levels. More specifically, significant social equalities in healthy food accessibility are identified via walking, public transit, and bicycle in Shenzhen. Based on the associations, we finally map the food deserts and propose corresponding planning strategies. The methods demonstrated in this study should offer deeper spatial insights into intra-urban foodscapes and provide more nuanced understanding of food deserts in urban settings of developing countries.

Megaregion has emerged as a new dimension of global urbanization. A megaregion approach based on polycentric strategy is deemed to enhance regional economic competitiveness. Numerous studies have highlighted the economic benefits, celebrated the spill-over effects, and outlined the creative potentials of contemporary megaregion policies in different nations worldwide. However, further policy instruments require the knowledge about the achievement and failure of megaregion policies that seek for spatial, economic, social, and environmental efficiency and sustainability. This paper introduces China’s megaregion policy and proposes an analytical framework for performance evaluation from four principle domains (rational urban growth, economic development, social equity, environmental protection) at three levels (internal collaboration, integral development, and overall development). Using a case of the Megaregion around Hangzhou Bay (MAHB), we find very limited success of China’s megaregion policy. In particular, the megaregion policy only accomplishes the economic goal, and fails to achieve the goals of rational urban growth, environmental protection, and social equity. A series of mechanism based regressions are established and show that the implementation duration of megaregion policy: (1) associates positively with the economic growth; (2) relates negatively to social equity and urban rational growth; and (3) has no significant relationship with improved environmental quality. Institutional fragmentation, no unified spatial planning, and inadequate legislation at megaregional level are the underlying causes of the expected performances within the policy context of governance itself. We finally propose some possible solutions and discuss the implications for spatial polycentric governance. The demonstrated methodological framework can be applicable to other megaregions around the world. This paper is thus believed to provide some new insights for land use policy.

Expanding numbers of homogenous geographical units with social and economic shortages and disadvantages (defined as social deprivation in the literature) have emerged in many cities around the world, particularly in developing countries. Consensus has been reached on the acknowledgement of social deprivation as a major challenge for humanity and sustainability. It therefore requires strong knowledge to measure, identify, and target the deprived units at different scales. Based on census data at prefecture city level from 2000 to 2010, this paper employs the principle component analysis to formulate a family of indices to measure China's social deprivation. In particular, one integrated social deprivation index (ISDI) and five sub-indices (general socioeconomic deprivation, disadvantaged population, education deprivation, housing deprivation, and occupation deprivation) are developed. The Receiver Operating Characteristic curve confirms that the ISDI is valid and efficient in social deprivation measurement. Social deprivation shows obvious spatiotemporal heterogeneity across the 333 cities in China. It roughly presents the geography that cities with lower ISDI are mainly distributed in eastern and northeastern China, while cities of higher social deprivation are generally located in the central and western regions. Comparisons between 2000 and 2010 indicate that only the capital cities remain at lower levels in the five domains. Many cities have experienced increased levels in certain domains. Quantile regression is finally utilized to explore the applications of ISDI in three major urban issues (urban policy performances evaluation, environmental inequalities assessment, and social injustice evaluation). Social deprivation is negatively associated with urbanization indicators at 5 representative quantiles (the 10th, 25th, 50th, 75th and 90th quantile), annual mean PM2.5 concentration at first 10 percentiles and urban public green spaces (UPGS) at almost all percentiles. These results respectively donate that urbanization policy has played a positive role in eliminating social inequalities, the least deprived 30 cities in China could be exposed to higher PM2.5 concentrations, and the highly deprived cities have fewer urban public green spaces. Our study demonstrates that the ISDI has promising applicability in addressing urban issues in China. The methodological framework is not restricted in China and can be employed to other developing countries around the world. This paper is believed to offer new insights into habitat research.

The volunteered geographic information (VGI) gains increasing popularity with the general public and scientific community. However, the optimism about the VGI has been tempered by two critical issues: inequality in data coverage (social justice) and data quality. It therefore requires a better understanding of the mechanism driving VGI contributions and content quality. With a case of China, this paper demonstrates one potential avenue, examining the associations between VGI coverage/quality and local demographic and socioeconomic characteristics. In particular, VGI data are harvested from the OpenStreetMap for 333 cities in China. VGI coverage is measured by the total volume of different geographic features (point, line and polygon); and VGI quality is described from two aspects: completeness and accuracy. Geographically weighted regression (GWR) shows that both demographic and socioeconomic factors have statistically significant influences on VGI coverage and quality. More specifically, densely populous cities with more young, educated and non-agricultural people enjoy higher VGI coverage and quality. Cities with lower VGI coverage and quality are primarily located in the western and southwestern regions where the ethnic minorities concentrate. High VGI coverage and quality are possibly observed in economically developed cities with high marketization degree. Besides, possibility of high VGI coverage and quality occurs in cities with more labor in scientific research and greater percentage of employers in the tertiary industry. The GWR also demonstrates that the strength and nature of the obtained relationships vary across the 333 cities. The spatial non-stationary relationships may partially answer for the controversial empirical conclusions in earlier case studies at different scales. Quantitative analysis (Gini index, Lorenz curve and Moran's I index) further evidences the great inequality in VGI coverage and quality. It can be safely inferred that the differences in engagement and use of VGI, as a new digital divide, can raise troubling concerns on the social justice implications.

Past literature has reported the ubiquitous differences in health outcomes associated with socioeconomic status across different international settings. Due to the absence of fine level aggregated data, however, rather limited studies have been conducted to explore the neighborhood social determinants of public health in developing countries, especially in China. Using district level official data in 2010, this study examines the neighborhood social determinants of three prevalent non-communicable diseases (NCDs) in Shenzhen, China. In total, 22 exploratory social variables are selected from five domains: employment, education, housing, demographic structure and income. Spatial regression in binary and stepwise form is used to recognize the most influential social determinants. Results indicate the social determinants vary with the three NCDs and they have some general similarities. Spatial binary regression identifies four principle social determinants of NCDs prevalence: employment, income, housing and demographic structure. One essential social determinant (percentage of the floating population) is identified for all the three diseases by stepwise regression. Our study highlights that the main social determinant related to the incidence rates of all NCDs is the indicator of demographic structure denoting percentage of the floating population. This paper advances the understanding of the essential social determinants of NCDs in China.

Metropolitan has progressively emerged as a new form of urban governance. Understanding the urban expansion process and its determinants at metropolitan level should provide critical references for urban management and planning. Using time-series nighttime stable light data (1993–2012), this paper captures the full continuum of urban expansion process in 30 major metropolitans of China. Greater expansion intensity and faster expansion speed are observed in metropolitans along the eastern coast. Conversely, the metropolitans in northeastern China exhibit lower urban expansion intensity and speed. Potential determinants are selected based on extensive literature review from four categories: economic, social, demographic, and natural. Multivariable linear regression and variances partitioning are employed to identify and compare the determinants of urban expansion and their relative importance across the 30 metropolitans. Results show that urban expansion is not only subjected to different categories of determinants but also influenced by their combinations. More specifically, the identified determinants vary with metropolitans but present some similarities. Economic growth, industrial development, and economic structural transformation are the key economic determinants. Population and labor growth, population urbanization, and population structure act as essential demographic determinants. Primary social determinants include infrastructure construction, energy consumption, and real estate development, while dominant natural determinants are the amount and topology of available land. Cluster analysis is further applied and it generates three major driving forces profiles among the 30 metropolitans, with respect to the relative importance of different categories of determinants and their combinations. The driving force profiles should be useful for managers to set priorities in decision making procedure and develop optimal urban strategies. Our study is believed to advance the understanding of driving mechanism governing urban expansion at metropolitan level and further provide deeper spatial insights for international habitat research.

The increasing health burdens pose great challenge for planners and policy makers to create a more livable city. Recent literature has linked land use to a wide range of illness and death causes in developed nations. It therefore requires evidence-driven and replicable frameworks to inform land use decisions that integrate with health promotion. This paper proposes a theoretical framework for the mechanistic links between neighborhood land use and public health; and then quantifies their associations at district level, using the case of Shenzhen in developing China. Data for 15 health indicators in 2010 are collected at district level; and a set of standardized land use variables (individual land use type, general land use mix, and physical street conditions) are proposed to comprehensively reflect the neighborhood land use characteristics. Spatial regression is employed to analyze the relationships between health outcomes and land use variables. Results show that the land use exploratory variables vary with health indicators, but several key land use determinants are identified: green land abundance, green land morphology, institutional land proximity, industrial land proximity, blue land abundance, street walkability and street connectivity. Structural equation modeling is further applied to examine the possible mediators and it demonstrates that the casual pathways between land use and public health are quite complex. The developed set of standardized land use variables can serve as practical tools to facilitate land use decisions with health considerations. In practice, several complexities at governance levels challenge the efforts to reconnect land use policies and public health promotion. We put forward three useful solutions to enhancing health connection with land use policy across different international settings. This study captures the nuanced health disparities varied with districts and indicators, and therefore produces some more generalized knowledge that ultimately helps develop land use policies aimed at promoting public health at home and abroad.

Housing has been acknowledged as one of the principle levels at which neighborhood deprivation occurs in recent literature. The urban political community thus advocates to devise research programs for examining the association between neighborhood housing deprivation and public health. Based on previous social medical studies, this paper proposes a theoretical framework for the mechanistic links between neighborhood housing deprivation and public health; and then quantifies their associations at district level in the case of Shenzhen, China. Data at district level in 2010 are collected for five health outcomes (cardiopathy, chronic pneumonia, general physical fitness, liver cancer, and mental health hospitalizations). Using varimax rotated principle component analysis, we establish an integrated neighborhood housing deprivation index (INHDI) by combining six domains of variables that comprehensively reflect the housing disadvantage (internal facilities, living space, physical form and structure, attached facilities, affiliated natural amenities, affordability). Spatial regression is employed to quantify the association between INHDI and public health, using income, occupation and education as control variables. Results show that housing deprivation acts as a significant exploratory factor for all the health outcomes. Variances decomposition shows that the individual contribution of housing deprivation is stronger than that of socioeconomic disadvantage for cardiopathy, chronic pneumonia and liver cancer. Differently, general physical fitness and mental health hospitalizations are more subjected to the joint impacts of housing deprivation and socioeconomic disadvantage. Path analysis demonstrates that the casual pathways between housing deprivation and public health are quite complex. The developed INHDI can serve as a practical tool to facilitate urban planning with health considerations. This study captures the nuanced health disparities varied with housing deprivation, and therefore produces some more generalized knowledge that ultimately help develop housing construction plans aimed at promoting public health.

Transit Oriented Development (TOD), as a widely practiced planning strategy toward urban sustainability, commonly refers to the integration of transport and land use in the form of an integrated transit station area surrounded by compact urban development and high-quality walkable environment. Although policymakers are very enthusiastic about the potential of TOD in boosting the development of real estate market and livable neighborhoods, how TOD characteristics affect housing rental prices has not well been gauged with empirical data. Taking advantage of online housing platforms, this paper attempts to fill this gap through explicitly unraveling the impact of TOD on housing rental prices across five major Chinese megacities (Beijing, Shanghai, Shenzhen, Hangzhou and Wuhan). We first propose a conceptual framework to explain why and how TOD impacts housing rental prices. TODs are then delineated in the five megacities and T-test confirms that TOD housing properties present significant higher rental prices than non-TOD ones. Spatial hedonic modeling is employed to identify the relationship between TOD characteristics (e.g., metro station, neighborhood and synergy) and housing rental prices in each megacity, and the variable decomposition is further utilized to quantify their relative contribution. Regardless the variations among the five megacities, it is discovered that TOD characteristics generally account for 10%–20% of the housing rental prices and the TOD neighborhood presents the highest relative contribution. The synergy between the metro station and the neighborhood also plays an essential role. Metro station type rather than metro station proximity acts as a significant exploratory variable. These findings foreground that the essence of TOD lies in high-quality urban (neighborhood) development, which are often overlooked in previous studies that overemphasize the importance of “transit” (station) itself. Based on the identified TOD-generated housing rental premiums, we offer five recommendations for TOD practices to be integrated into China's territorial spatial planning. This study renews our understanding of the outcomes of TOD both conceptually and methodologically.

Transit-oriented development (TOD), which is regarded as an efficient planning strategy for urban sustainability, has surged in use across the globe in the recent past. While the lessons learned from case studies of individual cities can provide valuable references, they also result in a gap between existing theoretical principles and actual planning practices. The comparative analysis of TOD typologies among cities affords unique strengths for addressing such a challenge. By extending the classic ‘node–place’ model with a third dimension, this paper first constructs a ‘node–functionality–place’ model in the form of a magic cube as the theoretical basis for classifying TOD typologies. Then, the model is applied to five typical Chinese megacities, namely, Beijing, Shanghai, Shenzhen, Wuhan and Hangzhou. After establishing an indicator system, the analytic hierarchy process (AHP) is used to determine the TOD degree, and a two-step cluster is employed to classify TOD typologies. The results show that the TOD degree, although it varies with cities, presents a similar spatial pattern, with a general tendency to decline from the central core to the outskirts. In total, six TOD types are distinguished and present notable variations within and across the five megacities. The identified TOD typologies thus equip urban planners and policymakers with a useful tool for designing more targeted strategies. The discoveries made in the comparative context differ from those made in the individual context in prior studies. This paper is thus believed to make a new contribution to the existing TOD literature.

The socioeconomic status (SES) related disparities in healthy food accessibility have been heatedly debated. Theoretically, a gap remains in the literature about how healthy food accessibility changes over time and the relative contribution of local factors to the change. Methodologically, one main obstacle to this issue emerges on how to address the nested structural interactions among SESs across different levels. This paper first proposes a novel theoretical framework on healthy food accessibility from a spatiotemporally dynamic perspective. Guided by this framework, we employ the enhanced two-step floating catchment area model to examine the dynamic accessibility to four types of healthy food stores (i.e., vegetable markets, supermarkets, seafood markets, and fruit stores) from 2016 to 2018 across communities within the Hangzhou metropolitan area, China. The chain substitution method is further utilized to quantify the relative contributions of local factors, including population demand, healthy food supply, and transportation linkage. Multilevel regression is finally applied to analyze the associations between healthy food accessibility and the nested SESs across community and subdistrict levels. Results reveal that the dynamics of healthy food accessibility and the contributions of the local factors substantially vary depending on the type of healthy food stores and over time. Social inequality in accessibility to supermarkets and fruit stores is much greater than that to vegetable markets. The impact of the SES at the subdistrict level is greater than that at the community level. This study demonstrates a new methodological procedure and can offer new insights into healthy food accessibility in rapidly urbanizing metropolitan areas.

Understanding the geographical preferences of international tourists is critical for the tourism planning and marketing. However, it is not an easy endeavor to gather the corresponding information, given the absence of city-level tourism statistical data and high costs of participant survey. This paper characterizes the geographical preferences of international tourists using geo-tagged photos on social media (the Flickr in particular). Data are harvested for 333 prefecture-level cities in China from 2008 to 2013, and the intensity of photo sharing (IPS: the ratio between total number of uploaded photos and total area of the city/region) is used an indicator of tourist geographical preferences. IPS visualization shows the geography that tourism hotpots generally concentrate in regional capital cities and economically developed megaregions. More specifically, the East Asia and Oceania tourists exhibit more preferences towards the eastern coastal cities, while the Europe and North America visitors show increasing interest in exploring the western and northern places. Spatial regression is employed to quantify the local influential factors of tourists’ geographical preferences. It is found that international tourists usually consider the local economy, accessibility, infrastructure and cultural attractions when they choose their destinations in China. East Asia and Oceania visitors particularly appreciate the local economy and cultural attractions. Europe and North America tourists especially value the cultural attractions and local openness. The demonstrated methodological framework is not restricted to Flickr data, and it can be applicable to social media offer geo-tagging service. This paper is therefore believed to advance the applications of social media into geographical research.

Forest loss and fragmentation, which generate various negative environmental and ecological consequences, have become widespread phenomena across the globe. Motivation to investigate the underlying drivers is essential for land use planning and policy decision making. This paper characterizes forest loss and fragmentation from 1979 to 2014 in the Ningbo region (China) using multitemporal satellite imageries and a set of landscape metrics (area-weighted mean patch area, edge density, area-weighted shape index, Euclidean nearest neighbor distance, effective mesh size and total area); and then quantifies the responsible socioeconomic drivers (economy, social activities, science and technology, culture and policy, demography) under different land use planning schemes (urban and non-urban) using multivariate linear regression. Results show that the two zones present identical trend of intensifying forest loss and fragmentation but differ in changing magnitude and speed. More specifically, forest loss and fragmentation in the non-urban planning zone occurs at a significantly higher pace and magnitude. For the urban planning zone, population pressure, economic growth and fruit consumption are the primary drivers of forest loss, while forest fragmentation is mainly driven by economic openness, cash crop consumption and environmental protection consciousness. For the non-urban planning zone, income increases, fruit consumption and infrastructure development are the primary drivers of forest loss, while infrastructure and tourism development are the major drivers of forest fragmentation. Besides, forest loss and fragmentation in the two zones are both heavily subjected to land use policy. The variance partitioning analysis highlights that the policy driver is the most influential one and economic driver also has strong effect on forest loss and fragmentation in the urban planning zone. For the non-urban planning zone, the influence of policy driver is the strongest and social activity is also very powerful. These results provide compelling evidence that land use planning fails to play an efficient role in protecting forest resources in the Ningbo region. The failure should be attributed to several issues associated with land use planning and forestry governance that widely exist in China. We finally propose some pertinent implications and suggestions for China’s land use planning and forest policy. This study is believed to advance the understanding of the socioeconomic drivers of forest loss and fragmentation. It therefore provides some new insights in land use policy.