Inequality, Bi-Polarization and Mobility of Urban Infrastructure Investment in China’s Urban System

Ma, Lie; Li, Dandan; Tao, Xiaobo; Dong, Haifeng; He, Bao‐Jie; Ye, Xiaosu

doi:10.3390/su9091600

Cited by 5 publications

(4 citation statements)

References 80 publications

(132 reference statements)

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“…However, most of these studies have been conducted in large cities, and small cities are rarely examined [1]. Small cities are an important part of the urbanization process and they have begun to face "urban diseases" in China, such as traffic congestion (Figure 1) [2,3], environment pollution [4], lack of infrastructure [5,6], and so forth. Therefore, it is necessary to examine the distribution law of small urban populations.…”

Section: Introductionmentioning

confidence: 99%

Research on Population Spatiotemporal Aggregation Characteristics of a Small City: A Case Study on Shehong County Based on Baidu Heat Maps

Ding

Sun

2019

Sustainability

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Baidu heat maps can be used to explore the pattern of individual citizens conducting their activities and their agglomeration effects at the city scale. To investigate the spatiotemporal pattern of population aggregation and its relationship with land parcel attributes in small cities, we collected Baidu heat map data for a weekday and a weekend day in Shehong County and used Getis–Ord general G and the raster overlay methods to analyze population aggregation spatiotemporal characteristics. Chi-squared and Pearson correlation tests were used to analyze the correlation between population aggregation and land parcel attributes against three types of land parcel divisions: land use parcels, road network blocks, and grids. The results showed that, (1) for most hours of the workday, the degree of population aggregation was greater than on the weekend, and the fluctuation magnitude on the workday was higher as well. (2) On the weekday, people clustered and dispersed faster than on the weekend. (3) On the weekday and weekend, the spatial position of people aggregation was highly overlapping. (4) The correlation between the degree of population aggregation and the type of parcel was not significant. (5) Regarding different parcel unit sizes, the correlations between population aggregation degree and point of interest (POI) density, floor area ratio, and building density were significant and positively correlated, and the correlation coefficients increased as the grid size increased.

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Section: Introductionmentioning

confidence: 99%

Research on Population Spatiotemporal Aggregation Characteristics of a Small City: A Case Study on Shehong County Based on Baidu Heat Maps

Ding

Sun

2019

Sustainability

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“…We applied the densi-graph method based on POIs to obtain the urbanized land area. The reason behind this choice is that urban infrastructure on urbanized land should be sufficient and that the spatial distribution of these POIs should conform to certain density patterns [76]. We also compared the urbanized land area extracted through the densi-graph method and the urban area extracted from remote sensing images.…”

Section: Discussionmentioning

confidence: 99%

Sustainable Land Urbanization and Ecological Carrying Capacity: A Spatially Explicit Perspective

et al. 2018

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Rapid urbanization has become a common occurrence all over the world, particularly in developing countries, and has thus resulted in various eco-environmental problems. In China, urban land has expanded at an unprecedented rate in the past several decades, and sustainable land urbanization has become an important issue in promoting sustainable development. Hence, scholars have proposed ecological carrying capacity (ECC) as a solution to balance socio-economic development and the ecosystems for achieving sustainable development. In the current work, we explored the spatial influence of ECC on land urbanization and its driving mechanism, using the Wuhan agglomeration as a case study. In the first step, we calculated the ECC at the county level using the ecological footprint method. Then, we applied a combination of kernel density and the “densi-graph method” on the basis of points of interest, in order to identify urbanized areas and to measure land urbanization rates. Finally, we devised spatial models with ECC-based spatial weight matrices to examine the potential spatial interactions or constraints and the influencing factors. Results indicate the following. (1) Land urbanization rates in most counties increased, whereas the average ECC per capita in the Wuhan urban agglomeration decreased from 2010 to 2015; (2) China’s land urbanization is primarily driven by socio-economic development, in which fixed asset investments and urban income present positive influences and agricultural outputs show a negative influence; (3) Spatial interaction was formulated through ECC during the land urbanization process. However, this effect was attenuated in 2010–2015. The findings are beneficial for understanding the regional spatial influence of ECC on urban land urbanization. They should also facilitate the formulation of relevant policies for protecting, restoring, and promoting the sustainable use of terrestrial ecosystems to ultimately achieve coordinated and balanced regional development.

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“…Several studies show that uneven spatial distribution and accessibility of service infrastructures are influenced by geographical characteristics of the place, urbanization, population density, transportation and connectivity, resource allocation and investment (CBS 2011;Gu et al 2019;Ma et al 2017;Yin et al 2018). In this study, we used locational phenomena to explain the spatial disparities of service infrastructure in the GDR.…”

Section: Explaining the Observed Distribution Patternmentioning

confidence: 99%

Spatial distribution of strategic service infrastructures that favour quality of life (QOL) in the Greater Dhaka Region (GDR), Bangladesh

2021

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This paper illustrates the spatial distribution pattern of the basic services infrastructures in the Greater Dhaka Region (GDR), Bangladesh that favour a quality life to live. The study employed the modified Human Development Index (here we termed as Service Infrastructure Index) to assess the spatial inequalities of service infrastructures at the sub-district level. Total 13 indicators from three major dimensions, such as housing and related services, education and health were used to estimate the service infrastructure index. Spatial autocorrelation based on the Global Moran's I was used to assess the statistical significance of the observed distribution pattern and Local Moran's I (cluster and outlier analysis) was employed to map the locations of infrastructure concentrations. The study found that the basic service infrastructures are unevenly distributed across the sub-districts. The index score ranges from 0.12 in Daulatpur of Manikganj district to 0.80 in Dhaka Metropolitan Area (DMA) while the regional mean is 0.29. The core city (DMA) and the areas near the core city (especially the Gazipur district) with a higher level of urbanisation and inter-regional connectivity enjoy better services. This study identified where the infrastructures are clustered and which type of infrastructure lack in different areas. Thus, it is expected that demand-based infrastructure development should get priority in the development agenda of the region.

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Inequality, Bi-Polarization and Mobility of Urban Infrastructure Investment in China’s Urban System

Cited by 5 publications

References 80 publications

Research on Population Spatiotemporal Aggregation Characteristics of a Small City: A Case Study on Shehong County Based on Baidu Heat Maps

Research on Population Spatiotemporal Aggregation Characteristics of a Small City: A Case Study on Shehong County Based on Baidu Heat Maps

Sustainable Land Urbanization and Ecological Carrying Capacity: A Spatially Explicit Perspective

Spatial distribution of strategic service infrastructures that favour quality of life (QOL) in the Greater Dhaka Region (GDR), Bangladesh

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