Urban Form

How mixed is Beijing?

Long and Liu 2013 EPA_Mixed.pdf
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Parcel Direction

The parcel direction, as a spatial index to evaluate the urban form, is paid less attention in urban morphology domain. In this paper we proposed a new indicator “parcel direction (PD)” and its definition to evaluate the urban form quantitatively. The PD indicates the direction of the direction of the longest edge of a polygon parcel in our definition. The approach for calculating the PD based on Geographical Information System (GIS) and how to measure urban form spatiotemporally using the PD were elaborated in this paper. A case study was conducted for planned parcels, expected to be realized in 2020, in the Detailed Planning of the Beijing Central Metropolitan Area. With respect to spatial dimension, we measured urban form in three scales, namely the parcel, zone and region scales, respectively. With respect to temporal dimension, we calculated the PD of the historical urban form of Beijing in 1949, followed with the comparison of the historical and planned forms using our proposed PD. To what extent the planned urban form inherits the historical urban form can be identified via comparing the PD index of two forms in various stages. The PD as a new indicator is proved to be capable to measure urban form spatiotemporally in our case study, and has its implication for urban form evaluation.

Paper in Planner
In Chinese: http://d.g.wanfangdata.com.cn/Periodical_ghs201004004.aspx
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An agent based model for low carbon urban form

More energy is being consumed as urbanization spreads. Extensive research has found that a dominant share of urban energy consumption belongs to transportation energy, which has a strong relationship with urban form in the intracity level. However, little attention has been paid to the relationship between urban form, transportation energy consumption, and its environmental impact in the inner-city level. This chapter aims to investigate the impact of urban form, namely, the land-use pattern, distribution of development density, and the number and distribution of job centers on the residential commuting energy consumption (RCEC). We developed a multi-agent model for the urban form, transportation energy consumption, and environmental impact integrated simulation (FEE-MAS). Numerous distinguishable urban forms were generated using the Monte Carlo approach in the hypothetical city. On the one hand, the RCEC for each urban form was calculated using the proposed FEE-MAS; on the other hand, we selected 14 indicators (e.g., Shape Index, Shannon’s Diversity Index, and Euclidean Nearest Neighbor Distance) to evaluate each generated urban form using the tool FRAGSTATS, which is loosely coupled with the FEE-MAS model. Afterward, the quantitative relationship between the urban form and RCEC was identified using the calculated 14 indicators and RCEC of all generated urban forms. Several conclusions were drawn from simulations conducted in the hypothetical city: (1) the RCEC may vary three times for the same space with various urban forms; (2) among the 14 indicators for evaluating urban form, the patch number of job parcels is the most significant variable for the RCEC; (3) the RCECs of all urban forms generated obey a normal distribution; and (4) the shape of an urban form also exerts an influence on the RCEC. In addition, we evaluated several typical urban forms—e.g., compact/sprawl, single center/multicenters, traffic-oriented development, and greenbelt—in terms of the RCEC indicator using our proposed model to quantify those conventional planning theories. We found that not all simulation results obey widely recognized existing theories. The FEE-MAS model can also be used for evaluating plan alternatives in terms of transportation energy consumption and environmental impact in planning practice.

Chapter in Springer book
Long et al 2013 Springer LCF.pdf
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Human mobility and urban form

Identifying the impact of the socioeconomic attributes of urban space on human mobility: Evidence from the analysis of TAZ and individual scales in Beijing

In this chapter, we investigate the spatially heterogeneous impact of the socioeconomic attributes of the urban space on human mobility (HM in Figure 1), such as trip length, time, and count. The data were obtained from the Household Travel Survey of Beijing in 2005. The socioeconomic attributes of urban space in 1,118 TAZs in Beijing are analysed from three approaches, namely ordinary least square (OLS), spatial regression, and geographically weighted regression (GWR). Because of the increasing attention being paid to identify the factors influencing human mobility, the results of each approach were compared with each other. In addition, we also conducted OLS analysis for person-level data (208,290 persons in total) using the same survey data and compared the analysis results with that of the TAZ-level data. This systematic analysis on the impact of socioeconomic attributes on human mobility would contribute to planning a greener urban form for Beijing. 

This paper appears in the Springer book "Geospatial Analysis for Supporting Urban Planning in Beijing" as a chapter.

Long and Shen 2015 Springer_BeijingMonog
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Identifying required policies for a desired urban form

We propose an approach to identify the spatial policy parameters (termed the implementation intensity reflecting planning controls on corresponding spatial constraint) associated with a predefined alternative plan, namely, a predefined-binary urban form. During plan implementation, the alternative plan cannot be fully realized in some cases due to practical urban growth driven by both institutional forces and market incentives, which are comprehensive and complex. Few researchers have investigated spatial policies appropriate for an alternative plan. We aim to propose a novel approach incorporating constrained cellular automata and regionalized sensitivity analysis, a method for global sensitivity analysis to calculate the realization possibility and identify the spatial policy parameters for an alternative plan. This approach is first tested in a virtual space with four predefined urban forms and various point, line, and polygon spatial constraints, with both positive and negative impacts on urban growth. Finally, the approach is also tested in the Beijing Metropolitan Area to identify the required spatial policy parameters for four alternative plans with seven spatial constraints. 

Paper in EPB
Long et al 2012 EPB_PPI.pdf
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Paper in ACTA
In Chinese
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Rediscovering Chinese cities through the lens of land use pattern

Urbanization is a dynamic phenomenon involving significant changes in land use. Due to the nature of top-down and bottom-up combined urbanization, Chinese cities exhibit a more complex and multidimensional urban land use pattern. This paper presents a novel method to model the spatial dynamics of urban land use pattern in China by employing multi-measurements, which include Spatial Entropy and Dissimilarity Index, and a combination of cellular-automata (CA) modeling. The study extracts 61 sets of urban land use images of year 2011 and classifies them into three integrated categories (Residential, Commercial and Public) to analyze urban land use pattern in China. Spatial metrics were used to compare the structural and functional differences of cities through land use pattern, quantify the spatial properties of urbanization, and show the impacts of urbanization on land use. The new integrated method can better reveal the complex spatial characterization of urbanization in China, where government still plays an important role of facilitating the urbanization process. The results show that cities exhibit distinctive spatial differences of fragmentation, and they are expanding rapidly and becoming less compact but more dispersed. The factors that drive a disperse-aggregation process of urbanization are debated in the context of China’s urban evolution in recent years. The new method provides an effective approach to improve our understanding on land use pattern, as well as contributing towards better planning and governance.

Lang et al 2018 LUP_LandUsePattern.pdf
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