Understanding job-housing relationship and commuting pattern in Chinese cities: Past, present and future

Ta, Na; Chai, Yanwei; Zhang, Yan; Sun, Daosheng

doi:10.1016/j.trd.2016.11.011

Cited by 95 publications

(60 citation statements)

References 46 publications

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“…Since the economic reform of the 1990s that restructured urban land use, the housing market has gradually transitioned from government-assigned units into a free-trade system [25]. Before the reform, housing was characterized by work-unit compounds (the Chinese danwei), where residents lived and worked within the same community and were provided with a wide range of amenities, such as retail stores, schools, and hospitals [26,27]. The commodification of residential housing has substantially transformed the urban landscape and, therefore, residents' commuting patterns.…”

Section: Housing Development and Data Issues In Chinamentioning

confidence: 99%

Exploring Determinants of Housing Prices in Beijing: An Enhanced Hedonic Regression with Open Access POI Data

Ying

Chen

et al. 2017

IJGI

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Abstract:The housing market in Chinese metropolises have become inflated significantly over the last decade. In addition to an economic upturn and housing policies that have potentially fueled the real estate bubble, factors that have contributed to the spatial heterogeneity of housing prices can be dictated by the amenity value in the proximity of communities, such as accessibility to business centers and transportation hubs. In the past, scholars have employed the hedonic pricing model to quantify the amenity value in relation to structural, locational, and environmental variables. These studies, however, are limited by two methodological obstacles that are relatively difficult to overcome. The first pertains to difficulty of data collection in regions where geospatial datasets are strictly controlled and limited. The second refers to the spatial autocorrelation effect inherent in the hedonic analysis. Using Beijing, China as a case study, we addressed these two issues by (1) collecting residential housing and urban amenity data in terms of Points of Interest (POIs) through web-crawling on open access platforms; and (2) eliminating the spatial autocorrelation effect using the Eigenvector Spatial Filtering (ESF) method. The results showed that the effects of nearby amenities on housing prices are mixed. In other words, while proximity to certain amenities, such as convenient parking, was positively correlated with housing prices, other amenity variables, such as supermarkets, showed negative correlations. This mixed finding is further discussed in relation to community planning strategies in Beijing. This paper provides an example of employing open access datasets to analyze the determinants of housing prices. Results derived from the model can offer insights into the reasons for housing segmentation in Chinese cities, eventually helping to formulate effective urban planning strategies and equitable housing policies.

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Section: Housing Development and Data Issues In Chinamentioning

confidence: 99%

Exploring Determinants of Housing Prices in Beijing: An Enhanced Hedonic Regression with Open Access POI Data

Ying

Chen

et al. 2017

IJGI

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“…Since the reform and opening up of China's housing system, a large number of urban residents have chosen to purchase newly built commercial houses [1][2][3], which has caused the collapse of the urban jobs-housing space structure in the planned economy period [4]. e freedom of residence facilitated by the housing system reform has made the separation of mixed jobs-housing patterns increasingly common in China [5].…”

Section: Introductionmentioning

confidence: 99%

Delineating Mixed Urban “Jobs-Housing” Patterns at a Fine Scale by Using High Spatial Resolution Remote-Sensing Imagery

et al. 2020

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e spatial distribution pattern of jobs and housing plays a vital role in urban planning and traffic construction. However, obtaining the jobs-housing distribution at a fine scale (e.g., the perspective of individual jobs-housing attribute) presents difficulties due to a lack of social media data and useful models. With user data acquired from a location-based service provider in China, this study employs a deep bag-of-features network (BagNet) to classify remote-sensing (RS) images into various jobshousing types. Considering Wuhan, one of the fastest developing cities in China, as a case study area, three jobs-housing types (i.e., only working, only living, and both working and living) at the land-parcel level are obtained. We demonstrate that the multiscale random sampling method can reduce the influence of image noise, increase the utilization of training data, and reduce network overfitting. By altering the network structure and the training strategy, BagNet achieved excellent fitting accuracy for identifying each jobs-housing type (overall accuracy > 0.84 and kappa > 0.8). For the first time, we demonstrate that urban socioeconomic characteristics can be obtained from high-resolution RS images using deep learning techniques. Additionally, we conclude that the total level of mixing within Wuhan is not high at present; however, Wuhan is continuously improving the mixture of jobs and housing. is study has reference value for extracting urban socioeconomic characteristics from RS images and could be used in urban planning as well as government management.

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“…It is a considerable challenge to provide efficient methods to measure how public transit services match public transit users [7]. Conventional methods such as inhabitant surveys [8] and household interviews are often used to identify regular transit users and their locations [9]. Due to its high efficiency, data mining of smart card data can be applied to identify commuting transit users [10], but few existing studies manage to match regular users with transportation resources.…”

Section: Introductionmentioning

confidence: 99%

Measuring Spatial Mismatch between Public Transit Services and Regular Riders: A Case Study of Beijing

Jin

Zhu

2019

IJGI

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Public transit services should favor space equity, and the concern of this study is how the allocation of public transportation resources corresponds to the needs of transit users. Identifying mismatches between urban transit resources and regular transit users benefits the transportation resource allocation policy. This study introduces a location maximum likelihood estimation method and a cell space collector mechanism to explore distribution differences of regular transit riders and transit stations based on data mining. In Beijing, 5.37 million regular transit users were identified, and their first-morning transit stations were found to be within 2 km from their last transit stations used the day before. As their locations were estimated, differences in ratios of the regular transit riders to residents were found among areas. Most regular transit users were located in the suburban areas of 5-20 km from the center of Beijing, and the spatial distribution of transit stations declined from the center to the peripheral urban areas. This mismatch between public transit services and regular transit riders sheds light on urban transportation policies.

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Understanding job-housing relationship and commuting pattern in Chinese cities: Past, present and future

Cited by 95 publications

References 46 publications

Exploring Determinants of Housing Prices in Beijing: An Enhanced Hedonic Regression with Open Access POI Data

Exploring Determinants of Housing Prices in Beijing: An Enhanced Hedonic Regression with Open Access POI Data

Delineating Mixed Urban “Jobs-Housing” Patterns at a Fine Scale by Using High Spatial Resolution Remote-Sensing Imagery

Measuring Spatial Mismatch between Public Transit Services and Regular Riders: A Case Study of Beijing

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