Evaluation and Planning of Urban Green Space Distribution Based on Mobile Phone Data and Two-Step Floating Catchment Area Method

et al. 2019

IJGI

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The commute of residents in a big city often brings tidal traffic pressure or congestions. Understanding the causes behind this phenomenon is of great significance for urban space optimization. Various spatial big data make the fine description of urban residents' travel behaviors possible, and bring new approaches to related studies. The present study focuses on two aspects: one is to obtain relatively accurate features of commuting behaviors by using mobile phone data, and the other is to simulate commuting behaviors of residents through the agent-based model and inducing backward the causes of congestion. Taking the Baishazhou area of Wuhan, a local area of a mega city in China, as a case study, we simulated the travel behaviors of commuters: the spatial context of the model is set up using the existing urban road network and by dividing the area into space units. Then, using the mobile phone call detail records of a month, statistics of residents' travel during the four time slots in working day mornings are acquired and then used to generate the Origin-Destination matrix of travels at different time slots, and the data are imported into the model for simulation. Under the preset rules of congestion, the agent-based model can effectively simulate the traffic conditions of each traffic intersection, and can induce backward the causes of traffic congestion using the simulation results and the Origin-Destination matrix. Finally, the model is used for the evaluation of road network optimization, which shows evident effects of the optimizing measures adopted in relieving congestion, and thus also proves the value of this method in urban studies.residents' mobility over time and space can be used for urban geographic mapping [13], epidemiological analysis [14,15], real-time urban monitoring [16], etc. and can also be used for recognition of urban spatial features [17][18][19] or measurement of urban vibrancy [20]. Another important scenario of application is the study of residents' commuting and urban transport, including the identification of commuting areas and commuting distances [21,22], and the acquisition of commuter Origin-Destination (OD) matrices [23][24][25]. Among various sources of location data, mobile phone data have been widely employed in studies such as residents' commuting thanks to its extensive coverage, passive data collection, and the fact that its data acquisition requires no extra equipment. In comparison, alternative data sources, such as smart card data or taxi GPS data, have equivalent difficulties in data coverage but much smaller population coverage [26]. Results of studies based on new data have been shown to have higher accuracy compared to those that are based on statistical data or measured data, proving the effectiveness of big data application in urban studies. In general, most of these studies are at an early stage of describing urban phenomena through data, few studies attempt to go further such as using big data to identify the connection between residents' travel and traffic c...

Section: Discussionmentioning

confidence: 99%

Section: Mobile Phone Data Processingmentioning

confidence: 99%

An Agent-based Model Simulation of Human Mobility Based on Mobile Phone Data: How Commuting Relates to Congestion

et al. 2019

IJGI

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“…As a comparison, the Mobile Phone (MP) base stations (n = 33,758) were utilized as well. This data was provided by a partner telecommunication operator whose market share was about 60%, and verified to be proportionally representative of the whole population distribution in Wuhan [37].…”

Section: Datamentioning

confidence: 83%

Delimitating the Natural City with Points of Interests Based on Service Area and Maximum Entropy Method

Xia

et al. 2019

Entropy

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The natural city, which is essential to understand urban physical scale and identify urban sprawling in urban studies, represents the urban functional boundaries of the city defined by human activities rather than the administrative boundaries. Most studies tend to utilize physical environment data such as street networks and remote sensing data to delimitate the natural city, however, such data may not match the real distribution of human activity density in the new cities or even ghost cities in China. This paper suggests aggregating the natural city boundary from the service area polygons of points of interest based on Reilly’s Law of Retail Gravitation and the maximum entropy method, since most points of interests provide services for surrounding communities, reflecting the vitality in a bottom-up way. The results indicate that the natural city defined by points of interests shows a high resolution and accuracy, providing a method to define the natural city with POIs.

“…Data of phone call records used in the present study was provided by a partner telecommunication operator whose market share was about 60%, verified for representing whole population distribution proportionally in Wuhan [49]. Mobile phone data of 7,300,000 users in November 2015 in Wuhan City was used in the study.…”

Section: Gridded Population Generated By Mobile Phone Datamentioning

confidence: 99%

Fast Identification of Urban Sprawl Based on K-Means Clustering with Population Density and Local Spatial Entropy

Peng

et al. 2018

Sustainability

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Abstract:As urban sprawl is proven to jeopardize the sustainability system of cities, the identification of urban sprawl is essential for urban studies. Compared with previous related studies which tend to utilize more and more complicated variables to recognize urban sprawl while still retaining an element of uncertainty, this paper instead proposes a simplified model to identify urban sprawl patterns. This is a working theory which is based on a diagram interpretation of the classic urban spatial structure patterns of the Chicago School. The method used in our study is K-means clustering with gridded population density and local spatial entropy. The results and comparison with open population data and mobile phone data verify the assumption and furthermore indicate that the accuracy of source population data will limit the precision of output identification. This article concludes that urban sprawl is mainly dominated by population and surrounding unevenness. Moreover, the Floating Catchment Area (FCA) local spatial entropy method presented in this research brings about an integration of Shannon entropy, Tobler's first law of geography and the Moore neighborhood, improving the spatial homogeneity and locality of Batty's Spatial Entropy model which can only be used in a general scope.