Inferring land use from mobile phone activity

By Jameson L. Toole, Michael Ulm, Dietmar Bauer, Marta C. González. UrbComp '12, 2012

329 citations recorded by the source corpus.

Understanding the spatiotemporal distribution of people within a city is crucial to many planning applications. Obtaining data to create required knowledge, currently involves costly survey methods. At the same time ubiquitous mobile sensors from personal GPS devices to mobile phones are collecting massive amounts of data on urban systems. The locations, communications, and activities of millions of people are recorded and stored by new information technologies. This work utilizes novel dynamic data, generated by mobile phone users, to measure spatiotemporal changes in population. In the process, we identify the relationship between land use and dynamic population over the course of a typical week. A machine learning classification algorithm is used to identify clusters of locations with similar zoned uses and mobile phone activity patterns. It is shown that the mobile phone data is capable of delivering useful information on actual land use that supplements zoning regulations.

Subfields: Global and Planetary Change, Transportation

View Paper: https://www.semanticscholar.org/paper/27ece58e16360d95559bb7004e9e6254a2c3c9f0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Global and Planetary Change

1. Michael Batty
2. Yu Zheng
3. Hai Yang
4. Jacques Teller

In Transportation

1. Yu Zheng
2. Ouri Wolfson
3. Licia Capra
4. Hai Yang
5. Xianfeng Huang
6. Depeng Jin
7. Santi Phithakkitnukoon
8. Li Gong

Citations in the corpus

• Classifying urban land use by integrating remote sensing and social media data (2017, unknown): “2009, Toole et al .”
• Revealing daily travel patterns and city structure with taxi trip data (2013, background): “Keywords: GPS-enabled taxi data, intra-urban human mobility, urban structure, land use condition, Shanghai”
• Mapping Urban Land Use by Using Landsat Images and Open Social Data (2016, background): “Combined with open social data, it is promising to map detailed urban land use over a large metropolitan area, which is advantageous compared with methods that focus only on discerning a specific land use type in a relatively small area [19,27].”
• Detecting the dynamics of urban structure through spatial network analysis (2014, background): “In other cases, machine learning methods are being introduced to infer land use from mobile phone activity records and zoning regulations (Toole et al. 2012) while data mining methods are being explored for discovering patterns (Jiang et al .”
• Inferring Passenger Travel Demand to Improve Urban Mobility in Developing Countries Using Cell Phone Data: A Case Study of Senegal (2016, background): “The main reason is that most of the developing cities do not have enough budget to collect detailed information necessary for 1524-9050 © 2016 IEEE.”
• Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment (2015, methodology): “The footprint of mobile devices and cellular network has been used to model human mobility and trajectories [9, 7, 24, 5].”
• Urban Computing: Concepts, Methodologies, and Applications (2014, background): “For example, Toole et al. [2012] utilized call detail records, which provide information on the location of mobile phones any time a call is made or a text message is sent, to measure spatiotemporal changes in phone activities.”