Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2346496.2346498 | knowledge discovery and data mining |
Keywords | Field | DocType |
mobile phone activity pattern,novel dynamic data,obtaining data,time ubiquitous mobile sensor,inferring land use,mobile phone,mobile phone user,dynamic population,land use,mobile phone data,actual land use,survey methods,dynamic data,social science,machine learning,computational social science,information technology | Mobile computing,Population,Data mining,Mobile search,Computer science,Mobile database,Dynamic data,Urban computing,Mobile phone,Land use | Journal |
Volume | Citations | PageRank |
abs/1207.1115 | 92 | 4.45 |
References | Authors | |
7 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jameson L. Toole | 1 | 179 | 10.57 |
| Michael Ulm | 2 | 95 | 5.15 |
| Marta C. González | 3 | 299 | 18.26 |
| Dietmar Bauer | 4 | 219 | 21.28 |