13,243 research outputs found
A New Insight into Land Use Classification Based on Aggregated Mobile Phone Data
Land use classification is essential for urban planning. Urban land use types
can be differentiated either by their physical characteristics (such as
reflectivity and texture) or social functions. Remote sensing techniques have
been recognized as a vital method for urban land use classification because of
their ability to capture the physical characteristics of land use. Although
significant progress has been achieved in remote sensing methods designed for
urban land use classification, most techniques focus on physical
characteristics, whereas knowledge of social functions is not adequately used.
Owing to the wide usage of mobile phones, the activities of residents, which
can be retrieved from the mobile phone data, can be determined in order to
indicate the social function of land use. This could bring about the
opportunity to derive land use information from mobile phone data. To verify
the application of this new data source to urban land use classification, we
first construct a time series of aggregated mobile phone data to characterize
land use types. This time series is composed of two aspects: the hourly
relative pattern, and the total call volume. A semi-supervised fuzzy c-means
clustering approach is then applied to infer the land use types. The method is
validated using mobile phone data collected in Singapore. Land use is
determined with a detection rate of 58.03%. An analysis of the land use
classification results shows that the accuracy decreases as the heterogeneity
of land use increases, and increases as the density of cell phone towers
increases.Comment: 35 pages, 7 figure
Comparing and modeling land use organization in cities
The advent of geolocated ICT technologies opens the possibility of exploring
how people use space in cities, bringing an important new tool for urban
scientists and planners, especially for regions where data is scarce or not
available. Here we apply a functional network approach to determine land use
patterns from mobile phone records. The versatility of the method allows us to
run a systematic comparison between Spanish cities of various sizes. The method
detects four major land use types that correspond to different temporal
patterns. The proportion of these types, their spatial organization and scaling
show a strong similarity between all cities that breaks down at a very local
scale, where land use mixing is specific to each urban area. Finally, we
introduce a model inspired by Schelling's segregation, able to explain and
reproduce these results with simple interaction rules between different land
uses.Comment: 9 pages, 6 figures + Supplementary informatio
Supervised Land Use Inference from Mobility Patterns
This paper addresses the relationship between land use and mobility patterns. Since each particular zone directly feeds the global mobility once acting as origin of trips and others as destination, both roles are simultaneously used for predicting land uses. Specifically this investigation uses mobility data derived from mobile phones, a technology that emerges as a useful, quick data source on people's daily mobility, collected during two weeks over the urban area of Málaga (Spain). This allows exploring the relevance of integrating weekday-weekend trip information to better determine the category of land use. First, this work classifies patterns on trips originated and terminated in each zone into groups by means of a clustering approach. Based on identifiable relationships between activity and times when travel peaks appear, a preliminary categorization of uses is provided. Then, both grouping results are used as input variables in a K-nearest neighbors (KNN) classification model to determine the exact land use. The KNN method assumes that the category of an object must be similar to the category of the closest neighbors. After training the models, the findings reveal that this approach provides a precise land use categorization, yielding the best accuracy results for the major categories of land uses in the studied area. Moreover, as a result, the weekend data certainly contributes to finding more precise land uses as those obtained by just weekday data. In particular, the percentage of correctly predicted categories using both weekday and weekend is around 80%, while just weekday data reach 67%. The comparison with actual land uses also demonstrates that this approach is able to provide useful information, identifying zones with a specific clear dominant use (residential, industrial, and commercial), as well as multiactivity zones (mixed). This fact is especially useful in the context of urban environments where multiple activities coexist.Unión Europea Programa Operativo FEDER de AndalucÃa 2011–2015Ministerio de EconomÃa y Competitividad PTQ-13-0642
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