7,797 research outputs found
Inferring land use from mobile phone activity
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.Comment: To be presented at ACM UrbComp201
Is spatial information in ICT data reliable?
An increasing number of human activities are studied using data produced by
individuals' ICT devices. In particular, when ICT data contain spatial
information, they represent an invaluable source for analyzing urban dynamics.
However, there have been relatively few contributions investigating the
robustness of this type of results against fluctuations of data
characteristics. Here, we present a stability analysis of higher-level
information extracted from mobile phone data passively produced during an
entire year by 9 million individuals in Senegal. We focus on two
information-retrieval tasks: (a) the identification of land use in the region
of Dakar from the temporal rhythms of the communication activity; (b) the
identification of home and work locations of anonymized individuals, which
enable to construct Origin-Destination (OD) matrices of commuting flows. Our
analysis reveal that the uncertainty of results highly depends on the sample
size, the scale and the period of the year at which the data were gathered.
Nevertheless, the spatial distributions of land use computed for different
samples are remarkably robust: on average, we observe more than 75% of shared
surface area between the different spatial partitions when considering activity
of at least 100,000 users whatever the scale. The OD matrix is less stable and
depends on the scale with a share of at least 75% of commuters in common when
considering all types of flows constructed from the home-work locations of
100,000 users. For both tasks, better results can be obtained at larger levels
of aggregation or by considering more users. These results confirm that ICT
data are very useful sources for the spatial analysis of urban systems, but
that their reliability should in general be tested more thoroughly.Comment: 11 pages, 9 figures + Appendix, Extended version of the conference
paper published in the proceedings of the 2016 Spatial Accuracy Conference, p
9-17, Montpellier, Franc
Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges
Today's mobile phones are far from mere communication devices they were ten
years ago. Equipped with sophisticated sensors and advanced computing hardware,
phones can be used to infer users' location, activity, social setting and more.
As devices become increasingly intelligent, their capabilities evolve beyond
inferring context to predicting it, and then reasoning and acting upon the
predicted context. This article provides an overview of the current state of
the art in mobile sensing and context prediction paving the way for
full-fledged anticipatory mobile computing. We present a survey of phenomena
that mobile phones can infer and predict, and offer a description of machine
learning techniques used for such predictions. We then discuss proactive
decision making and decision delivery via the user-device feedback loop.
Finally, we discuss the challenges and opportunities of anticipatory mobile
computing.Comment: 29 pages, 5 figure
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
The Effect of Pok\'emon Go on The Pulse of the City: A Natural Experiment
Pok\'emon Go, a location-based game that uses augmented reality techniques,
received unprecedented media coverage due to claims that it allowed for greater
access to public spaces, increasing the number of people out on the streets,
and generally improving health, social, and security indices. However, the true
impact of Pok\'emon Go on people's mobility patterns in a city is still largely
unknown. In this paper, we perform a natural experiment using data from mobile
phone networks to evaluate the effect of Pok\'emon Go on the pulse of a big
city: Santiago, capital of Chile. We found significant effects of the game on
the floating population of Santiago compared to movement prior to the game's
release in August 2016: in the following week, up to 13.8\% more people spent
time outside at certain times of the day, even if they do not seem to go out of
their usual way. These effects were found by performing regressions using count
models over the states of the cellphone network during each day under study.
The models used controlled for land use, daily patterns, and points of interest
in the city.
Our results indicate that, on business days, there are more people on the
street at commuting times, meaning that people did not change their daily
routines but slightly adapted them to play the game. Conversely, on Saturday
and Sunday night, people indeed went out to play, but favored places close to
where they live.
Even if the statistical effects of the game do not reflect the massive change
in mobility behavior portrayed by the media, at least in terms of expanse, they
do show how "the street" may become a new place of leisure. This change should
have an impact on long-term infrastructure investment by city officials, and on
the drafting of public policies aimed at stimulating pedestrian traffic.Comment: 23 pages, 7 figures. Published at EPJ Data Scienc
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