A matrix representation of places of residence and places of commuting destination in a metropolis, is coupled with evidence regarding spatio-temporal change in average household size. This approach allows the average number of persons per household who commute to be hierarchically ordered in a square matrix which shows attributes associated with a well-known class of matrices. Based on these attributes it is shown that any given spatial distribution of households implies a bounded range of vectors representing the spatial distribution of commuters. Two related distributions correspond to night-time and daytime populations in the metropolitan subareas. Whereas much of contemporary urban modeling is rooted in economic considerations, this alternative approach replaces explicit economic reasoning with some speculative considerations. The proposed methodology is applied to thirty-four subareas throughout the city of Saskatoon, Saskatchewan.
[11]
Roddis SM, Richardson AJ.
Construction of daytime activity profiles from household travel survey data
. In: Transportation Research Record 1625, TRB. National Research Council, 1998: 102-108.
The perception of population as the number of people living in a region is entrenched firmly in the minds of many urban planners and transport professionals. In the past, the use of such a residential or home-based description of population would have suited most local area planning and transportation modeling needs. In light of this, the use of a daytime distribution of population to locate services and facilities forms a radical departure form conventional practice. However, there is a clear need to consider the location of people across the day in planning for the provision of facilities that will be used during the day. Data from the Victorian Activity and Travel Survey were used to develop methods by which daytime population behavior can be examined. Specifically, two measures were developed. A population profile provides an estimate of population within any region at any time of the day. A visitation curve supplements the population profile by monitoring the number of people using a region across the day. Further disaggregation of the population to reveal behavioral and demographic characteristics constitutes an important component of the methodology.
[12]
SleeterR, WoodN.
Estimating daytime and nighttime population density for coastal communities in Oregon
. In: Proceedings of 44th Urban and Regional Information Systems Association Annual Conference. Columbia, British, 44th Urban and Regional Information Systems Association Annual Conference, 2006: 906-920.
ABSTRACT Hazard preparedness has become a critical issue for local populations who are potentially vulnerable to natural disasters. Essential to preparedness planning is determining where people are likely to be located, which varies from day to night. The fundamental approaches to geographic scale and cartographic representation are an integral aspect of how population distribution is represented over space. Using a dasymetric mapping technique, residential populations are estimated by interpolating the census block values to 10 m pixels based on parcel-level land use and density. To determine daytime population estimates, a quantitative employee database gives x,y point locations of each business and exact numbers of how many people are employed within a coastal community. From census records, we can estimate the number of people who are leaving their residences during the daytime to go to work outside of a tsunami hazard zone.
[13]
KavanaughP.
A method for estimating daytime population by small area geography. Proceedings of 18th Urban and Regional Information Systems Association Conference
. Edmonton, Alberta, Canada, 18th Urban and Regional Information Systems Association Conference, 1990: 150-164.
The emergence of big data brings new opportunities for us to understand our socioeconomic environments. We use the term social sensing for such individual-level big geospatial data and the associated analysis methods. The word sensing suggests two natures of the data. First, they can be viewed as the analogue and complement of remote sensing, as big data can capture well socioeconomic features while conventional remote sensing data do not have such privilege. Second, in social sensing data, each individual plays the role of a sensor. This article conceptually bridges social sensing with remote sensing and points out the major issues when applying social sensing data and associated analytics. We also suggest that social sensing data contain rich information about spatial interactions and place semantics, which go beyond the scope of traditional remote sensing data. In the coming big data era, GIScientists should investigate theories in using social sensing data, such as data representativeness and quality, and develop new tools to deal with social sensing data.
This paper describes a new real-time urban monitoring system. The system uses the Localizing and Handling Network Event Systems (LocHNESs) platform developed by Telecom Italia for the real-time evaluation of urban dynamics based on the anonymous monitoring of mobile cellular networks. In addition, data are supplemented based on the instantaneous positioning of buses and taxis to provide information about urban mobility in real time, ranging from traffic conditions to the movements of pedestrians throughout the city. This system was exhibited at the Tenth International Architecture Exhibition of the Venice Biennale. It marks the unprecedented monitoring of a large urban area, which covered most of the city of Rome, in real time using a variety of sensing systems and will hopefully open the way to a new paradigm of understanding and optimizing urban dynamics.
[26]
ReadesJonathan, CalabreseFrancesco, RattiCarlo.
Eigenplaces: Analysing cities using the space-time structure of the mobile phone network.
Environment and Planning B: Planning and Design, 2009, 36(5): 824-836.
Several attempts have already been made to use telecommunications networks for urban research, but the datasets employed have typically been neither dynamic nor fine grained. Against this research backdrop the mobile phone network offers a compelling compromise between these extremes: it is both highly mobile and yet still localisable in space. Moreover, the mobile phone’s enormous and enthusiastic adoption across most socioeconomic strata makes it a uniquely useful tool for conducting large-scale, representative behavioural research. In this paper we attempt to connect telecoms usage data from Telecom Italia Mobile (TIM) to a geography of human activity derived from data on commercial premises advertised through Pagine Gialle, the Italian ‘Yellow Pages’. We then employ eigendecomposition—a process similar to factoring but suitable for this complex dataset—to identify and extract recurring patterns of mobile phone usage. The resulting eigenplaces support the computational and comparative analysis of space through the lens of telecommuniations usage and enhance our understanding of the city as a ‘space of flows’.
Information and communication technologies (ICTs), such as mobile phones and the Internet, are increasingly pervasive in modern society. These technologies provide new resources for spatio-temporal data mining and geographic knowledge discovery. Since the development of ICTs also impacts physical movement of individuals in societies, much of the existing research has focused on examining the correlation between ICT and human mobility. In this paper, we aim to provide a deeper understanding of how usage of mobile phones correlates with individual travel behavior by exploring the correlation between mobile phone call frequencies and three indicators of travel behavior: (1) radius, (2) eccentricity, and (3) entropy. The methodology is applied to a large dataset from Harbin city in China. The statistical analysis indicates a significant correlation between mobile phone usage and all of the three indicators. In addition, we examine and demonstrate how explanatory factors, such as age, gender, social temporal orders and characteristics of the built environment, impact the relationship between mobile phone usage and individual activity behavior. .
[30]
AhasRein, AasaAnto, SilmSiiri, et al.
Daily rhythms of suburban commuters' movements in the Tallinn metropolitan area: Case study with mobile positioning data.
Transportation Research Part C: Emerging Technologies, 2010, 18(1): 45-54.
The investigation of the space–time movements and daily distances of respondents showed that the majority of respondents had a similar temporal rhythm related to work, school, services and leisure in the city. Because of the different timing of those activities, the mobile positioning data made it possible to map functional differences in the city. The advantages and disadvantages of mobile positioning data in mapping urban life are discussed in the final section of the study.
Comparison of retail trade areas of retail centers with different hierarchical levels: A case study of East Nanjing Road, Wujiaochang, Anshan Road in Shanghai.
Large-scale urban sensing data such as mobile phone traces are emerging as an important data source for urban modeling. This study represents a first step towards building a methodology whereby mobile phone data can be more usefully applied to transportation research. In this paper, we present techniques to extract useful mobility information from the mobile phone traces of millions of users to investigate individual mobility patterns within a metropolitan area. The mobile-phone-based mobility measures are compared to mobility measures computed using odometer readings from the annual safety inspections of all private vehicles in the region to check the validity of mobile phone data in characterizing individual mobility and to identify the differences between individual mobility and vehicular mobility. The empirical results can help us understand the intra-urban variation of mobility and the non-vehicular component of overall mobility. More importantly, this study suggests that mobile phone trace data represent a reasonable proxy for individual mobility and show enormous potential as an alternative and more frequently updatable data source and a compliment to the conventional travel surveys in mobility study.
[34]
DevilleP, LinardC, MartinS, et al.
Dynamic population mapping using mobile phone data.
Proceedings of the National Academy of Sciences, 2014, 111(45): 15888-15893.
During the past few decades, technologies such as remote sensing, geographical information systems, and global positioning systems have transformed the way the distribution of population is studied and modeled in space and time. However, the mapping of populations remains constrained by the logistics of censuses and surveys. Consequently, spatially detailed changes across scales of days, weeks, or months, or even year to year, are difficult to assess and limit the application of population maps in situations in which timely information is required, such as disasters, conflicts, or epidemics. Mobile phones (MPs) now have an extremely high penetration rate across the globe, and analyzing the spatiotemporal distribution of MP calls geolocated to the tower level may overcome many limitations of census-based approaches, provided that the use of MP data is properly assessed and calibrated. Using datasets of more than 1 billion MP call records from Portugal and France, we show how spatially and temporarily explicit estimations of population densities can be produced at national scales, and how these estimates compare with outputs produced using alternative population mapping methods. We also demonstrate how maps of population changes can be produced over multiple timescales while preserving the anonymity of MP users. With similar data being collected every day by MP network providers across the world, the prospect of being able to map contemporary and changing population distributions over relatively short intervals exists, paving the way for new applications and a near real-time understanding of patterns and processes in geography.
[35]
Vieira MR, Inez V Fr I, Oliver N, et al.
Characterizing dense urban areas from mobile phone-call data: Discovery and social dynamics
. In: Proceedings of 2010 IEEE Second International Conference, 2010: 241-248.
The recent adoption of ubiquitous computing technologies (e.g. GPS, WLAN networks) has enabled capturing large amounts of spatio-temporal data about human motion. The digital footprints computed from these datasets provide complementary information for the study of social and human dynamics, with applications ranging from urban planning to transportation and epidemiology. A common problem for all these applications is the detection of dense areas, i.e. areas where individuals concentrate within a specific geographical region and time period. Nevertheless, the techniques used so far face an important limitation: they tend to identify as dense areas regions that do not respect the natural tessellation of the underlying space. In this paper, we propose a novel technique, called DADMST, to detect dense areas based on the Maximum Spanning Tree (MST) algorithm applied over the communication antennas of a cell phone infrastructure. We evaluate and validate our approach with a real dataset containing the Call Detail Records (CDR) of over one million individuals, and apply the methodology to study social dynamics in an urban environment.
[36]
AhasRein, Markülar.
Location based services-new challenges for planning and public administration?
The massive spread of mobile phones and their social acceptance is an important information-technological feature of the beginning of the 21st century. In addition to making calls, mobile phones are associated with various additional services and games, which are becoming part of everyday life. As the location of mobile phones can be precisely tracked in space, they can be used for investigating the space-time behaviour of society. In this paper we will introduce the Social Positioning Method and its possible applications in the organisation and planning of public life. The Social Positioning Method (SPM) studies social flows in time and space by analysing the location coordinates of mobile phones and the social identification of the people carrying them. So far, relatively few SPM surveys have been carried out he reason for that is related to people's anxieties about the idea of being tracked, as well as technical aspects. In this article we assert that SPM will become very wide-spread in the future and will fundamentally change public life and administration. Due to the widespread use of telephones and the possibilities of social positioning, the questions of privacy and freedom of the individual are already being discussed. Despite this, there has still been only a limited discussion concerning live-map geography and real-time planning in relation to privacy issues.
[37]
WangP.
Understanding the spreading patterns of mobile phone viruses.
Proceedings of the National Academy of Sciences, 2009, 106(20): 8380-8385.
