Understanding the dynamics of urban areas of interest through volunteered geographic information (original) (raw)
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Social networks, available open data and massive online APIs provide huge amounts of data about our surrounding location, especially for cities and urban areas. Unfortunately, most previous applications and research usually focused on one kind of data over the other, thus presenting a biased and partial view of each location in question, hence partially negating the benefits of such approaches. To remedy this, we developed the CitySense framework that simultaneously combines data from administrative sources (e.g., public agencies), massive Point of Interest APIs (Google Places, Foursquare) and social microblogs (Twitter) to provide a unified view of all available information about an urban area, in an intuitive and easy to use web-application platform. This work describes the engineering and design challenges of such an effort and how these different and divergent sources of information may be combined to provide an accurate and diverse visualization for our use-case, the urban area of Chicago, USA.
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Scientific Reports, 2014
Pervasive infrastructures, such as cell phone networks, enable to capture large amounts of human behavioral data but also provide information about the structure of cities and their dynamical properties. In this article, we focus on these last aspects by studying phone data recorded during 55 days in 31 Spanish metropolitan areas. We first define an urban dilatation index which measures how the average distance between individuals evolves during the day, allowing us to highlight different types of city structure. We then focus on hotspots, the most crowded places in the city. We propose a parameter free method to detect them and to test the robustness of our results. The number of these hotspots scales sublinearly with the population size, a result in agreement with previous theoretical arguments and measures on employment datasets. We study the lifetime of these hotspots and show in particular that the hierarchy of permanent ones, which constitute the 'heart' of the city, is very stable whatever the size of the city. The spatial structure of these hotspots is also of interest and allows us to distinguish different categories of cities, from monocentric and "segregated" where the spatial distribution is very dependent on land use, to polycentric where the spatial mixing between land uses is much more important. These results point towards the possibility of a new, quantitative classification of cities using high resolution spatio-temporal data.
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Cities, 2021
Cities are continuing to develop and are grappling with uncertainties and difficulties as they do so. It has therefore become essential to understand how urban spatial structure changes, particularly with the increasingly available sources of 'big data'. However, most studies mainly focus on delineating the spatial structure and its variations. Only a few have investigated the incentives behind the movement dynamics. To identify the urban structure of Greater London and uncover how it co-evolves with socioeconomic and spatial policy factors, this study applies network community detection, using smart card data derived from the years 2013, 2015 and 2017, respectively. Our findings show that, firstly, between 2013 and 2017, London's urban structure moved towards a more polycentric and compact pattern. Secondly, it is found that Greater London can be clustered into five communities based on the characteristics of passengers' travel patterns. Thirdly, the dynamics of structural change in different urban clusters differ both in terms of changing intensity and potential motivation. In addition to spatial impact and spatial strategic policies, our results show that employment density and residential densities are also the main indicators that affected the interaction between Londoners in different areas on various levels. Keywords Urban structure; Big data analytics; Urban planning; Community detection; Network analysis; London Highlights • A technique borrowed from the complex network sciences, namely community detection, is applied using smart card data. • London's urban structure moved towards a more polycentric and compact pattern. • The Greater London can be clustered into five communities based on the characteristics of passengers' travel patterns. • The dynamics of structural change in different urban clusters differ both in terms of changing intensity and potential motivation. • Employment density and residential densities are the main indicators affecting the movement of and interaction between Londoners in different areas. 1. Introduction Parr (2014) asserted that "[U]rban structure is concerned with the organisation and functioning of markets for goods and factors of production". This underscores the fact that the regional economy does not operate at a single point and is distributed unevenly over space. Urban structure, therefore, can be seen as a reflection of the locational characteristics of economic activities. This point echoes studies by economists that have explored the initial motivation for studying urban structure. They aim to explore whether there is an optimal way to organise metropolitan areas to ensure faster economic development. For instance, some studies (Gordon & Richardson, 1997, Richardson, 1969) have found that there is a significant relationship between metropolitan spatial structure and economic growth, depending on metropolitan size and its structural organisation. Inevitably, the trade-off or cost of economic agglomerations can also cause significant problems concerning urban development. Scholars, such as Lee and Gordon (2007), have highlighted that people living in large cities are more likely to suffer from negative externalities. Therefore, the research agenda of the urban structure has shifted towards a more comprehensive interpretation of the 'optimal' urban structure. This topic has also attracted great interest from urban planners, geographers and policymakers, particularly in the last two decades, because spatial structure exerts a strong influence on people's daily life, economic performance (
Having the ability to detect emerging patterns in cities is crucial for efficient management of urban resources. Patterns that are useful in identifying and addressing future resource consumption needs include spatial changes in urban form and structure as well as temporal changes in human concentrations and activity patterns during the course of a day. Other patterns of interest are characteristics of local populations in dynamically changing neighborhoods and social-functional spaces. In this paper, we use the Integrated Multimedia City Data (iMCD) platform which brings together multiple strands of structured and unstructured data, to examine such trends in the Greater Glasgow region. We present an approach to, first, understand spatial and time-dependent changes that capture the flow of resources needed to meet demands of residents and businesses at different times and locations, and second, generate hypotheses regarding urban engagement, activity patterns and travel behaviour. We use social media data, GPS trajectories, and background data from the UK Population Census for this purpose. The approach identifies the " roughness " in activity patterns across the urban space that are indicative of different concentrations of social and functional activities. When the time dimension is added to the mix, we are able to uncover time-varying transitions from one type of use pattern into another in different parts of the region. Such transitions, particularly in mixed-use areas, allow early detection of points of excess urban metabolism, with implications for traffic congestion, waste production, energy and other resource consumption patterns. Finally, the ability to detect what citizens talk about socially may provide a way to understand whether or not the language patterns detected in different parts of the city reflect underlying uses and concerns. A preliminary step to evaluate this idea is explored by extracting context-awareness and semantic enrichment to socially-generated data.
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2010 IEEE Second International Conference on Social Computing, 2010
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 DAD-MST, 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.
ISPRS International Journal of Geo-Information, 2020
Urban environments play a crucial role in the design, planning, and management of cities. Recently, as the urban population expands, the ways in which humans interact with their surroundings has evolved, presenting a dynamic distribution in space and time locally and frequently. Therefore, how to better understand the local urban environment and differentiate varying preferences for urban areas has been a big challenge for policymakers. This study leverages geotagged Flickr photographs to quantify characteristics of varying urban areas and exploit the dynamics of areas where more people assemble. An advanced image recognition model is used to extract features from large numbers of images in Inner London within the period 2013-2015. After the integration of characteristics, a series of visualisation techniques are utilised to explore the characteristic differences and their dynamics. We find that urban areas with higher population densities cover more iconic landmarks and leisure zones, while others are more related to daily life scenes. The dynamic results demonstrate that season determines human preferences for travel modes and activity modes. Our study expands the previous literature on the integration of image recognition method and urban perception analytics and provides new insights for stakeholders, who can use these findings as vital evidence for decision making.