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volume 58, issue 15, november 2021
1. title: big data in the city
authors: jon bannister, anthony o�sullivan.
abstract: this editorial introduces a special issue on big data in the city. collectively, six research articles and two commentaries explore the roles that big data can and might play in enhancing our understanding of urban processes and the qualities of urban outcomes. big data may be intrinsically considered a neutral technology but � refracted through existing power structures and resource distributions � its application within cities is by no means guaranteed always to help in the amelioration of social injustices or in the promotion of urban well-being. in application, big data becomes a performative technology that can be, is and will be further used in the creation and regulation of the cities of this century, a process that will be messy and of mixed consequence. the task for urban studies research is to shape that performativity, and to challenge any tendency that emerges to the further entrenchment of social inequities. in pursuit of these aims, and sensitively deployed, big data can be cast as part of the route map to better urban futures.
2. title: a new framework for very large-scale urban modelling
authors: michael batty, richard milton.
abstract: the generation of ever-bigger data sets pertaining to the distribution of activities in cities is paralleled by massive increases in computer power and memory that are enabling very large-scale urban models to be constructed. here we present an effort to extend traditional land use�transport interaction (luti) models to extensive spatial systems so that they are able to track increasingly wide repercussions on the location of population, employment and related distributions of spatial interactions. the prototype model framework we propose and implement called quant is available anywhere, at any time, at any place, and is open to any user. it is characterised as a set of web-based services within which simulation, visualisation and scenario generation are configured. we begin by presenting the core spatial interaction model built around the journey to work, and extend this to deal with many sectors. we detail the computational environment, with a focus on the size of the problem which is an application to a 8436 zone system comprising england, scotland and wales generating matrices of around 71 million cells. we detail the data and spatial system, showing how we extend the model to visualise spatial interactions as vector fields and accessibility indicators. we briefly demonstrate the implementation of the model and outline how we can generate the impact of changes in employment and changes in travel costs that enable transport modes to compete for travellers. we conclude by indicating that the power of the new framework consists of running hundreds of �what if?� scenarios which let the user immediately evaluate their impacts and then evolve new and better ones.
3. title: from residence to movement: the nature of racial segregation in everyday urban mobility
authors: jennifer candipan, nolan edward phillips, robert j sampson, mario small.
abstract: while research on racial segregation in cities has grown rapidly over the last several decades, its foundation remains the analysis of the neighbourhoods where people reside. however, contact between racial groups depends not merely on where people live, but also on where they travel over the course of everyday activities. to capture this reality, we propose a new measure of racial segregation � the segregated mobility index (smi) � that captures the extent to which neighbourhoods of given racial compositions are connected to other types of neighbourhoods in equal measure. based on hundreds of millions of geotagged tweets sent by over 375,000 twitter users in the 50 largest us cities, we show that the smi captures a distinct element of racial segregation, one that is related to, but not solely a function of, residential segregation. a city�s racial composition also matters; minority group threat, especially in cities with large black populations and a troubled legacy of racial conflict, appears to depress movement across neighbourhoods in ways that produce previously undocumented forms of racial segregation. our index, which could be constructed using other data sources, expands the possibilities for studying dynamic forms of racial segregation including their effects and shifts over time.
4. title: life between buildings from a street view image: what do big data analytics reveal about neighbourhood organisational vitality?
authors: mingshu wang, floris vermeulen.
abstract: this article uses big data from images captured by google street view (gsv) to analyse the extent to which the built environment impacts the survival rate of neighbourhood-based social organisations in amsterdam, the netherlands. these organisations are important building blocks for social life in urban neighbourhoods. examining these organisations� relationships with their environment has been a useful way to study their vitality. to extract data on built environment features from gsv images, we applied a deep learning model, deeplabv3 . we then used elastic net regression to test the relationship between the built environment empirically � distinguishing between car-related, walking-related and mixed-use land infrastructure � and the survival of neighbourhood organisations. this testing approach is novel, to our knowledge not yet having been applied in urban studies. besides revealing the effects of built environment features on the social life between buildings, our study points to the value of easily applicable observational big data. data captured by gsv and other recently developed methods offer researchers the opportunity to conduct detailed yet relatively swift and inexpensive studies without resorting to overly coarse or common subjective measurements.
5. title: measuring risk of missing transfers in public transit systems using high-resolution schedule and real-time bus location data
authors: luyu liu, harvey j miller.
abstract: the emergence of urban big data creates new opportunities for a deeper understanding of transportation within cities, revealing patterns and dynamics that were previously hidden. public transit agencies are collecting and publishing high-resolution schedule and real-time vehicle location data to help users schedule trips and navigate the system. we can use these data to generate new insights into public transit delays, a major source of user dissatisfaction. leveraging open general transit feed specification (gtfs) and administrative automatic passenger counter (apc) data, we develop two measures to assess the risk of missing bus route transfers and the consequent time penalties due to delays. risk of missing transfers (romt) measures the empirical probability of missed transfers, and average total time penalty (attp) shows overall time loss compared to the schedule. we apply these measures to data from the central ohio transit authority (cota), a public transit agency serving the columbus, ohio, usa metropolitan area. we aggregate, visualise and analyse these measures at different spatial and temporal resolutions, revealing patterns that demonstrate the heterogeneous impacts of bus delays. we also simulate the impacts of dedicated bus lanes reducing missing risk and time penalties. results demonstrate the effectiveness of measures based on high-resolution schedule and real-time vehicle location data to assess the impacts of delays and to guide planning and decision making that can improve on-time performance.
6. title: understanding policing demand and deployment through the lens of the city and with the application of big data
authors: mark ellison, jon bannister, won do lee, muhammad salman haleem.
abstract: the effective, efficient and equitable policing of urban areas rests on an appreciation of the qualities and scale of, as well as the factors shaping, demand. it also requires an appreciation of the factors shaping the resources deployed in their address. to this end, this article probes the extent to which policing demand (crime, anti-social behaviour, public safety and welfare) and deployment (front-line resource) are similarly conditioned by the social and physical urban environment, and by incident complexity. the prospect of exploring policing demand, deployment and their interplay is opened through the utilisation of big data and artificial intelligence and their integration with administrative and open data sources in a generalised method of moments (gmm) multilevel model. the research finds that policing demand and deployment hold varying and time-sensitive association with features of the urban environment. moreover, we find that the complexities embedded in policing demands serve to shape both the cumulative and marginal resources expended in their address. beyond their substantive policy relevance, these findings serve to open new avenues for urban criminological research centred on the consideration of the interplay between policing demand and deployment.
7. title: big data, accessibility and urban house prices
authors: steven c bourassa, martin hoesli, louis merlin, john rennejohn renne.
abstract: big data applications are attracting increasing interest among urban researchers. one unexplored question is whether the inclusion of big data accessibility indices improves the accuracy of hedonic price models used for residential property valuation. this paper compares a big data index with an index derived from a regional travel demand model developed by local transportation planning agencies and traditional measures of accessibility defined as distances to employment centres. controls for submarkets and a combined spatial autoregressive and spatial error model are also assessed as tools for capturing the value of location. using single-family residential transactions from the miami, florida, metropolitan area, the study�s main conclusion is that the big data accessibility measure does not add meaningful explanatory or predictive power. in contrast, the spatial autoregressive and error model outperforms the other options considered.
8. title: the taming of chaos: optimal cities and the state of the art in urban systems research
authors: linnet taylor.
abstract: what can urban big data research tell us about cities? while studying cities as complex systems offers a new perspective on urban dynamics, we should dig deeper into the epistemological claims made by these studies and ask what it means to distance the urban researcher from the city. big data research has the tendency to flatten our perspective: it shows us technology users and their interactions with digital systems but does so often at the expense of the informal and irregular aspects of city life. it also presents us with the city as optimisable system, offering up the chance to engineer it for particular forms of efficiency or productivity. both optimisation itself, and the process of ordering of the city for optimisation, confer political and economic power and produce a hierarchy of interests. this commentary advocates that researchers connect systems research to questions of structure and power. to do this requires a critical approach to what is missing, what is implied by the choices about which data to collect and how to make them available, and an understanding of the ontologies that shape both the data sets and the urban spaces they describe.
9. title: out of the loop? on the radical and the routine in urban big data
authors: sarah barns.
abstract: this commentary interrogates what it means for routine urban behaviours to now be replicating themselves computationally. the emergence of autonomous or artificial intelligence points to the powerful role of big data in the city, as increasingly powerful computational models are now capable of replicating and reproducing existing spatial patterns and activities. i discuss these emergent urban systems of learned or trained intelligence as being at once radical and routine. just as the material and behavioural conditions that give rise to urban big data demand attention, so do the generative design principles of data-drive !"$,-/1235>��̬̽�̚�yjvna6h�o5�ojqj^jh�ud5�ojqj^jo(h�"�h�"�o(&h�"�h�"�5�cjojqj^jajo(hm8x5�cjojqj^jajh
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