Temporal uncertainty in a small area open geodemographic classification

The 2001 Output Area Classification (2001 OAC) is an open source geodemographic classification of the UK built exclusively from 2001 UK Census data. There has been considerable user interest in its applicability to subsequent time periods, particularly given the potential propensity of characteristics and attributes in some areas to change during inter-censual periods. Users often purchase commercial geodemographic classification products in the belief that purely census-based classifications such as the 2001 OAC are uniformly unreliable because there is no temporal updating of input data. Yet there is evidence to suggest that whilst some UK neighborhoods are prone to sudden changes, many others change very little over protracted time periods. Using measures that are available at the small area level, temporal uncertainty indicators can be constructed to identify those areas that are less stable. Using mid-year population estimates and dwelling stock data, this article develops three temporal uncertainty indicators. These provide a reliable means of gauging the stability or otherwise of neighborhood conditions. The conclusion from this is that while a large number of small areas in the UK do experience change over time, this change is not uniform in either degree or distribution, or by geodemographic type. © 2013 John Wiley & Sons Ltd

Understanding urban gentrification through machine learning

Recent developments in the field of machine learning offer new ways of modelling complex socio-spatial processes, allowing us to make predictions about how and where they might manifest in the future. Drawing on earlier empirical and theoretical attempts to understand gentrification and urban change, this paper shows it is possible to analyse existing patterns and processes of neighbourhood change to identify areas likely to experience change in the future. This is evidenced through an analysis of socio-economic transition in London neighbourhoods (based on 2001 and 2011 Census variables) which is used to predict those areas most likely to demonstrate ‘uplift’ or ‘decline’ by 2021. The paper concludes with a discussion of the implications of such modelling for the understanding of gentrification processes, noting that if qualitative work on gentrification and neighbourhood change is to offer more than a rigorous post-mortem then intensive, qualitative case studies must be confronted with – and complemented by – predictions stemming from other, more extensive approaches. As a demonstration of the capabilities of machine learning, this paper underlines the continuing value of quantitative approaches in understanding complex urban processes such as gentrification

Extending geodemographics using data primitives: a review and a methodological proposal

This paper reviews geodemographic classifications and developments in contemporary classifications. It develops a critique of current approaches and identifiea a number of key limitations. These include the problems associated with the geodemographic cluster label (few cluster members are typical or have the same properties as the cluster centre) and the failure of the static label to describe anything about the underlying neighbourhood processes and dynamics. To address these limitations, this paper proposed a data primitives approach. Data primitives are the fundamental dimensions or measurements that capture the processes of interest. They can be used to describe the current state of an area in a multivariate feature space, and states can be compared over multiple time periods for which data are available, through for example a change vector approach. In this way, emergent social processes, which may be too weak to result in a change in a cluster label, but are nonetheless important signals, can be captured. As states are updated (for example, as new data become available), inferences about different social processes can be made, as well as classification updates if required. State changes can also be used to determine neighbourhood trajectories and to predict or infer future states. A list of data primitives was suggested from a review of the mechanisms driving a number of neighbourhood-level social processes, with the aim of improving the wider understanding of the interaction of complex neighbourhood processes and their effects. A small case study was provided to illustrate the approach. In this way, the methods outlined in this paper suggest a more nuanced approach to geodemographic research, away from a focus on classifications and static data, towards approaches that capture the social dynamics experienced by neighbourhoods

Identifying and Predicting Neighbourhood Level Gentrification: A Data Primitive Approach

Identifying and analysing neighbourhood change is a critical task for urban planners and policy makers and is an active academic field. However, traditional approaches to neighbourhood change often rely on temporally static data and methods that reduce complex processes to one cluster label, or one score for example. This leads to a fragmented understanding of neighbourhood dynamics, on a temporal scale that does not align with the processes, resulting in the failure to capture their complex and multifaceted nature. These limitations highlight the importance of adopting new and innovative methods to provide more accurate and dynamic insights into neighbourhood dynamics. This research subsequently proposes a new approach, data primitives, and a methodological framework for their application. Data primitives are measurements of the fundamental components that capture the driving characteristics of clearly conceptualised neighbourhood processes. Their utility is explored in a regional analysis, identifying 123 cycles of gentrification and their respective temporal properties, which are exhaustively validated via Google Earth and Google Street View. This demonstrates the effectiveness of data primitives at capturing processes, and quantifying their changes over time, to provide a more comprehensive picture of neighbourhood change. These validated cycles of gentrification are used as a training dataset for training three machine learning algorithms for predicting gentrification in England. Three models were created to predict the presence of gentrification, the type of gentrification, and the temporal properties of the predicted types of gentrification in England. These predicted cycles of gentrification are explored, generating novel insights for the neighbourhood change and gentrification communities. Overall, the results of this research have important implications for urban planning and policy making, as they can provide a framework for informing decisions on where to invest resources and how to mitigate the potential negative effects of gentrification, in an appropriately scheduled timetable of interventions. They also provide a framework for uncovering novel insights into the complexities of neighbourhood processes, and their impacts upon neighbourhood change, thus developing upon knowledge in suitable academic fields

Applications of new forms of data to demographics

At the outset, this thesis sets out to address limitations in conventional population data for the representation of stocks and flows of human populations. Until now, many of the data available for studying population behaviour have been static in nature, often collected on an infrequent basis or in an inconsistent manner. However, rapid expansion in the use of online technologies has led to the generation of a huge volume of data as a byproduct of individuals’ online activities. This thesis sets out to exploit just one of these new data channels: raw geographically referenced messages collected by the Twitter Online Social Network. The thesis develops a framework for the creation of functional population inventories from Twitter. Through the application of various data mining and heuristic techniques, individual Twitter users are attributed with key demographic markers including age, gender, ethnicity and place of residence. However, while these inventories possess the required data structure for analysis, little is understood about whom they represent and for what purposes they may be reliably employed. Thus a primary focus of this thesis is the assessment of Twitter-based population inventories at a range of spatial scales from the local to the global. More specifically, the assessment considers issues of age, gender, ethnicity, geographic distribution and surname composition. The value of such rich data is demonstrated in the final chapter in which a detailed analysis of the stocks and flows of peoples within the four largest London airports is undertaken. The analysis demonstrates both the extraction of conventional insight, such as passenger statistics and new insights such as footfall and sentiment. The thesis concludes with recommendations for the ways in which social media analysis may be used in demographics to supplement the analysis of populations using conventional sources of data

Using the urban landscape mosaic to develop and validate methods for assessing the spatial distribution of urban ecosystem service potential

The benefits that humans receive from nature are not fully understood. The ecosystem service framework has been developed to improve understanding of the benefits, or ecosystem services, that humans receive from the natural environment. Although the ecosystem service framework is designed to provide insights into the state of ecosystem services, it has been criticised for its neglect of spatial analysis. This thesis contains a critical discussion on the spatial relationships between ecosystem services and the urban landscape in Salford, Greater Manchester. An innovative approach has been devised for creating a landscape mosaic, which uses remotely-sensed spectral indices and land cover measurements. Five ecosystem services are considered: carbon storage, water flow mitigation, climate stress mitigation, aesthetics, and recreation. Analysis of ecosystem service generation uses the landscape mosaic, hotspot identification and measurements of spatial association. Ecosystem service consumption is evaluated via original perspectives of physical accessibility through a transport network, and greenspace visibility over a 3D surface. Results suggest that the landscape mosaic accuracy compares favourably to a map created using traditional classification methods. Ecosystem service patterns are unevenly distributed across Salford. The regulating services draw from similar natural resource locations, while cultural services have more diverse sources. The accessibility and visibility analysis provides evidence for the importance of urban trees as mitigators of ‘grey’ views, and urban parks as accessible producers of multiple services. Comprehensive ecosystem service analysis requires integration of quantitative and qualitative approaches. Evaluation of spatial relationships between ecosystem services and the physical landscapes in this thesis provides a practical method for improved measurement and management of the natural environment in urban areas. These findings can be used by urban planners and decision makers to integrate ecological considerations into proposed development schemes