A data driven approach to mapping urban neighbourhoods

Neighbourhoods have been described by the UK Secretary of State for Communities and Local Government as the “building blocks of public service society”. Despite this, difficulties in data collection combined with the concept’s subjective nature have left most countries lacking official neighbourhood definitions. This issue has implications not only for policy, but for the field of computational social science as a whole (with many studies being forced to use administrative units as proxies despite the fact that these bear little connection to resident perceptions of social boundaries). In this paper we illustrate that the mass linguistic datasets now available on the internet need only be combined with relatively simple linguistic computational models to produce definitions that are not only probabilistic and dynamic, but do not require a priori knowledge of neighbourhood names

Walkability around primary schools and area deprivation across Scotland

Background: A number of studies based in the US, Canada, and Australia, have found evidence of associations between the built environment (BE) and mode of transport to school, and links between active travel and deprivation. Limited research in the UK compares potential BE supports for walking to school by area deprivation. Within this study, we gathered data on BE attributes previously linked to active travel, i.e., street/path connectivity, and dwelling density, created a composite ‘walkability score’ (WS) for areas around primary schools across urban Scotland, and explored whether poorer areas exhibit lower scores than more affluent areas, or vice versa. We consider this to be a novel approach as few studies have compared BE features by deprivation across a whole country. Methods: Address and road/path maps were obtained and primary schools (N = 937) across mainland Scotland were mapped. Schools were attributed income deprivation scores (scores divided into quintiles (Q1: least deprived, Q5: most deprived)). Catchment area (CA) boundaries, i.e., the geographic area representing eligibility for local school attendance, were drawn around schools, and WS calculated for each CA. We compared mean WS by income quintile (ANOVA), for all local authorities (LAs) combined (N = 29), and separately for the four LAs with the greatest number of schools included in the analysis. Results: For all LAs combined, the least deprived quintile (Q1) showed a significantly lower WS (−0.61), than quintiles 3, 4 and 5 (Q2: −0.04 (non-sig), Q3: 0.38, Q4: 0.09, Q5: 0.18); while for Glasgow the second least deprived quintile (Q2) showed significantly higher WS (Q1: 1.35, Q2: 1.73), than middling (Q3: 0.18) and most deprived quintiles (Q4: 0.06, Q5: −0.10). Conclusion: WS differ by deprivation with patterns varying depending on the spatial scale of the analysis. It is essential that less walkable areas are provided with the resources to improve opportunities to engage in active travel

Ways of interpreting urban regeneration: Hamburg, London, Brussels and Rome

Over the coming decades all cities throughout and beyond Europe, be they large or small, will face the great challenge of regeneration. European Commission has promoted a “regeneration agenda” focused on an integrated sustainable approach. But, while the European Commission draws the path, European cities provide a variety of ways to transform drafts in deeds. The four case studies described below – Hamburg, London, Brussels, Rome – give evidence that, in the last decades, every city had drawn its own “regeneration way”, with a different level of sensitiveness regarding the European principles. However, all the case studies deliver at least one action attuned to the principles of a sustainable regeneration, and it’s possible to select from every experience the “good” that has been realized

Gradients in urban material composition: A new concept to map cities with spaceborne imaging spectroscopy data

To understand processes in urban environments, such as urban energy fluxes or surface temperature patterns, it is important to map urban surface materials. Airborne imaging spectroscopy data have been successfully used to identify urban surface materials mainly based on unmixing algorithms. Upcoming spaceborne Imaging Spectrometers (IS), such as the Environmental Mapping and Analysis Program (EnMAP), will reduce the time and cost-critical limitations of airborne systems for Earth Observation (EO). However, the spatial resolution of all operated and planned IS in space will not be higher than 20 to 30 m and, thus, the detection of pure Endmember (EM) candidates in urban areas, a requirement for spectral unmixing, is very limited. Gradient analysis could be an alternative method for retrieving urban surface material compositions in pixels from spaceborne IS. The gradient concept is well known in ecology to identify plant species assemblages formed by similar environmental conditions but has never been tested for urban materials. However, urban areas also contain neighbourhoods with similar physical, compositional and structural characteristics. Based on this assumption, this study investigated (1) whether cover fractions of surface materials change gradually in urban areas and (2) whether these gradients can be adequately mapped and interpreted using imaging spectroscopy data (e.g. EnMAP) with 30 m spatial resolution. Similarities of material compositions were analysed on the basis of 153 systematically distributed samples on a detailed surface material map using Detrended Correspondence Analysis (DCA). Determined gradient scores for the first two gradients were regressed against the corresponding mean reflectance of simulated EnMAP spectra using Partial Least Square regression models. Results show strong correlations with R2 = 0.85 and R2 = 0.71 and an RMSE of 0.24 and 0.21 for the first and second axis, respectively. The subsequent mapping of the first gradient reveals patterns that correspond to the transition from predominantly vegetation classes to the dominance of artificial materials. Patterns resulting from the second gradient are associated with surface material compositions that are related to finer structural differences in urban structures. The composite gradient map shows patterns of common surface material compositions that can be related to urban land use classes such as Urban Structure Types (UST). By linking the knowledge of typical material compositions with urban structures, gradient analysis seems to be a powerful tool to map characteristic material compositions in 30 m imaging spectroscopy data of urban areas

Making sense of assets: Community asset mapping and related approaches for cultivating capacities

This working paper critically reviews some main aspects from asset based approaches highlights key strengths and weaknesses for future research/development. Drawing on a large body of reports and relevant literature we draw on different theoretical traditions and critiques, as well as practices and processes embedded within a broad range of approaches including, widely acknowledged frameworks such Asset Based Community Development (ABCD), Appreciative Inquiry (AI), Sustainable Livelihood Approaches (SLA) and Community Capitals Framework (CCF). Although these are presented as distinct approaches, there is a sense of evolution through them and many of them overlap (in terms of both theories and methodologies). We also include emerging frameworks, including geographical, socio-spatial, visual and creative approaches, stemming from a number of projects within AHRC’s Connected Communities programme and additional collaborations

Intergroup relations in a super-diverse neighbourhood: the dynamics of population composition, context and community

There is now an extensive literature demonstrating that experiences of migration and diversity differ significantly between and across local geographies. Three broad explanations for differences in local outcomes have been put forward (Robinson, 2010): first, population composition – the characteristics of individuals living in the neighbourhood; second, context – the social and physical environment; and third, community – socio-cultural histories and collective identities. Few studies examine the linkages between all three explanations and their relative importance. This article applies all three explanations to intergroup relations in a super-diverse context. It draws on data from a mixed methods case study of a neighbourhood in Glasgow, Scotland where long-term white and ethnic minority communities reside alongside Central and Eastern European migrants, refugees and other recent arrivals. The evidence comprises local statistics and documentary evidence, participant observation and qualitative and walk-along interviews with residents and local organisations. The findings highlight the different ways in which people respond to super-diversity, and the importance of the neighbourhood context and the material conditions for intergroup relations. The article thus demonstrates the ambiguities that arise from applying the dynamics of population composition, context and community to neighbourhood analysis, with implications for the study of neighbourhoods more widely

Community-driven sanitation improvement in deprived urban neighbourhoods: meeting the challenges of local collective action, co-production, affordability and a trans-sectoral approach.

There is an international consensus that urban sanitary conditions are in great need of improvement, but sharp disagreement over how this improvement should be pursued. Both market-driven and state-led efforts to improve sanitation in deprived communities tend to be severely compromised, as there is a lack of effective market demand (due to collective action problems) and severe barriers to the centralized provision of low-cost sanitation facilities. In principle, community-driven initiatives have a number of advantages. But community-driven sanitary improvement also faces serious challenges, including: 1) The collective action challenge of getting local residents to coordinate and combine their demands for sanitary improvement; 2) The co-production challenge of getting the state to accept community-driven approaches to sanitary improvement, and where necessary to co-invest and take responsibility for the final waste disposal; 3) The affordability challenge of finding improvements that are affordable and acceptable to both the state and the community – and to other funders if relevant; 4) The trans-sectoral challenge of ensuring that other poverty-related problems, such as insecure tenure, do not undermine efforts to improve sanitation. Each of these challenges is analysed in some detail in the pages that follow. The report then goes on to examine two community-driven approaches to urban sanitation improvement that have been expanding for more than two decades, one in Pakistan and the other in India. It is argued that a large part of their success lies in the manner in which they have met and overcome the aforementioned challenges. Indeed, both overcame the co-production challenge to the point where sanitary improvement became the basis for attempts to radically improve community–government relations – relations unfortunately also very dependent on other political dynamics. They also systematically tackled other, less institutionally-rooted challenges, such as the lack of local technical skills in building and maintaining improved sanitary facilities

Driven to Distraction: Self-Supervised Distractor Learning for Robust Monocular Visual Odometry in Urban Environments

We present a self-supervised approach to ignoring "distractors" in camera images for the purposes of robustly estimating vehicle motion in cluttered urban environments. We leverage offline multi-session mapping approaches to automatically generate a per-pixel ephemerality mask and depth map for each input image, which we use to train a deep convolutional network. At run-time we use the predicted ephemerality and depth as an input to a monocular visual odometry (VO) pipeline, using either sparse features or dense photometric matching. Our approach yields metric-scale VO using only a single camera and can recover the correct egomotion even when 90% of the image is obscured by dynamic, independently moving objects. We evaluate our robust VO methods on more than 400km of driving from the Oxford RobotCar Dataset and demonstrate reduced odometry drift and significantly improved egomotion estimation in the presence of large moving vehicles in urban traffic.Comment: International Conference on Robotics and Automation (ICRA), 2018. Video summary: http://youtu.be/ebIrBn_nc-