Unravelling the variations of the society of England and Wales through diffusion mapping analysis of census 2011

We propose a new approach to identify geographical clustering and inequality hotspots from decadal census data, with a particular emphasis on the method itself. Our method uses diffusion mapping to study the 181 408 output areas in England and Wales (EW), which enables us to decompose the census data's EW-specific feature structures. We further introduce a localization metric, inspired by statistical physics, to reveal the significance of minority groups in London. Our findings can be adapted to analogous datasets, illuminating spatial patterns and differentiating within datasets, especially when meaning factors for determining the datasets' structure are scarce and spatially heterogeneous. This approach enhances our ability to describe and explore patterns of social deprivation and segregation across the country, thereby contributing to the development of targeted policies. We also underscore the method's intrinsic objectivity, guaranteeing its ability to offer comprehensive and unbiased analysis, unswayed by preconceived hypotheses or subjective interpretations of data patterns

Mobility Census for the analysis of rapid urban development

Traditionally urban structure and development are monitored using infrequent high-quality datasets such as censuses. However, human culture is accelerating and aggregating, leading to ever-larger cities and an increased pace of urban development. Our modern interconnected world also provides us with new data sources that can be leveraged in the study of cities. However, these often noisy and unstructured sources of big data pose new challenges. Here we propose a method to extract meaningful explanatory variables and classifications from such data. Using movement data from Beijing, which is produced as a byproduct of mobile communication, we show that meaningful features can be extracted, revealing for example the emergence and absorption of subcenters. In the future this method will allow the analysis of urban dynamics at a high spatial resolution (here, 500m) and near real-time frequency

Extracting hierarchical boundaries of places from noisy geotagged user-generated content

A place reflects the collective cognition of the geographical extent and semantics of a named spatial domain, acting as a vital reference to a particular space in daily discourse. Boundaries and toponyms are essential identifiers of places. Frameworks that are efficient in real-world boundary determination of cognitive places are still missing. The emergence of a large amount of geotagged user-generated content (geo-UGC) offers new opportunities to model the place boundaries from a more human-centric perspective. However, the broad geographical scales of places and the noise in geo-UGC data conflict with traditional approaches that only focus on places with similar spatial extents. In this paper, we advocate considering spatial hierarchy when determining place boundaries. We propose the Hierarchical Place Detector (HPD), a framework that composes noise detection, spatial hierarchy reconstruction, and boundary extraction, to rebuild the boundaries and spatial hierarchy of places from geo-UGC. The HPD is a state-of-the-art framework for determining and organizing place boundaries by the spatial hierarchy, thereby preserving morphology and geographical relationships among places. The hierarchical boundaries could be fundamental analytical units in various downstream applications, including spatial visualization, geographical information retrieval, navigation services, and spatial interaction modelling