46,634 research outputs found
From buildings to cities: techniques for the multi-scale analysis of urban form and function
The built environment is a significant factor in many urban processes, yet direct measures of built form are
seldom used in geographical studies. Representation and analysis of urban form and function could provide
new insights and improve the evidence base for research. So far progress has been slow due to limited data
availability, computational demands, and a lack of methods to integrate built environment data with
aggregate geographical analysis. Spatial data and computational improvements are overcoming some of
these problems, but there remains a need for techniques to process and aggregate urban form data. Here we
develop a Built Environment Model of urban function and dwelling type classifications for Greater
London, based on detailed topographic and address-based data (sourced from Ordnance Survey
MasterMap). The multi-scale approach allows the Built Environment Model to be viewed at fine-scales for
local planning contexts, and at city-wide scales for aggregate geographical analysis, allowing an improved
understanding of urban processes. This flexibility is illustrated in the two examples, that of urban function
and residential type analysis, where both local-scale urban clustering and city-wide trends in density and
agglomeration are shown. While we demonstrate the multi-scale Built Environment Model to be a viable
approach, a number of accuracy issues are identified, including the limitations of 2D data, inaccuracies in
commercial function data and problems with temporal attribution. These limitations currently restrict the
more advanced applications of the Built Environment Model
A framework for community detection in heterogeneous multi-relational networks
There has been a surge of interest in community detection in homogeneous
single-relational networks which contain only one type of nodes and edges.
However, many real-world systems are naturally described as heterogeneous
multi-relational networks which contain multiple types of nodes and edges. In
this paper, we propose a new method for detecting communities in such networks.
Our method is based on optimizing the composite modularity, which is a new
modularity proposed for evaluating partitions of a heterogeneous
multi-relational network into communities. Our method is parameter-free,
scalable, and suitable for various networks with general structure. We
demonstrate that it outperforms the state-of-the-art techniques in detecting
pre-planted communities in synthetic networks. Applied to a real-world Digg
network, it successfully detects meaningful communities.Comment: 27 pages, 10 figure
- β¦