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Identifying residential neighbourhood types from settlement points in a machine learning approach

Identifying residential neighbourhood types from settlement points in a machine learning approach
Identifying residential neighbourhood types from settlement points in a machine learning approach
Remote sensing techniques are now commonly applied to map and monitor urban land uses to measure growth and to assist with development and planning. Recent work in this area has highlighted the use of textures and other spatial features that can be measured in very high spatial resolution imagery. Far less attention has been given to using geospatial vector data (i.e. points, lines, polygons) to map land uses. This paper presents an approach to distinguish residential settlement types (regular vs. irregular) using an existing database of settlement points locating structures. Nine data features describing the density, distance, angles, and spacing of the settlement points are calculated at multiple spatial scales. These data are analysed alone and with five common remote sensing measures on elevation, slope, vegetation, and nighttime lights in a supervised machine learning approach to classify land use areas. The method was tested in seven provinces of Afghanistan (Balkh, Helmand, Herat, Kabul, Kandahar, Kunduz, Nangarhar). Overall accuracy ranged from 78% in Kandahar to 90% in Nangarhar. This research demonstrates the potential to accurately map land uses from even the simplest representation of structures.
0198-9715
1-10
Jochem, Warren
ef65df67-4364-4438-92e9-f93ceedb8da1
Bird, Tomas
b491394a-2b91-42d5-8262-d1c0e9ff17cd
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Jochem, Warren
ef65df67-4364-4438-92e9-f93ceedb8da1
Bird, Tomas
b491394a-2b91-42d5-8262-d1c0e9ff17cd
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e

Jochem, Warren, Bird, Tomas and Tatem, Andrew (2018) Identifying residential neighbourhood types from settlement points in a machine learning approach. Computers, Environment and Urban Systems, 1-10. (doi:10.1016/j.compenvurbsys.2018.01.004).

Record type: Article

Abstract

Remote sensing techniques are now commonly applied to map and monitor urban land uses to measure growth and to assist with development and planning. Recent work in this area has highlighted the use of textures and other spatial features that can be measured in very high spatial resolution imagery. Far less attention has been given to using geospatial vector data (i.e. points, lines, polygons) to map land uses. This paper presents an approach to distinguish residential settlement types (regular vs. irregular) using an existing database of settlement points locating structures. Nine data features describing the density, distance, angles, and spacing of the settlement points are calculated at multiple spatial scales. These data are analysed alone and with five common remote sensing measures on elevation, slope, vegetation, and nighttime lights in a supervised machine learning approach to classify land use areas. The method was tested in seven provinces of Afghanistan (Balkh, Helmand, Herat, Kabul, Kandahar, Kunduz, Nangarhar). Overall accuracy ranged from 78% in Kandahar to 90% in Nangarhar. This research demonstrates the potential to accurately map land uses from even the simplest representation of structures.

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Accepted/In Press date: 7 January 2018
e-pub ahead of print date: 18 January 2018

Identifiers

Local EPrints ID: 417168
URI: https://eprints.soton.ac.uk/id/eprint/417168
ISSN: 0198-9715
PURE UUID: cbe9b169-4a0d-47bf-85c1-6166b4d4a9b5
ORCID for Andrew Tatem: ORCID iD orcid.org/0000-0002-7270-941X

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Date deposited: 23 Jan 2018 17:30
Last modified: 06 Jun 2018 12:28

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