Linking socioeconomic classes and land cover data in Lima, Peru: Assessment through the application of remote sensing and GIS

Abstract The spatial differentiation of socioeconomic classes in a city can deliver insight into the nexus of urban development and the environment. The purpose of this paper is to identify poor and rich regions in large cities according to the predominant physical characteristics of the regions. Meaningful spatial information from urban systems can be derived using remote sensing and GIS tools, especially in large difficult-to-manage cities where the dynamics of development results in rapid changes to urban patterns. We use here very high resolution imagery data for the identification of homogeneous socioeconomic zones in a city. We formulate the categorization task as a GIS analysis of an image classified with conventional techniques. Experiments are conducted using a QuickBird image of a study area in Lima, Peru. We provide accuracy assessment of results compared to ground truth data. Results show an approximated allocation of socioeconomic zones within Lima. The methodology described could also be applied to other urban centers, particularly large cities of Latin America, which have characteristics similar to those of the study area.

[1]  Eike Jakob Schütz.  Ciudades en America Latina : desarrollo barrial y vivienda , 1996 .

[2]  D. Amiran,et al.  Coastal deserts : their natural and human environments , 1973 .

[3]  Christopher D. Elvidge,et al.  Sociodemographic Characterization of Urban Areas Using Nighttime Imagery,Google Earth, Landsat,and “Social” Ground Truthing , 2006 .

[4]  Paul Gibson,et al.  Introductory Remote Sensing Principles and Concepts , 2000 .

[5]  Joana Barros,et al.  Urban growth in Latin American cities: exploring urban dynamics through agent-based simulation , 2004 .

[6]  L. S. Davis,et al.  An assessment of support vector machines for land cover classi(cid:142) cation , 2002 .

[7]  D. Quattrochi,et al.  Urban Remote Sensing , 2006 .

[8]  V. Mesev.  Remotely-Sensed Cities , 2003 .

[9]  T. Esch,et al.  An urban classification approach based on an object-oriented analysis of high resolution satellite imagery for a spatial structuring within urban areas , 2006 .

[10]  Executive Summary.  World Urbanization Prospects: The 2018 Revision , 2019 .

[11]  Yun Zhang,et al.  PERFORMANCE ASSESSMENT OF AUTOMATED FEATURE EXTRACTION TOOLS ON HIGH RESOLUTION IMAGERY , 2006 .

[12]  Martin Herold,et al.  Remote Sensing and Urban Growth Theory , 2006 .

[13]  Xiaojun Yang.  What is Urban Remote Sensing , 2011 .

[14]  Jose L. Casanova,et al.  Land use mapping methodology using remote sensing for the regional planning directives in Segovia, Spain , 2003 .

[15]  J. R. Jensen,et al.  Remote Sensing of Urban/Suburban Infrastructure and Socio‐Economic Attributes , 2011 .

[16]  Jefferson Fox,et al.  People and the environment : approaches for linking household and community surveys to remote sensing and GIS , 2003 .

[17]  Giles M. Foody,et al.  Fully-fuzzy supervised classification of sub-urban land cover from remotely sensed imagery: Statistical and artificial neural network approaches , 2001 .

[18]  M. Ramsey,et al.  Monitoring urban land cover change: An expert system approach to land cover classification of semiarid to arid urban centers , 2001 .

[19]  Giles M. Foody,et al.  Status of land cover classification accuracy assessment , 2002 .

[20]  J. Campbell.  Introduction to remote sensing , 1987 .