Measuring intra-urban poverty using land cover and texture metrics derived from remote sensing data

Abstract

Elsevier Duque, JC.; Patiño Quinchía, JE.; Ruiz Fernández, LÁ.; Pardo Pascual, JE. (2015). Measuring intra-urban poverty using land cover and texture metrics derived from remote sensing data. Landscape and Urban Planning. 135:11-21. AbstractThis paper contributes empirical evidence about the usefulness of remote sensing imagery to quantify the degree of poverty at the intra-urban scale. This concept is based on two premises: first, that the physical appearance of an urban settlement is a reflection of the society; and second, that the people who reside in urban areas with similar physical housing conditions have similar social and demographic characteristics. We use a very high spatial resolution (VHR) image from one of the most socioeconomically divergent cities in the world, Medellin (Colombia), to extract information on land cover composition using per-pixel classification and on urban texture and structure using an automated tool for texture and structure feature extraction at object level. We evaluate the potential of these descriptors to explain a measure of poverty known as the Slum Index. We found that these variables explain up to 59% of the variability in the Slum Index. Similar approaches could be used to lower the cost of socioeconomic surveys by developing an econometric model from a sample and applying that model to the rest of the city and to perform intercensal or intersurvey estimates of intra-urban Slum Index maps.

Exploring the Potential of Machine Learning for Automatic Slum Identification from VHR Imagery

“…Different image texture measures and spatial pattern descriptors (structure measures) have been used for differentiating slum areas from formal ones in several cities of developing countries around the world [3,12,24,28,29]. We used current GE images (obtained in March 2016) and the regular grid of each city to extract image information using FETEX 2.0.…”

“…There has been a significant increase in the number of studies regarding the usefulness of remote sensing imagery to measure socioeconomic variables [3][4][5][6]. This trend is partly due to the increasing availability of satellite platforms, advances in methods and the decreasing costs of these images [7,8].…”

“…Remote sensing imagery may become an alternative source of information in urban settings for which survey data are scarce. In addition, this imagery may complement socioeconomic data that have been obtained from socioeconomic surveys [3]. The use of remote sensing data to estimate socioeconomic variables is based in the premise that the physical appearance of a human settlement is a reflection of the society that created it and is also based on the assumption that individuals who live in urban areas with similar physical housing conditions have similar social and demographic characteristics [9,10].…”

Capturing the Diversity of Deprived Areas with Image-Based Features: The Case of Mumbai

“…Based on the four morphological dimensions of deprived areas in Mumbai, i.e., environment, density, geometry, and texture pattern (building on the earlier work of [4,15,22,25,[55][56][57]), image features are created with the potential to capture the diversity of such areas (Figure 2). This list of image features (Figure 7) is generated based on distinguishing features reported in slum mapping studies (e.g., [4,10,22,23,56,[58][59][60]), as well as by considering the local characteristics of deprived areas in Mumbai.…”

Classifying the Built-Up Structure of Urban Blocks with Probabilistic Graphical Models and TerraSAR-X Spotlight Imagery

“…[17]. Alternatively, the metrics can be computed directly for each block separately [4,18]. Texture metrics are to a certain extent expressive; however, a more human-interpretable and effective approach attempts to formalize the evidence considered by a human interpreter by means of higher-level attributes.…”