Mapping Poverty and Slums Using Multiple Methodologies in Accra, Ghana

Engstrom, Ryan; Pavelesku, Dan; Tanaka, Tomomi; Wambile, Ayago

doi:10.1109/jurse.2019.8809052

Cited by 30 publications

(27 citation statements)

References 14 publications

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“…Engstrom et al 2019b, Engstrom et al 2019c, Sandborn and Engstrom 2016 In the simulated surveys drawn from the census, the rural dummy explains 24 percent of the variation in household non-monetary welfare in Tanzania, while the urban and estate sectors only explain 6 percent in Sri Lanka.28 This corresponds to the marginal R 2 inTable 7. The R 2 results differ slightly from the post-lasso model because the small area estimation model does not use weights.…”

mentioning

confidence: 98%

Small Area Estimation of Non-Monetary Poverty with Geospatial Data

Masaki¹,

Newhouse²,

Silwal³

et al. 2020

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The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

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confidence: 98%

Small Area Estimation of Non-Monetary Poverty with Geospatial Data

Masaki¹,

Newhouse²,

Silwal³

et al. 2020

Self Cite

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“…There is no easy solution to solve this gap; online visual data suitable for automated image classification in informal settlements is scarce, especially when the additional problem of how these environments change geographically; similar settlements in Haiti and Ghana have similar problems and features, but the details needed for image classification vary considerably. While remotely sensed imagery can be improved with other data sources [18] such as local censuses, there is still a need to contextualize local environment at the street scale [45] with onthe-ground imagery to improve the generalization and accuracy of machine learning models. While normally collecting these types of data are logistically challenging, the project team for this study has been using SV in multiple environments and time periods, amassing a considerable library of granular environmental imagery which can be used to explore various aspects of model training for these types of settings.…”

Section: International Journal Of Health Geographicsmentioning

confidence: 99%

“…As previously stated, not only do informal settlements pose considerable health problems, but they are also notoriously data poor, meaning that there is scant training data. The use of remotely sensed imagery as a data source to utilize machine learning including CNN has been tried for various health risks prevalent in informal settlements all around the world [ 1 18 , 25 , 30 , 44 , 48 , 50 ]. Of more relevance to this project, at least in terms of the data source if not the same environment, is the analysis of high resolution “neighborhood” imagery from sources such as Google Street View (GSV).…”

Section: Introductionmentioning

confidence: 99%

Exploring convolutional neural networks and spatial video for on-the-ground mapping in informal settlements

et al. 2021

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Background The health burden in developing world informal settlements often coincides with a lack of spatial data that could be used to guide intervention strategies. Spatial video (SV) has proven to be a useful tool to collect environmental and social data at a granular scale, though the effort required to turn these spatially encoded video frames into maps limits sustainability and scalability. In this paper we explore the use of convolution neural networks (CNN) to solve this problem by automatically identifying disease related environmental risks in a series of SV collected from Haiti. Our objective is to determine the potential of machine learning in health risk mapping for these environments by assessing the challenges faced in adequately training the required classification models. Results We show that SV can be a suitable source for automatically identifying and extracting health risk features using machine learning. While well-defined objects such as drains, buckets, tires and animals can be efficiently classified, more amorphous masses such as trash or standing water are difficult to classify. Our results further show that variations in the number of image frames selected, the image resolution, and combinations of these can be used to improve the overall model performance. Conclusion Machine learning in combination with spatial video can be used to automatically identify environmental risks associated with common health problems in informal settlements, though there are likely to be variations in the type of data needed for training based on location. Success based on the risk type being identified are also likely to vary geographically. However, we are confident in identifying a series of best practices for data collection, model training and performance in these settings. We also discuss the next step of testing these findings in other environments, and how adding in the simultaneously collected geographic data could be used to create an automatic health risk mapping tool.

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“…As an example, in the SLUMAP project (https://slumap.ulb.be/, accessed on 10 June 2021), gridded maps were produced using Sentinel-1/2 (Figure 8). Several studies (e.g., [63][64][65]) have confirmed that VHR resolution images (of 1 m and below spatial resolution) are not optimal for mapping deprived areas at city scale as they are impacted by too-high-level details, which is adding noise to the classification. Thus, an optimal resolution for city-scale maps depends on the urban morphology and typically vary around 2-5 m to capture deprived areas.…”

Section: The Role Of Low-cost Data For Mapping Slumsmentioning

confidence: 99%

Earth Observations and Statistics: Unlocking Sociodemographic Knowledge through the Power of Satellite Images

et al. 2021

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The continuous urbanisation in most Low-to-Middle-Income-Country (LMIC) cities is accompanied by rapid socio-economic changes in urban and peri-urban areas. Urban transformation processes, such as gentrification as well as the increase in poor urban neighbourhoods (e.g., slums) produce new urban patterns. The intersection of very rapid socio-economic and demographic dynamics are often insufficiently understood, and relevant data for understanding them are commonly unavailable, dated, or too coarse (resolution). Traditional survey-based methods (e.g., census) are carried out at low temporal granularity and do not allow for frequent updates of large urban areas. Researchers and policymakers typically work with very dated data, which do not reflect on-the-ground realities and data aggregation hide socio-economic disparities. Therefore, the potential of Earth Observations (EO) needs to be unlocked. EO data have the ability to provide information at detailed spatial and temporal scales so as to support monitoring transformations. In this paper, we showcase how recent innovations in EO and Artificial Intelligence (AI) can provide relevant, rapid information about socio-economic conditions, and in particular on poor urban neighbourhoods, when large scale and/or multi-temporal data are required, e.g., to support Sustainable Development Goals (SDG) monitoring. We provide solutions to key challenges, including the provision of multi-scale data, the reduction in data costs, and the mapping of socio-economic conditions. These innovations fill data gaps for the production of statistical information, addressing the problems of access to field-based data under COVID-19.

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Mapping Poverty and Slums Using Multiple Methodologies in Accra, Ghana

Cited by 30 publications

References 14 publications

Small Area Estimation of Non-Monetary Poverty with Geospatial Data

Small Area Estimation of Non-Monetary Poverty with Geospatial Data

Exploring convolutional neural networks and spatial video for on-the-ground mapping in informal settlements

Earth Observations and Statistics: Unlocking Sociodemographic Knowledge through the Power of Satellite Images

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