Detecting and Mapping Slums using Open Data

Urban and slum areas in Nairobi (False composite image created
by stacking image bands 7, 6 and 4 from the Landsat 8 satellite.

Turning back to slums, we just published paper entitled "Detecting and Mapping Slums using Open Data: A Case Study in Kenya" in the International Journal of Digital Earth. This work builds and extends our previous research on using new sources of data to explore the slum settlements in 3 cities in Kenya (i.e. Nairobi, Mombasa and Kisumu). Specifically, we examine how the fusion of Volunteered Geographical Information, Social Media, and other open data sources can complement remote sensing imagery in supporting slum detection, mapping and monitoring. 

We do this by using data mining tools (e.g. logistic regression, discriminant analysis and the See5 decision tree), to develop context-sensitive definitions for slums based on location, as well as for testing the generalizability of indicators and derived slum models. The end result is an indicator database for slums using open sources of physical and socio-economic data that can be used to characterize slum settlements. If you wish to know more, below we provide the abstract to the paper along with some of the figures and the full citation with a link to the paper itself.

Abstract:

The worldwide slum population currently stands at over one billion, with substantial growth expected in the coming decades. Traditionally, slums have been mapped using information derived mainly from either physical indicators using remote sensing data, or socio-economic indicators using census data. Each data source on its own provides only a partial view of slums, an issue further compounded by data poverty in less developed countries. To overcome such issues, this paper explores the fusion of traditional with emerging open data sources and data mining tools to identify additional indicators that can be used to detect and map the presence of slums, map their footprint, and map their evolution. Towards this goal, we develop an indicator database for slums using open sources of physical and socio-economic data that can be used to characterize slum settlements. Using this database, we then leverage data mining techniques to identify the most suitable combination of these indicators for mapping slums. Using three cities in Kenya as test cases, results show that the fusion of these data can improve the mapping accuracy of slums. These results suggest that the proposed approach can provide a viable solution to the emerging challenge of monitoring the growth of slums.

Keywords: Slums; Remote Sensing; Socio-economic; Urban sustainability; Data mining; Kenya

Study areas in Kenya

Methodology workflow

Distribution of positive classified cases for slums for (a) logistic regression, (b) discriminant analysis and (c) the See5 decision tree.

Full Reference:

Mahabir, R., Agouris, P., Stefanidis, A., Croitoru, A. and Crooks, A.T. (2018), Detecting and Mapping Slums using Open Data: A Case Study in Kenya, International Journal of Digital Earth. DOI: https://doi.org/10.1080/17538947.2018.1554010. (pdf)

A Review of High and Very High Resolution Remote Sensing Approaches for Detecting and Mapping Slums

Regular readers of this site might of noticed that we have an interest in slums. In the past this has focused on modeling them from an agent-based perspective, comparing volunteered geographical information to more authoritative data on slums, to that of attempting to come up with a Slum Severity Index. However, more recently we have taken to looking at how remote sensing approaches have been and can be used to detect and map slums.

To this end we recently had a review paper accepted in Urban Systems entitled "A Critical Review of High and Very High Resolution Remote Sensing Approaches for Detecting and Mapping Slums: Trends, Challenges and Emerging Opportunities". In this paper we carry out a comprehensive review of studies that have used high and very high resolution (H/VH-R) remote sensing techniques to detect and map slums (along with their global footprint). We discuss approaches used (e.g. multi-scale, image texture analysis, landscape analysis, object-based image analysis, building feature extraction, data mining, socio-economic measures) using H/VH-R imagery for identifying and mapping slums, listing what are the limitations and advantages of each. After this, we  discuss emerging sources of geospatial data that should we thing should be considered (e.g., volunteer geographic information, VGI, social media) in conjunction with growing trends and advancements in technology (e.g., geosensor networks, unmanned aerial vehicles (UAVs) or “drones) when trying to map and monitor slums. We argue that it is only through such data integration and analysis that we can then create a benchmark for determining the most suitable methods for mapping slums in a given locality. Below you can read the abstract of the paper and see some of the figures we use to support our discussion, along with the full reference.

Abstract: Slums are a global urban challenge, with less developed countries being particularly impacted. To adequately detect and map them, data is needed on their location, spatial extent and evolution. High- and very high-resolution remote sensing imagery has emerged as an important source of data in this regard. The purpose of this paper is to critically review studies that have used such data to detect and map slums. Our analysis shows that while such studies have been increasing over time, they tend to be concentrated to a few geographical areas and often focus on the use of a single approach (e.g., image texture and object-based image analysis), thus limiting generalizability to understand slums, their population, and evolution within the global context. We argue that to develop a more comprehensive framework that can be used to detect and map slums, other emerging sourcing of geospatial data should be considered (e.g., volunteer geographic information) in conjunction with growing trends and advancements in technology (e.g., geosensor networks). Through such data integration and analysis we can then create a benchmark for determining the most suitable methods for mapping slums in a given locality, thus fostering the creation of new approaches to address this challenge.

Keywords: high and very high resolution imagery; remote sensing, slums; geosensor networks; image analysis.

Global distribution of urban and slum populations.

Country level distribution of H/VH-R studies (studies published between 1997-2016).

OSM and Google Maps views of Kibera slum (a) Top:Left OSM and right Google Maps (b) Bottom:Left OSM and right Google Maps.

Full Reference:

Mahabir, R., Croitoru, A., Crooks, A.T., Agouris, P. and Stefanidis, A. (2018), A Critical Review of High and Very High Resolution Remote Sensing  Approaches for Detecting and Mapping Slums: Trends, Challenges and Emerging Opportunities, Urban Science. 2(1), 8; doi:10.3390/urbansci2010008 (pdf)

As always, any thoughts or comments are most welcome.

Authoritative and VGI in a Developing Country: A Comparative Case Study of Road Datasets in Nairobi

Continuing with our work of volunteered geographic information (VGI), we recently had a paper published in  the ISPRS International Journal of Geo-Information entitled: "Authoritative and Volunteered Geographical Information in a Developing Country: A Comparative Case Study of Road Datasets in Nairobi, Kenya.”

The motivation behind the paper was that while there are numerous studies comparing VGI to authoritative data in the developed world, there are very few that do so in developing world. In order to address this issue in the paper we compare the quality of authoritative road data (i.e. from the Regional Center for Mapping of Resources for Development - RCMRD) and non-authoritative crowdsourced road data (i.e. from OpenStreetMap (OSM) and Google’s Map Maker) in conjunction with population data in and around Nairobi, Kenya.

Results from our analysis show variability in coverage between all these datasets. RCMRD provided the most complete, albeit less current, coverage when taking into account the entire study area, while OSM and Map Maker showed a degradation of coverage as one moves from central Nairobi towards more rural areas. Further information including the abstract to our paper, some figures and full reference is given below.

Abstract:

With volunteered geographic information (VGI) platforms such as OpenStreetMap (OSM) becoming increasingly popular, we are faced with the challenge of assessing the quality of their content, in order to better understand its place relative to the authoritative content of more traditional sources. Until now, studies have focused primarily on developed countries, showing that VGI content can match or even surpass the quality of authoritative sources, with very few studies in developing countries. In this paper we compare the quality of authoritative (data from the Regional Center for Mapping of Resources for Development - RCMRD) and non-authoritative (data from OSM and Google’s Map Maker) road data in conjunction with population data in and around Nairobi, Kenya. Results show variability in coverage between all these datasets. RCMRD provided the most complete, albeit less current, coverage when taking into account the entire study area, while OSM and Map Maker showed a degradation of coverage as one moves from central Nairobi towards rural areas. Furthermore, OSM had higher content density in large slums, surpassing the authoritative datasets at these locations, while Map Maker showed better coverage in rural housing areas. These results suggest a greater need for a more inclusive approach using VGI to supplement gaps in authoritative data in developing nations.

Keywords: Volunteered Geographic Information; Crowdsourcing; Road Networks; Population Data; Kenya  

Road Coverage per km2

Pairwise difference in road coverage. Clockwise from top left: i) RCMRD 2011 versus Map Maker 2014; ii) RCMRD 2011 versus OSM 2011; iii) RCMRD 2011 versus OSM 2014; iv) OSM 2014 versus Map Maker 2014 (Red cells: first layer has higher coverage; Green cells: second layer has higher coverage).

Full Reference:

Mahabir, R., Stefanidis, A., Croitoru, A., Crooks, A.T. and Agouris, P. (2017), “Authoritative and Volunteered Geographical Information in a Developing Country: A Comparative Case Study of Road Datasets in Nairobi, Kenya”, ISPRS International Journal of Geo-Information, 6(1): 24, doi:10.3390/ijgi6010024.

As always any thoughts or comments about this work are welcome.

The study of slums as social and physical constructs: challenges and emerging research opportunities

Conceptual model for integrating social
and physical constructs to monitor,
analyze and model slums.

Continuing our research on slums, we have just had a paper published in the journal Regional Studies, Regional Science entitled "The Study of Slums as Social and Physical Constructs: Challenges and Emerging Research Opportunities". In this open access publication we review past lines of research with respect to studying slums which often focus on one of three constructs: (1) exploring the socio-economic and policy issues; (2) exploring the physical characteristics; and, lastly, (3) those modelling slums. We argue that while such lines of inquiry have proved invaluable with respect to studying slums, there is a need for  a  more  holistic  approach  for  studying  slums  to truly understand  them at the local, national and regional scales. Below you can read the abstract of our paper:

"Over 1 billion people currently live in slums, with the number of slum dwellers only expected to grow in the coming decades. The vast majority of slums are located in and around urban centres in the less economically developed countries, which are also experiencing greater rates of urbanization compared with more developed countries. This rapid rate of urbanization is cause for significant concern given that many of these countries often lack the ability to provide the infrastructure (e.g., roads and affordable housing) and basic services (e.g., water and sanitation) to provide adequately for the increasing influx of people into cities. While research on slums has been ongoing, such work has mainly focused on one of three constructs: exploring the socio-economic and policy issues; exploring the physical characteristics; and, lastly, those modelling slums. This paper reviews these lines of research and argues that while each is valuable, there is a need for a more holistic approach for studying slums to truly understand them. By synthesizing the social and physical constructs, this paper provides a more holistic synthesis of the problem, which can potentially lead to a deeper understanding and, consequently, better approaches for tackling the challenge of slums at the local, national and regional scales."

Keywords: Slums; informal settlements; socio-economic; remote sensing; crowdsourced information; modelling.

Framework for studying and understanding slums.

We hope you enjoy this paper and we wound be interested in receiving any feedback.

Full Reference:

Mahabir, R., Crooks, A.T., Croitoru, A. and Agouris, P. (2016), “The Study of Slums as Social and Physical Constructs: Challenges and Emerging Research Opportunities”, Regional Studies, Regional Science, 3(1): 737-757. (pdf)

New Paper: Agent-based modeling of Slums

We have just had a  paper published in Journal of Artificial Societies and Social Simulation the entitled "Slumulation: An Agent-Based Modeling Approach to Slum Formations". This paper adds to the small but growing body of agent-based models exploring slums. Below is the abstract from the paper:

Slums provide shelter for nearly one third of the world's urban population, most of them in the developing world. Slumulation represents an agent-based model which explores questions such as i) how slums come into existence, expand or disappear ii) where and when they emerge in a city and iii) which processes may improve housing conditions for urban poor. The model has three types of agents that influence emergence or sustenance of slums in a city: households, developers and politicians, each of them playing distinct roles. We model a multi-scale spatial environment in a stylized form that has housing units at the micro-scale and electoral wards consisting of multiple housing units at the macro-scale. Slums emerge as a result of human-environment interaction processes and inter-scale feedbacks within our model.

This paper is a starting point for a recently awarded a National Science Foundation grant entitled "An Integrated Simulation Framework to Explore Spatio-Temporal Dynamics of Slum Formation".  Moreover, anyone wanting to run or download the model can do so here.

Full reference:

Patel, A., Crooks, A.T. and Koizumi, N. (2012), Slumulation: an Agent-based Modeling Approach to Slum Formations, Journal of Artificial Societies and Social Simulation, 15 (4). Available at http://jasss.soc.surrey.ac.uk/15/4/2.html