Last week I gave a talk to Department of Computational and Data Sciences colloquium series entitled to "Computational Modeling of Slums: Progress and Challenges" in which I discussed some of our current work on slums. This talk was inspired by several of the papers we have written or currently writing on slums As we live stream and store these talks on YouTube, I thought I would share this one here (apologies about the sound quality).
Abstract:
Over the last 50 years the world has increasingly become more urbanized, much of this growth has occurred in less developed countries, which often lack the resources to accommodate such growth. This has led to the growth of slums, which is estimated to be home for other 1 billion people. The UN-Habitat projects slum population to double by 2030, which would make them home for 2 in 5 people living in cities. In this talk I will introduce slums, discuss their growth, and provide an overview on what progress has been made to studying and modeling them. This will lead to a discussion of a series of key challenges that need to be addressed if we are to tackle slums from a computational perspective.
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)
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.
"More than 900 million people or one third of the world’s urban population lives in either slum or squatter settlements. Urbanization rates in developing countries are often so rapid that formal housing development cannot meet the demand. In the past decades, international, national and local development communities have taken several policy actions in an attempt to improve the living conditions of people within slums or to eradicate them completely. However, such policies have largely failed and slum-free cities have remained a distant goal for many developing countries. This chapter argues that for informed policymaking, it is important to investigate questions related to slum formation such as: (1) How do slums form and expand? (2) Where and when are they formed? (3) What types of structural changes and/or policy interventions could improve housing conditions for the urban poor? In order to address these questions, this chapter develops a geosimulation model that is capable of exploring the spatio-temporal dynamics of slum formation and simulating future formation and expansion of slums within cities of the developing world. Our geosimulation model integrates agent-based modeling (ABM) and Geographic Information System (GIS), methods that are often applied separately to explore slums. In our model, ABM simulates human behavior and GIS provides a spatial environment for the housing market. GIS is also used to analyze empirical data using spatial analyses techniques, which is in turn used to validate the model outputs. The core of this framework is a linked dynamic model operating at both micro and macro geographic and demographic scales. The model explores the collective effect of many interacting inhabitants of slums as well as non-slum actors (e.g. local government) and how their interactions within the spatial environment of the city generate the emergent structure of slums at the macro scale. We argue that when empirical data is absent, geosimulation provides useful insights to study implications of various policies. The goal of this framework is to develop a decision support tool that could allow urban planners and policymakers to experiment with new policy ideas ex-ante in a simulated environment. We calibrate and validate the model using data from Ahmedabad, the sixth largest city of India, where 41% of its population lives in slums. This is one of the first attempts to develop an integrated and multi-scalar analytical framework to tackle slum issues in the developing world at multiple spatial scales."
Keywords: Slums Agent-based modeling India Geosimulation
Integrated Simulation Framework
Slum Locations and Slum Sizes in Ahmedabad, 2001
Spatial Sprawl Experiment
Full Reference:
Patel, A., Crooks, A.T. and Koizumi, N. (2018). “Spatial Agent-based Modeling to Explore Slum Formation Dynamics in Ahmedabad, India” in Thill J.C. and Drajicavic, S. (eds.), Geocomputational Analysis and Modeling of Regional Systems, Springer, New York, NY, pp 121-141. (pdf)
Further details of the model and project can be found here. As normal any thoughts and comments are most welcome.
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).
As you might of guessed the paper is about riots but that is not all. In the paper we have a highly detailed cognitive model implemented through the PECS (Physical conditions, Emotional state, Cognitive capabilities, and Social status) framework based around identity theory. The purpose of the model (and paper) is to explore how the unique socioeconomic variables underlying Kibera, a slum in Nairobi, coupled with local interactions of its residents, and the spread of a rumor, may trigger a riot such as those seen in 2007.
In order to explore this question from the "bottom up" we have developed a novel agent-based model that integrates social network analysis (SNA) and geographic information systems (GIS) for this purpose. In the paper we argue that this integration facilitates the modeling of dynamic social networks created through the agents’ daily interactions. The GIS is used to develop a realistic environment for agents to move and interact that includes a road network and points of interest which impact their daily lives.
Below is the abstract and a summary of its highlights in order to give you a sense of what our research contribution is. In addition to this we also provide some images either from the paper itself or the from Overview, Design Concepts, and Details (ODD) protocol. Finally at the bottom of this post you can see one of the simulation runs, details of where the model can be downloaded along with the full citation.
Paper Abstract:
Immediately after the 2007 Kenyan election results were announced, the country erupted in protest. Riots were particularly severe in Kibera, an informal settlement located within the nations capital, Nairobi. Through the lens of geosimulation, an agent-based model is integrated with social network analysis and geographic information systems to explore how the environment and local interactions underlying Kibera, combined with an external trigger, such as a rumor, led to the emergence of riots. We ground our model on empirical data of Kibera’s geospatial landscape, heterogeneous population, and daily activities of its residents. In order to effectively construct a model of riots, however, we must have an understanding of human behavior, especially that related to an individual’s need for identity and the role rumors play on a person’s decision to riot. This provided the foundation to develop the agents’ cognitive model, which created a feedback system between the agents’ activities in physical space and interactions in social space. Results showed that youth are more susceptible to rioting. Systematically increasing education and employment opportunities, however, did not have simple linear effects on rioting, or even on quality of life with respect to income and activities. The situation is more complex. By linking agent-based modeling, social network analysis, and geographic information systems we were able to develop a cognitive framework for the agents, better represent human behavior by modeling the interactions that occur over both physical and social space, and capture the nonlinear, reinforcing nature of the emergence and dissolution of riots.
Keywords: agent-based modeling; geographic information systems; social network analysis; riots; social influence; rumor propagation.
Paper Highlights:
An agent-based model integrates geographic information systems and social network analysis to model the emergence of riots.
The physical environment and agent attributes are developed using empirical data, including GIS and socioeconomic data.
The agent’s cognitive framework allowed for modeling their activities in physical space and interactions in social space.
Through the integration of the three techniques, we were able to capture the complex, nonlinear nature of riots.
Results show that youth are most vulnerable, and, increasing education and employment has nonlinear affects on rioting.
The high-level UML diagram of the model
A high-level representation of the model’s agent behavior incorporated into the PECS framework
An example of the evolution of social networks of ten Residents across the first two days of a simulation run.
The movie below shows the agent-based model which explores ethnic clashes in the Kenyan slum. The environment is made up of households, businesses, and service facilities (such data comes from OpenStreetMap). Agents within the model use a transportation network to move across the environment. As agents go about their daily activities, they interact with other agents - building out an evolving social network. Agents seek to meet their identity standard. Failure to reach their identity standard increases the agents frustration which can lead to an aggressive response (i.e. moving from blue to red color) such as rioting.
As with many of our models, we provide the data, model code and detailed model description in the form of the ODD protocol for others to use, learn more or to extend. Click here for more information.
Full Reference:
Pires, B. and Crooks, A.T. (2017), Modeling the Emergence of Riots: A Geosimulation Approach, Computers, Environment and Urban Systems, 61: 66-80. (pdf)
As normal, any thoughts or comments are most appreciated.
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."
Over the last few months we have been working on developing a agent-based model which explores the spread and containment of tuberculosis. Today is the first time that we show the model to a academic audience at the AAG Annual meeting. To give a sense of our research, below is the abstract:
Tuberculosis (TB) is a global problem and especially in developing
countries. After human immunodeficiency virus (HIV) it is the most
common form of death from an infectious disease. However, it is still
unknown exactly how it spreads within a population. A geographic
explicit agent-based model, with humans as agents, was created and
applied to study the TB problem. Specifically the model was developed to
see what epidemiological dynamics may occur, and what could be learned
about the spreading of the disease. The model was developed in MASON and
utilizes the GeoMason GIS extension. A
Susceptible-Exposed-Infectious-Recovered (SEIR) submodel was created to
model TB progression and linked to daily human activities. The slum of
Kibera, Kenya (the largest urban slum in Africa, and an area where TB
and HIV is particularly rampant) was chosen as a test-case. Detailed
geospatial and demographic information from Kibera was used for the
instantiation of the models spatial environment and demographic
properties of the agents. Preliminary results obtained from standard
model runs show that TB epidemics progress in staircase patterns of
emergence and stabilization. Furthermore, it was found that TB was
creating hotspots, or pockets of dense disease concentration, from where
it was spreading. The results and lessons gleaned from the model can be
easily incorporated into current health policies to mitigate TB's
negative impact. Furthermore, the research shows the potential of ABMs
in investigating infectious diseases.
To give a sense of what the model looks like below we show the full model running with 250,000 agents on the slum of
Kibera, Kenya.
In the next movie we show the model running with only 50,000 agents but with a zoomed in section of the Kibera Slum. In this movie, you can see the agents going about their daily activities and how some become infected with TB.
This work would not of been possible without the work of my co-author Parth Chopra and the Thomas Jefferson High School for Science and Technology Mentorship Program.
In a move to understand slums, we have switch gears slightly from agent-based modeling to a more statistical study of slums. To this end we have just received word that our paper entitled "Measuring Slum Severity in Mumbai and Kolkata: A Household-based Approach" has just been Habitat International. Specifically we propose a new household level enumeration of slums and develop a Slum Severity Index as shown Table 4 below. Moreover, the paper estimates number of slum households in Mumbai and Kolkata as shown in Figure 1 (below).
Furthermore, the paper demonstrates that varying slum definitions result into stark differences in slum population estimates. Paper abstract:
"Slums pose a significant challenge for urban planning and policy as they provide shelter to a third of urban residents. UN-Habitat reports that, in 2001, approximately 924 million people lived in slums or informal settlements across the world (UN-Habitat, 2003). However, varying definitions of what constitutes a slum result in different slum population estimates. Most definitions treat a slum as a community of several households, rarely recognizing that housing conditions differ for each individual household within the area. Moreover, definitions of slums usually take a dichotomous approach whereby a place is either a slum or not. Little attempt is made to go beyond this slum/non-slum dichotomy. This paper moves beyond the traditional ways of defining a slum by proposing a new household level enumeration of slums and developing Slum Severity Index (SSI), which measures the level of deprivation on a continuous scale based on the UN-Habitat's slum definition. We apply this new approach of analyzing slums to a household survey dataset to estimate the total number of slum households in Mumbai and Kolkata, two megacities in India. To contrast our approach, we compare these estimates with the Census of India's. The comparison highlights stark differences in the two estimates and the slum/non-slum household classifications. The estimates by the Census are considerably smaller than those based on the UN-Habitat definition in both cities. By applying the SSI, we also demonstrate intra-urban variability in housing conditions within our study cities. The analysis highlights differences in slum profiles measured in terms of both housing deprivation levels and housing deprivation types in both cities. The main objective of this study is to demonstrate the usefulness of the household level analysis of slums in drawing implications for designing and implementing slum policies."
Keywords: Slums, Definition, Deprivation, India, Housing.
Full Reference:
Patel, A., Koizumi, N. and Crooks, A.T. (2014), Measuring Slum Severity in Mumbai and Kolkata: A Household-based Approach, Habitat International, 41: 300-306. (pdf)
If you would like to read the paper and don't have access to Habitat International, feel free to send us an email and we can send you an early version of the paper.
As some readers might know, I been doing some work with Amit Patel and Naoru Koizumi on slums. To show some of this work we have organised a photo exhibation. If you are in the DC area feel free to pass by and say hello.
Not in Focus: Stories from Ahmedabad Slums
School of Public Policy, Krasnow Institute for Advanced Study and Arts Management Program at George Mason University are pleased to invite you to the Founders Art Gallery on:
Featuring:
A photo exhibition that takes you inside the slums of Ahmedabad, India to experience challenges and indigenous solutions of citizens who are rarely in focus in the development process. (Exhibition will remain open through November 3, 2013). The photographs featured result from a research grant funded by NSF on slum formation in Ahmedabad, India, collaboratively carried out by George Mason University, CEPT University (India) and SAATH (India).
Screening of: The Fourth World: A widely acclaimed documentary by Mark Volkers on individuals from slums around the world.
5:30 - 6:30 pm
Reception to follow
6:30 - 7:30 pm
This event is Free and Open to the Public. RSVP Here.
Contact Amit Patel at apatelh@gmu.edu for queries.
It seems my interest into disease models is growing. While the development of the cholera model is still underway, over the summer I have had been working with a very talented high school student looking at the outbreak, spread and containment of tuberculosis (TB). Why might you ask? TB is a global problem with 1.8 billion people having a TB Infection, 8.8 million people infected with the TB disease, and around 1.5 million annual deaths. It is the second most common form of death from an infectious disease with the majority of cases in developing countries.
So we have been developing a model that explores how TB might manifest itself, spread within an urban setting and the potential to contain the disease. We have chosen as our test case the Kibera slum within Nairobi, Kenya. Agents in this model represent the residents of the Kibera slum. They are mobile and goal-orientated, seeking to fulfill one goal before moving on to the next.
Goals are determined based on the agent’s characteristics (age, sex, etc.) as well as their needs (water, food, health etc.). The exact location they choose to go to is also affected by the distance.
When agents interact with one another, they can be infected with TB. Infection is determined upon the amount of bacilli absorbed by agents and their immune response. The transition from infection to disease for HIV positive patients is also dependent on the patient’s CD4 cell count. What you see below is a poster we presented at Krasnow Institute Retreat.
To give a sense of the dynamics of the model, the movie below shows agents moving around the slum and how their health status changes as time progresses.
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
This project focuses on the emergence of criminal activity due to the unmet human needs of those living in Rio's favelas. An agent-based model is developed to explore how human needs, environmental factors, and individual attributes impact state-level behaviors. The emergence of organized crime is observed as "common" criminals turn into gang members. The prevention of conflict requires policies that anticipate responses and avoid conflict. By "re-creating" the current environment, we have the ability to potentially predict the onset of violence where it does not yet exist or understand the source of conflict in those areas already in the midst of violence.
Selected outputs from this research:
Pint, B., Crooks, A. T., and Geller, A. (2010), An Agent-based Model of Organized Crime: Favelas and the Drug Trade. 2nd Brazilian Workshop on Social Simulation, Sao Bernardo do Campo, Brazil. (pdf)
