Simulating informal settlement growth in Dar es Salaam, Tanzania: An agent-based housing model

Augustijn-Beckers, Ellen-Wien; Flacke, Johannes; Retsios, Bas

doi:10.1016/j.compenvurbsys.2011.01.001

Cited by 60 publications

(39 citation statements)

References 17 publications

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“…Validation was discussed through engagement with stakeholders (e.g., [56]), structural validation [55] or [54] used experiments to validation the output of the model. In some papers, no explicit mention of validation is given but it is clear that model results were compared to real world data (e.g., [23,60]), whilst others clearly do discuss the role of validation and verification but do not explicitly mention them in context of the results (e.g., [58]). Others say validation will be a future task (e.g., [61]).…”

Section: Abm For City Simulationmentioning

confidence: 99%

“Space, the Final Frontier”: How Good are Agent-Based Models at Simulating Individuals and Space in Cities?

Heppenstall

Malleson

Crooks

2016

Systems

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Cities are complex systems, comprising of many interacting parts. How we simulate and understand causality in urban systems is continually evolving. Over the last decade the agent-based modeling (ABM) paradigm has provided a new lens for understanding the effects of interactions of individuals and how through such interactions macro structures emerge, both in the social and physical environment of cities. However, such a paradigm has been hindered due to computational power and a lack of large fine scale datasets. Within the last few years we have witnessed a massive increase in computational processing power and storage, combined with the onset of Big Data. Today geographers find themselves in a data rich era. We now have access to a variety of data sources (e.g., social media, mobile phone data, etc.) that tells us how, and when, individuals are using urban spaces. These data raise several questions: can we effectively use them to understand and model cities as complex entities? How well have ABM approaches lent themselves to simulating the dynamics of urban processes? What has been, or will be, the influence of Big Data on increasing our ability to understand and simulate cities? What is the appropriate level of spatial analysis and time frame to model urban phenomena? Within this paper we discuss these questions using several examples of ABM applied to urban geography to begin a dialogue about the utility of ABM for urban modeling. The arguments that the paper raises are applicable across the wider research environment where researchers are considering using this approach.

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Section: Abm For City Simulationmentioning

confidence: 99%

“Space, the Final Frontier”: How Good are Agent-Based Models at Simulating Individuals and Space in Cities?

Heppenstall

Malleson

Crooks

2016

Systems

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“…Many researchers have studied the growth of slums (e.g., [48,[52][53][54][55][56]). Sliuzas [57] suggests that physical changes to the incremental growth of slums, as observed from H/VH-R remote sensing images, can be monitored at three distinct stages: infancy, consolidation and maturity.…”

Section: Temporal Growth Of Slumsmentioning

confidence: 99%

A Critical Review of High and Very High-Resolution Remote Sensing Approaches for Detecting and Mapping Slums: Trends, Challenges and Emerging Opportunities

et al. 2018

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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.

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“…To our knowledge, in the context of fine-scale modelling of the informal settlement growth, up to now, only two research have been conducted by Iqbal (2009) and Augustijn-Beckers, et al (2011). In both of these proposed models, the spatial pattern of informal infrastructure was fully adopted from the final state of transport informal infrastructure in the reality.…”

Section: Indirect Dynamic Of Informal Transport Infrastructure Growthmentioning

confidence: 99%

“…However, the main extension and infilling of the settlement has been done between 1967-1982(AugustijnBeckers, et al, 2011R. Sliuzas, 2004) This area was selected in our study as this neighbourhood has been studied extensively in the past by the different researchers, particularly by Iqbal (2009) and Augustijn-Beckers, et al (2011) to implement their proposed models for simulation of the informal settlement growth phenomenon.…”

Section: Case Studymentioning

confidence: 99%

Towards Spatially Explicit Agent-based Model for Simulation of Informal Transport Infrastructure Indirect Growth Dynamic in Informal Settlements

Vahidi

Yan

2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The informal construction of dwellings (housing) and the informal growth of transport infrastructure are two main dynamics of an informal settlement growth phenomenon that may influence each other over time. Some studies have been done to understand the growth mechanisms of informal settlements, but not enough attention has been paid to study the informal growth of transport infrastructure dynamic. In this study, the morphology and spatial pattern of different types of informal settlements have been reviewed and categorized to clearly define and formulate the mechanisms of informal transport infrastructure formation in the context of informal settlement. To this end, two different dynamics have been suggested in this study for modelling of the growth of informal transport infrastructure. In the first suggested dynamic, the infrastructure is extended directly as the result of consolidation of the physical signs of dwellers' movement (foot prints) on the environment over time. In the second proposed dynamic that is the main focus of this study, the informal infrastructure growth could be considered as the function of dwelling construction (housing) dynamic in the settlement. In this context, informal transport infrastructure is extended based on an indirect dynamic by consideration of the void spaces by dwellers as the accessibility corridors in the settlement during the construction process of the settlement. Finally, to study the validity of the second proposed dynamic for modelling of the indirect growth of informal transport infrastructure, a simple conceptual model was developed and tested and the results were evaluated and discussed.

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Simulating informal settlement growth in Dar es Salaam, Tanzania: An agent-based housing model

Cited by 60 publications

References 17 publications

“Space, the Final Frontier”: How Good are Agent-Based Models at Simulating Individuals and Space in Cities?

“Space, the Final Frontier”: How Good are Agent-Based Models at Simulating Individuals and Space in Cities?

A Critical Review of High and Very High-Resolution Remote Sensing Approaches for Detecting and Mapping Slums: Trends, Challenges and Emerging Opportunities

Towards Spatially Explicit Agent-based Model for Simulation of Informal Transport Infrastructure Indirect Growth Dynamic in Informal Settlements

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