Slumulation: An Agent-Based Modeling Approach to Slum Formations

Patel, Amit; Crooks, Andrew; Koizumi, Naoru

doi:10.18564/jasss.2045

Cited by 54 publications

(39 citation statements)

References 26 publications

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“…This growth can take different forms depending the circumstances of the slum dweller (e.g., conflict or war leading to mass land invasions [47]), the various actors involved (e.g., politicians and land developers [48]) and historical land practices (e.g., segregation [49]) among others. Slum formation and growth can be modeled following one of three processes [50]: (1) incremental growth where the land is illegally occupied, (2) overnight invasions where the land is informally but legally occupied by residents, and (3) overnight invasions where the land is occupied illegally.…”

Section: Temporal Growth Of Slumsmentioning

confidence: 99%

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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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Section: Temporal Growth Of Slumsmentioning

confidence: 99%

“…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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“…Although there are no data on the ratio of economic performance in the cities compared to the whole country, we found individual indicators-mainly from agriculture or economy-having a high correlation with slum development. These includex "Cereal Yield" (rank 19, 13, 9), "Agriculture, value added" (rank 18, 38, 50), "Imports of goods and services" (rank 20,15,32), and "Merchandise imports" (rank 15,12,14). The here-specified information can be seen in the information gain ratio lists presented in Table 4 for the specified rank for the timesteps 5, 10, and 20 years.…”

Section: Discussionmentioning

confidence: 99%

“…[44]). Furthermore, they also could help in the design of better slum development models and to refine or improve current models (e.g., Patel et al [20], Roy et al [45]). Above all, however, it is appropriate to examine the presented connections together with other disciplines, such as the social sciences, in order to better understand the causes and effects of slum growth.…”

Section: Discussionmentioning

confidence: 99%

Determining Factors for Slum Growth with Predictive Data Mining Methods

et al. 2018

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Currently, more than half of the world's population lives in cities. Out of these more than four billion people, almost one quarter live in slums or informal settlements. In order to improve living conditions and provide possible solutions for the major problems in slums (e.g., insufficient infrastructure), it is important to understand the current situation of this form of settlement and its development. There are many different models that attempt to simulate the development of slums. In this paper, we present data mining models that correlate information about the temporal development of slums with other economic, ecologic, and demographic factors in order to identify dependencies. Different learning algorithms, such as decision rules and decision trees, are used to learn descriptive models for slum development from data, and the results are evaluated with commonly used attribute evaluation methods known from data mining. The results confirm various previously made statements about slum development in a quantitative way, such as the fact that slum development is very strongly linked to the demographic development of a country. Applying the introduced classification models to the most recent data for different regions, it can be shown that the slum development in Africa is expected to be above average.

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“…Por outro lado, Patel et al (2012) abordam essa questão e afirmam que há vantagens em definir assentamentos precários no nível do domicílio, pois é mais difícil diferenciar as condições de vida dos habitantes dos assentamentos no nível de bairro.…”

Section: Dimensão E Representação Dos Assentamentosunclassified

As definições de assentamentos precários e favelas e suas implicações nos dados populacionais: abordagem da análise de conteúdo

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2015

urbe, Rev. Bras. Gest. Urbana

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ResumoO objetivo deste artigo é discutir as diferentes definições de assentamentos precários e favelas e suas implicações nos dados populacionais. A amostra de definições foi extraída de pesquisas relacionadas ao United Nations Human Settlements Programme (UN-Habitat) e ao Instituto Brasileiro de Geografia e Estatística (IBGE). A manipulação dos dados foi realizada conforme a abordagem da análise de conteúdo (AC). A quantificação, realizada com o software Iramuteq, se baseou na frequência de palavras e na análise fatorial de correspondências (AFC). As análises qualitativas e quantitativas dessas definições ressaltaram duas principais diferenças: na caracterização do objeto (área, edificação e ambas); e nos tipos de qualificação (aspectos legais, padrão construtivo, deficiência de infraestrutura, propriedade da terra, densidade populacional, referências geográficas e tipificação dos moradores). Como o número de qualificações é elevado e seu conteúdo diverso, os dados populacionais agregam informações muito diferentes, tornando sua comparação menos precisa. Em decorrência, essa imprecisão tende a se ampliar em função do crescimento da extensão territorial e do número de países analisados.

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Slumulation: An Agent-Based Modeling Approach to Slum Formations

Cited by 54 publications

References 26 publications

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

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

Determining Factors for Slum Growth with Predictive Data Mining Methods

As definições de assentamentos precários e favelas e suas implicações nos dados populacionais: abordagem da análise de conteúdo

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