Determinants of residential location choices by slum dwellers in Lagos megacity

Badmos, Olabisi S.; Callo-Concha, Daniel; Agbola, Babatunde; Rienow, Andreas; Badmos, Biola K.; Greve, Klaus; Jürgens, Carsten

doi:10.1016/j.cities.2019.102589

Cited by 22 publications

(44 citation statements)

References 28 publications

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“…In addition, (Takyi et al, 2020) stated where all slum areas in their analysis were located around the Central Business District. The result is also in line with (Badmos et al, 2020) that discovered a factor influenced the choice of residence for slum was markets adjacency. Zain et al, (2018) in his research indicated that engaging space for the development of the trade and services area had an impact on the growth of slums.…”

Section: Cluster and Outlier Analysissupporting

confidence: 89%

“…The expansion of an area or space interm of developing trade and services is proportional to the growth of slums which could also escalate regional microeconomic growth, where slum dwellers operate small-scale businesses (Zain et al, 2018). Slums are growing near strategic areas including business centers, trade, markets, or industries (Badmos et al, 2020;Prianto & Amalia, 2019;Zain et al, 2018). Slums also contribute to the development of a nearby business district, since this sector could provide man power to support the operations of the business district (Ray, 2017), and most of the dwellers make living close the slum (Saika & Matsuyuki, 2017).…”

Section: Cluster and Outlier Analysismentioning

confidence: 99%

“…In Lagos, Nigeria, a study of slums characteristics was performed through a census using respondents from 120 slums as samples, and the results showed that 7.7% of respondents received incomes above US $ 352.94, 13.8% were graduated of formal education and 67% were non-native, also, 5.6% of buildings were built with wood, while 91.5% of buildings were more 10 years (Adedayo & Malik, 2015). Research relating slum was also performed engaging questionnaires towards respondents, revealed that the major factors affecting slums were affordability, cultural, kinship and markets adjacency, and then followed by other factors (Badmos et al, 2020). Friesen et al (2018) also utilized population data of slum area to indicate that slum development was strongly related to demographic development of a country.…”

Section: Introductionmentioning

confidence: 99%

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Building Density Level of Urban Slum Area in Jakarta

Legarias¹,

Nurhasana²,

Irwansyah³

2020

Geos. Ind.

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Currently, the number of urban residents is increasing and some of the urban population live in slum areas. Therefore, identifying the characteristics of slum areas has become crucial. This study aimed to identify more specific slum locations in Jakarta through the pattern of building densities analysis between case studies of neighborhoods association (RT) in 15 hamlets (RW) that classified as heavy slums category. This study also attempted to determine the relation between building density levels in the slum area and Jakarta spatial detail planning. This study engaged the Cluster and Outlier Analysis (Anselin Local Moran's I) method. This study also observed socio-economic factors of citizen census data based the Dasawisma Census of Family Welfare Empowerment in 2019. The result shows that slum locations that had direct neighbors towards areas which was designated at spatial detail planning as industrial zones and ware housing areas as well as office, trade and service zones, obtained a higher level of building density compared to slum locations that secured neighbors to areas that were designated as housing zones. High economic opportunities provided attraction and affected the growth of slum locations. The results also reveal that slum areas were not a concentrated population with low income and/or low education. Applying cluster and outlier modeling of building density levels of urban slums in Jakarta based on RT cluster level could reveal more specific slum locations and could identify factors that influence the differences of building density levels. Keywords: Slums; Spatial Detail Planning; Cluster and Outlier Analysis; Geographic Information System Copyright (c) 2020 Geosfera Indonesia Journal and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

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Section: Cluster and Outlier Analysissupporting

confidence: 89%

Section: Cluster and Outlier Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Building Density Level of Urban Slum Area in Jakarta

Legarias¹,

Nurhasana²,

Irwansyah³

2020

Geos. Ind.

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“…This finding is similar to that of other studies, stating that the informal sector dominates the slum population, 72 %. The occupation includes carpentry, commerce, and fishery [13]. Besides, the alternative occupation in the cities are in the informal sector [28], the developed countries were the ones with workers in the informal sector [29].…”

Section: Origin and Occupationmentioning

confidence: 99%

Population characteristics and distribution patterns of slum areas in Palembang City: Getis ord gi* analysis

2020

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The paper aims to describe the population characteristics and the distribution patterns of slums in Palembang City. The research employs a quantitative method with 382 respondents. The data are analyzed using cross-tabulation of IBM SPSS 23 to know the population characteristics. Meanwhile, the distribution patterns of slums are analyzed by observing the sample distribution through the proportional random sampling technique. It is carried out by calculating the number of buildings of each area and noting the coordinates of each sample using GPS essentials application. The data are recorded and inserted into the sample spots on the map, which were then analyzed using the High-Low Clustering Report of Getis Ord General Gi*, to see the distribution pattern, especially the cold spot and hot spot, through ArcMap 103 program. The research found that non-migrant married Moslems dominate the population of Palembang city, with the average occupation is labor or manual worker. The slum distribution forms a low cluster pattern, meaning that it has a low value. The value is due to the government’s effort to manage the city and the development of the market sector, limiting the slum distribution. Getis Ord Gi* analysis revealed that the slum area in the city center and within a dense population is a cold spot (low cluster), while those far from the city center yet are still crowded are hot spots (high cluster).

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“…Earlier studies on the drivers of slum development were loosely based on institutional [32,33], economic [34][35][36], and locational [37,38] factors. However, recent studies have shown that socio-demo-cultural factors such as ethnicity, religion, family and community ties, etc., can influence decision to either move in, remain or leave a particular slum [39][40][41]. The implication is that there are some underlying bonds, which can be loosely put under the umbrella of social ties, as they emanate from the socio-demo-cultural characteristics of slum dwellers and, if employed, are likely to influence the development and resilience of slums.…”

Section: Introductionmentioning

confidence: 99%

Social Capital: Higher Resilience in Slums in the Lagos Metropolis

et al. 2021

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Different slums exhibit different levels of resilience against the threat of eviction. However, little is known about the role of the social capital of the slum community in this context. This study investigates the factors contributing to slum resilience in the Lagos Metropolis, Nigeria, through a social capital lens. This study first investigates land allocation in slums, then the available social capital, and subsequently how this capital influences resilience to the threat of eviction in slums. Data were collected in two slum communities, in Lagos, through in-depth interviews and focus groups discussion. This study shows that land allocation is done by the traditional heads, contrarily to the mandate of the Nigeria Land Use Act of 1978. Furthermore, there is a form of structural social capital through the presence of government registered community development associations in the slums; however, their activities, decision-making process and the perception of the residents’ towards their respective associations, differs. This led to differences in trust, social cohesion and bonding ties among residents of the slum, thereby influencing resilience to the threat of eviction in slums. Since community group associations, through the appointed executives, drive the efficient utilization of social capital in slums, this study therefore recommends their restructuring in order to support a sustainable solution to the threat of eviction in slums in Lagos.

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Determinants of residential location choices by slum dwellers in Lagos megacity

Cited by 22 publications

References 28 publications

Building Density Level of Urban Slum Area in Jakarta

Building Density Level of Urban Slum Area in Jakarta

Population characteristics and distribution patterns of slum areas in Palembang City: Getis ord gi* analysis

Social Capital: Higher Resilience in Slums in the Lagos Metropolis

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