1.2. Liveability Indicators
2. Materials and Methods
- Neighbourhood 1—Shinde Vasti: Slum with no intervention.
- Neighbourhood 2—Laxmi Nagar: Slum upgraded by retrofitting.
- Neighbourhood 3—Kamela: Slum temporarily relocated to transit housing. This is considered a substitute for upgrading by relocation since no interviewees could be contacted in a relocated neighbourhood and the transit housing share similar characteristic of a relocated housing.
- Neighbourhood 4—Dattawadi: In-situ redevelopment by shifting to multi-storey housing.
2.1. Semi-Structured Interviews
- The first question elicits the interviewees’ general perception about their neighbourhood, whether they are satisfied, or whether they think it can be improved, or are dissatisfied. To minimize language barriers during the interviews, the wording chosen for this three-point scale answers were “a lot” (satisfied), “a bit” (can be improved), and “not at all” (dissatisfied).
- The second set of questions finds how they rated the performance of the 13 selected liveability indicators, with the same three-point scale. Indicators which are inherently improved during the upgrading process, like the Quality of Housing, Access to Basic Amenities, and Security of Tenure were left from the next round of questions and mapping since their performance has objectively improved through the upgrading. Of the remaining indicators, the ones which were not rated satisfactory were taken forward to check how their performance can be positively influenced by other indicators. This limits the number of causal relationships which is crucial to limit the time taken for each interview. When time is not a constraint, finding a causal relationship between all indicators would be preferred for optimal results.
- The third set of questions attempts to identify the interlinkages between the indicators, the potential of indicators to improve those that were rated either “not at all” (unsatisfactory) or “a bit” (can be improved), based on the interviewees’ perception. Depending on whether the indicators were thought to have some influence in improving the unsatisfactory indicators, they could be rated from “no influence” (influence value 0) to “very little” (influence value = 0.3), “a bit” (influence value = 0.6), and “a lot” (influence value = 1.0).
2.2. Fuzzy Cognitive Maps (FCMs)
2.2.1. Components of an FCM
- Concepts (from here on as Liveability Indicators (LI)): They represent the drivers/indicators that have influence (causation) on the system in consideration and can be represented with LI1, LI2 … LIn. They can be defined contextually and need not have a dimensional definition.
- Directed edges: Arrows with signs (+/−) depicting the relationships between concepts (causality), indicating that a concept causes another concept. A positive correlation ‘+’ between LI1 and LI2 means increasing LI1 increases LI2 and decreasing LI1 decreases LI2 while the reverse is the case for a negative correlation ‘−’.
- Weight of directed edge: While the directed edges or arrows with signs show a causal relation between two indicators, the weight (between 0 and 1) shows the degree to which one indicator causes another. The stronger the causation, negative or positive, the closer the value is to 1 and the weaker the causation, the closer the value is to 0.
- Adjacency Matrix: Mathematical representation of the FCM to analyse the centrality of a concept or indicator (conceptual centrality) and the role of each component in the network, whether it is ordinary, driver/transmitter or receiver.
2.2.2. Drawing and Aggregating FCMs
- Indicators not rated satisfactory (“not at all” or “a bit”) by the interviewees are taken forward to check how their performance can be improved by other indicators.
- To determine the interlinkages, the responses gathered from the questionnaire were transformed into respective FCMs using the software “Mental Modeler” and “FCM Expert”.
- Degree Centrality is calculated for all indicators from each interviewee using the Adjacency Matrix.
- The FCMs of interviewees from each neighbourhood are aggregated into one, resulting in four neighbourhood level FCMs, which highlight all the indicators considered relevant by the interviewees of each neighbourhood. For aggregation, the total weight of each directed edge is calculated via matrix addition to derive a new aggregated adjacency matrix. All individual FCMs are weighted equally in the aggregation. The aggregated FCMs can be normalized by dividing the matrix elements by the number of individual interviews [20,50]. This has been done in Figure 15 for better comparability across the four slums.
3.1. Neighbourhood 1: Shinde Vasti, Hadapsar: Informal Settlement with No Intervention
3.2. Neighbourhood 2: Laxmi Nagar, Yerwada. Upgrading by Retrofitting
3.3. Neighbourhood 3: Kamela, Kondhwa. Transit Housing for SRA In-Situ Multi-Storey Housing
3.4. Neighbourhood 4: Dattawadi SRA In-Situ Multi-Storey Housing
- Contextualizing indicators: As pointed out in the introduction, liveability indicators for informal urban contexts are still scarce. Building on the indicators presented in this work, on-the-ground contextualization could greatly benefit the local applicability of liability indicators. This could be achieved through conducting workshops with field experts like NGOs, CBOs and local municipality, stakeholders, and academics.
- Tripartite partnership: As our results show, community agency plays a vital role in successful rehabilitation. Enabling partnership between CBOs, NGOs and the municipality is important to vocalize resident concerns and ensure that built-environment upgradations consider the social habits of the neighbourhood.
- Integration to the formal city fabric: Strategies need to be developed for comprehensive integration of the rehabilitated neighbourhoods to the formal city fabric, safeguarding access to the various functional attributes of liveability, like proximity and access to public transport, education, healthcare.
- Mandating periodic liveability assessments: Credible before- and after rehabilitation evaluations are required to better capture the actual effect of the intervention. In particular, Post-Occupancy Liveability Evaluation (POLE) could ensure the workability of completed projects as well as gathering feedback on residents’ change in liveability.
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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