**3. Framework and methodology**

*Habitats of the World - Biodiversity and Threats*

have to be rebuilt or repaired [15].

**2.3 Spatial heterogeneity of slums**

binary system, as follows:

extensive urbanization, waterlogging, overly saturated grounds, and blockage of the sewage and drain systems by solid wastes [23]. Indeed, slums' growing population increases waste productions which are accumulating on-site due to the absence of proper waste management. For example, in Dhaka, only half of the total wastes are collected, and no waste collection is made in slums due to access difficulties. The combination of solid wastes and human ones creates a major threat to slums—human waste could lead to sanitary disaster, and solid ones, when carried by water flow, are partially responsible for the destructions during floods as well as for the blockage of drains and sewages [24, 25]—worsening the risks faced by slum dwellers during prolonged floods. Another dramatic issue is the massive spread of diseases—long-lasting floods generated several health hazards, going from drinkable water contamination to mosquito infestation—and this risk increases in

accordance with flood duration which could last several months [26].

The impact of prolonged floods on dwellers drastically limits access to basic needs such as food, drinkable water, medicines, and cloth as well as access to sanitation, shelters, and dry places to sleep [27]. Flooding also disrupts small-scale activities like petty and artisanal trading, thus threatening slum dwellers' livelihoods. Indeed, Kanke Arachchilage [28] shows how flooding disrupts the economic activities of rickshaw pullers. As streets are converted to streams, they are unable to work. Rather than get to a safer area, slum dwellers are often in a state of forced inertia during flooding conditions in order to not displace their assets and social and livelihood networks. Nevertheless, post-disaster, more than 50% of households

Recent literature is increasingly studying the spatial variability apparent in slums and how to measure it [2, 4, 5]. For instance, in a study made in Union

in high vulnerable areas, 44% in medium and 40.5% in low vulnerable areas.

• If the housing unit does not have piped water, then slum1 = 1 (else 0).

• If there is no toilet and no sewage connection, then slum2 = 1 (else 0).

• If the building material is less durable, then slum4 = 1 (else 0).

• If the resident is not the owner, then slum5 = 1 (else 0)

• Slum index for each housing unit (Sh) = ∑ (slum1…slum5).

• If the number of persons per room is greater than 2, then slum3 = 1 (else 0).

The results of the study indicated considerable variability in the "slumness" of the neighborhoods in Accra. Rather than defining households as "slum" or "not slum," the measure adds the number of slum conditions for each housing unit in a defined area to then calculate an average score for each neighborhood. As a result, each slum is placed along a continuum. A later study on the slums of Accra highlights how vulnerability even varies spatially in a single slum settlement [30]. Indeed, Jamestown in Accra, often considered as one slum, is demonstrated as a

Territory, Chandigarh, Rao and Thakur [29] found that 15.5% of slum dwellers lived

The slum index introduced by Weeks et al. [4] presents an attempt to measure spatial variability of a slum in Accra. Each housing unit is scored, according to a

**82**

To investigate the relation between locational characteristics and living conditions in slums near water, three stages have been undertaken in the conceptual framework, namely, the creation of a Flood Proneness Index, Slum Living Conditions Index, and spatial pattern analysis (**Figure 1**). These stages are built around data from 2006 to 2016 of Korail, Dhaka, serving as case study in this work.

#### **3.1 Case study: Korail, Dhaka**

Surrounded by Banani Lake on the eastern and southern sides, Korail is the largest informal settlement in Dhaka and located in a low-lying, flood-prone area. It started to develop during the late 1980s on vacant high grounds but later expanded into more hazardous low-lying areas with houses built on the flood-prone water edges. Korail is mostly inhabited by people engaged in service jobs in the high-end area on the eastern bank of the lake. Population estimates vary enormously, from 100,000 people [15] living on approximately 90 acres to more recent newspaper reports suggesting numbers as high as 175,000 people living on 170 acres [33], which would point toward an increase of 75,000 people and 80 acres in only 4 years.

The slum's existing living conditions—stemming from its location and high population density—is further exacerbated by unmanaged waste disposal and changing climate and weather conditions. Almost every year, Korail is subjected to extreme and hazardous conditions, due to excessive rainfall, increased heat, and flooding [15, 34]. There have been at least 10 heavy floods that were recorded in Dhaka city between the years from 1954 and 2007 [35].

**Figure 1.** *Conceptual framework to assess flood proneness alongside living conditions [by authors].*

Due to the lack of tenure in Korail, both the inhabitants and the government are reluctant to improve their living conditions [15]. Basic facilities, schools, and healthcare facilities are run by NGOs. Cameron [36] highlights the crucial role that NGO primary schools play in Korail, some of which host as much as 600 kids. Other types of education centers are kindergartens that accommodate around 300 kids.

To test the methodology of this study, a statistical analysis in the form of spatial autocorrelation is performed to identify building units that are exhibiting significant correlation. Secondly, these clusters make it possible to highlight spatial variability—through a multi-distance spatial cluster analysis (see Appendix A.1)—within the slum, assess the overall influence that water has on the slum, and thus help place Korail on a spectrum. The slum is situated amid poor environmental condition, siting mostly on low-lying areas which cause waterlogging and flooding in most building units inside the slum [37]. In these aspects, Korail is similar to many other slums around the work, thus making it a suitable case study.

Due to a lack of updated credible data, the conceptual framework (**Figure 1**) has been adjusted for Korail's spatial pattern analysis (**Figure 2**). Thus, data for only two of the four physical components of the living conditions diamond, infrastructure and neighborhood and location, were available and integrated in the framework.

**85**

*Influence of Floods on Spatial Variability of Wetslums Using Geo-information Techniques...*

These data are secondary data collected from research papers [15, 35, 37] and conference [7] as well as from institutions and NGO documents [28, 34, 36, 38, 39].

A flood index can be determined by looking one or a combination of the follow-

Topographic Wetness Index (TWI): a hydrological analysis can be performed to retrieve a "Topographic Wetness Index" from Digitally Elevated Maps (DEM) to identify areas that are more susceptible to intense flooding due to the location's

Rainfall data: crucial information required for identifying areas prone to flash floods. A study of rainfall and its intensity can aid the understanding of annual or

Flood inundation: modeling inundation can predict the extent to which a floodplain is at the risk of flooding. Conventional inundation simulation involves overlaying water depths at different cross sections onto a DEM. Alternatively,

*3.2.3 Mapping flood-prone areas through the topographic wetness index in Korail,* 

Due to the limited availability of flood inundation and rainfall data for Korail, open-source elevation data was chosen as the main input for the mapping of flood impacts. The elevation data helps to examine the TWI of Korail. A high indicator is considered to be illustrative for areas which are prone to drain due to excess flows of water during flooding conditions. A hydrological analysis was performed on the basis of TWI data from DEM. Based on this analysis, Korail was mapped into

The analysis of TWI required minimal data in the form of a DEM, which was obtained from the United States Geological Survey (USGS). An ASTER geo-dataset was retrieved in DEM format (ASTGTM2\_N23E090) with the coordinates for Korail, Dhaka. The TWI was retrieved through the software TauDEM [42]. The topographic or compound wetness index is retrieved through the calculation of the ratio between slope and the specific catchment or contributing area [40]. In other words, the wetness index is a slope over area ratio. Although slope in this case is a much complex variable, it is achieved by studying the "flow direction" of steepest slopes in the elevation profile. Subsequently, the corresponding catchment area is identified at the scale of grid cells as "contributing area." Previous studies have considered TWI to be a cost-efficient method to summarize overland flash floodprone areas [41]. TWI is dependent on two variables—D-infinity flow direction and

Flow directions are assigned based on the D-infinity flow method, which primarily captures the steepest slope on a planar block-centered grid. The grid presents the elevation value taken to represent the elevation of the center of the corresponding grid cell. The flow directions are recorded as angles in radians and are calculated

inundation extents can be interpolated at cross sections [41].

*DOI: http://dx.doi.org/10.5772/intechopen.85649*

ing hydrological characteristics.

"slope" and "flow direction."

*3.2.2 Dynamic variables*

monthly patterns [40].

*Dhaka*

*3.2.1 Physical conditions of the locations*

vulnerable and non-vulnerable zones.

D-infinity contributing area.

Calculating D-Infinity Flow Direction:

**3.2 Stage 1: development of a Flood Proneness Index**

**Figure 2.**

*Spatial pattern analysis in Korail, Dhaka [by authors].*

These data are secondary data collected from research papers [15, 35, 37] and conference [7] as well as from institutions and NGO documents [28, 34, 36, 38, 39].
