Trimble E-cognition, 10.4

Summary: This dataset shows the boundaries of all Krotspot areas. In these slum areas, the team of property supervisors will screen all private rental properties to check whether they comply with the Flemish housing code. Purpose: The geographical division into different slum districts aims to make slum spots manageable and to follow up in project form with numerical tables, maps and graphs. Creation: This dataset was manually entered in ArcGIS. The Krotspot borders are completely separate from existing borders such as postal zone, sector or neighbourhood borders.

Author: J Nelson, educator, Minnesota Alliance for Geographic EducationGrade/Audience: grade 8Resource type: lessonSubject topic(s): cities, geographic thinking, gisRegion: africaStandards: Minnesota Social Studies Standards

Standard 1. People use geographic representations and geospatial technologies to acquire, process and report information within a spatial context.

Standard 2. Geographic inquiry is a process in which people ask geographic questions and gather, organize and analyze information to solve problems and plan for the future.

Standard 3. Places have physical characteristics (such as climate, topography and vegetation) and human characteristics (such as culture, population, political and economic systems).Objectives: Students will be able to:

1. Describe a slum and how it forms.
2. Describe how a slum functions and issues slum dwellers face.
3. Analyze maps and kmz files through multi-media to investigate issues within the slum and answer specific geographic questions.
4. Construct and analyze a variety of population pyramids.
5. Synthesize findings and create an action plan to expose issues of slum life. Summary: The phenomenon of rapid urbanization will be examined through a GIS analysis of the Kianda Village, which lies in the Kibera slum in Nairobi, Kenya. Kibera slum houses one million people within an area the size of New York City’s Central Park. With one third of urban dwellers living in slums, students will explore what life is like in a slum by analyzing a variety of maps, population pyramids, and Google Earth Layers.

Abstract Public open spaces are key to cities, as a place where society can create and recreate public life. Thus, we aim to investigate the production of these areas in slums through concepts such as “right to the city”, “return to the city”, accessibility, mobility, and the implications of fragmentation and segregation, in promoting the socio-integration into the city. The empirical object is the “Beira Molhada” slum in João Pessoa, Paraíba, because of its inclusion in peri-urban area and of environmental preservation, with large public open spaces. The methodology consists of literature and documentary research, mental maps and geo-referenced analyticals. The results indicate an issue poorly related with city and environmental goods under threat. However, there is great potential for socio-spatial integration of the slum with the city, by encouraging social dynamic that provides accessibility and mobility to the environmental heritage and its fruition, contributing to the establishment of an “urban society” as stated by Lefebvre.

By 2030, ensure access for all to adequate, safe and affordable housing and basic services and upgrade slums.

Urban slums are hotspots of infectious diseases like COVID-19 as was seen in the waves of 2020 and 2021. One of the primary reasons why slums are disproportionately affected is their location in inaccessible and uninhabitable zones, crowded and poorly ventilated living spaces, unsanitary conditions and common facilities (water taps, common toilets, etc.). Staying at home during pandemics is hardly an option for slum dwellers as it often means giving up work and even basic necessities. This paper aims to understand the habitat vulnerabilities of slums in the two Indian megacities of Pune and Surat which were the worst hit during both waves. The study is done at the level of wards, which is the smallest administrative boundary, taking the habitat vulnerability (congestion and access to basic services). To identify the explanatory variables which increase the vulnerability of slums to infectious diseases, literature study is done on the triggering factors which affect habitat vulnerability derived from common characteristics and definitions of slum. The aim of the research is to categorize the slums into 3 levels of risk zones and map them subsequently. This study will help in formulating a model to prioritize the allocation of sparse resources in developing countries to tackle the habitat vulnerabilities of the slum dwellers especially during health emergencies of contagious diseases like COVID-19.

Series Name: Proportion of urban population living in slums (percent)Series Code: EN_LND_SLUMRelease Version: 2021.Q2.G.03 This dataset is part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 11.1.1: Proportion of urban population living in slums, informal settlements or inadequate housingTarget 11.1: By 2030, ensure access for all to adequate, safe and affordable housing and basic services and upgrade slumsGoal 11: Make cities and human settlements inclusive, safe, resilient and sustainableFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

Urban slums are hotspots of infectious diseases like COVID-19 as was seen in the waves of 2020 and 2021. One of the primary reasons why slums are disproportionately affected is their location in inaccessible and uninhabitable zones, crowded and poorly ventilated living spaces, unsanitary conditions and common facilities (water taps, common toilets, etc.). Staying at home during pandemics is hardly an option for slum dwellers as it often means giving up work and even basic necessities. This paper aims to understand the habitat vulnerabilities of slums in the two Indian megacities of Pune and Surat which were the worst hit during both waves. The study is done at the level of wards, which is the smallest administrative boundary, taking the habitat vulnerability (congestion and access to basic services). To identify the explanatory variables which increase the vulnerability of slums to infectious diseases, literature study is done on the triggering factors which affect habitat vulnerability derived from common characteristics and definitions of slum. The aim of the research is to categorize the slums into 3 levels of risk zones and map them subsequently. This study will help in formulating a model to prioritize the allocation of sparse resources in developing countries to tackle the habitat vulnerabilities of the slum dwellers especially during health emergencies of contagious diseases like COVID-19.

Abstract

The main objective of the 2019 Chattogram for Low Income Area Gender, Inclusion, and Poverty (CITY) study is to collect primary data from male and female residents in slum and non-slum poor neighborhoods in Chattogram, the second largest city of Bangladesh, and build the evidence base about their constraints to access more and better jobs. The CITY survey was designed to shed light on poverty, economic empowerment, and livelihood in urban areas of Bangladesh as well as to identify key constraints and solutions for low-income women trying to obtain better jobs.

A broad array of information was collected on issues related to women's economic empowerment, ranging from demographic and socioeconomic characteristics to detailed work history, time use, attitudes about work, and perceptions of work. The key feature of this survey is to collect economic data directly from the main household members, generally the main couples, unlike traditional surveys which only interviewed the heads of households (who tend to be men in most cases); thus, failed to gather valuable information from the female population.

Geographic coverage

Poor areas of slum & non-slum areas of Chattogram, the second largest city of Bangladesh.

Analysis unit

Household, individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The CITY 2019 survey was designed using a two-stage sampling strategy. The major features include the following steps:

FIRST STAGE: The primary sampling units (PSUs) in the first stage were selected using a probability proportional to size (PPS) methods. Using the 2011 census sampling frame, low-income PSUs were defined as non-slum census enumeration areas (EAs) using the 2011 Bangladesh Poverty Map. Three strata were used for sampling the low-income EAs. These strata were defined based on the poverty head-count ratios. The first stratum encompasses EAs with a poverty headcount ratio less than 10%; the second stratum between 11% and 14%; and the third stratum, those exceeding 15%. Overall, 22 low-income EAs were selected in the Chattogram City Corporation (CC).

Slums were defined as informal settlements that were listed in the Bangladesh Bureau of Statistics' slum census from 2013/14. This census was used as sampling frame of the slum areas. Based on the sizes of the slums, three strata were used for sampling purposes. This time the strata were based on the size of the slums. The first stratum comprises slums of 50 to 75 households; the second 76 to 99 households; and the third, more than 100 households. Small slums with fewer than 50 households were not included in the sampling frame. Overall, 18 slums were included as a part of the survey.

SECOND STAGE: The second stage of the selection process in each of the EAs began with a listing exercise. For very large EAs, a smaller section was delineated for the listing. The second level of stratification are defined as follows:

i) Households with both working-age male and female members; ii) Households with only a working-age female; iii) Households with only a working-age male.

Households were randomly selected from each stratum with the predetermined ratio of 16:3:1. Overall, data was collected from 805 households (1289 individuals - 580 in slum and 709 in non-slum areas).

Sampling deviation

For EAs where the ratio was unable to be attained due to absence of households in certain strata, households from the first category to arrive at a final number of 20 per EA.

Mode of data collection

Computer Assisted Personal Interview [capi]

Response rate

77%

Abstract

The DPHS in Accra, Ghana was collected in May and June 2017 in slum areas across nine neighborhoods in the city. The survey focused on the impacts of a major flood event that happened in June 2015 in Accra and how the impacts related to the poverty status of households, focusing on exposure, vulnerability and capacity to recover.

This project was a collaborative effort between Global Facility for Disaster Reduction and Recovery (GFDRR), the Poverty Global Practice and Urban, Disaster Risk Management, Resilience and Land Global Practice (GPURL). The Institute of Statistical, Social and Economic Research (ISSER) of the University of Accra carried out the data collection.

Geographic coverage

Slum areas in Accra, Ghana.

Analysis unit

• Household
• Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample selection stratifies the targeted slums by flood proneness and the level of poverty (Erman et al., 2018) as the following:

1. Slum areas were identified by combining the definition for informal settlement used by Accra Metropolitan Assembly (AMA) and UN Habitat (2011) and a slum index score developed by Engstrom et al. (2017). Enumeration areas (EAs) were added to the sample frame if they were defined as being in a slum area using the following definition: i) they were fully inside the areas defined as informal settlement according to AMA and UN Habitat’s definition and ii) had a slum index value higher than 0.7.

2. Enumeration areas in the sample frame were categorized as low poverty and high poverty by using a neighborhood-level poverty estimate created by Engstrom et al. (2017).

3. Enumeration areas in the sample frame were also categorized as flood-prone and not flood-prone using average elevation levels in the enumeration area. High flood risk areas are defined as below 17.5 meters (based on average elevation of areas flooded in the 2015 flood) and low risk areas as above 35 meters (the elevation level, above which there were no reported flooding during the 2015 flood).

4. Four neighborhoods in which all EAs were considered high risk and 4 neighborhoods in which all EAs were considered low-risk and one neighborhood with a mix of high and low-risk EAs were selected for the sample frame. In all selected neighborhoods, all EAs were defined as slum areas. The neighborhoods selected were Korle Lagoon Area, Jamestown, Gbegbeyise and Korle Dudor as high flood risk areas, and Abeka, Accra New Town, Mamobi, and Nima as low flood risk areas and Pig Farm, which includes both high and low flood risk areas. Neighborhoods are indicated in Figure 1 in a map of Accra. This administrative division was extracted from Engstrom et al. (2013).

5. The EAs in the selected neighborhoods were stratified into four categories: i) high flood risk and high poverty incidence; ii) low flood risk and high poverty incidence; iii) high flood risk and low poverty incidence; iv) low flood risk and low poverty incidence, of all selected neighborhoods.

6. Two-stage sampling was applied; 12 EAs per strata were selected using Probability Proportion to Size (PPS) and then 20 households per selected EA were selected using random sampling after listing. The sample size was determined using power calculations.

The shapefile of the Accra neighborhoods can be found in the folder DPHS_AccraGhana_Neighbourhoods, among the resources made available. The neighborhood shapefile can be matched with the surveyed neighborhoods in the DPHS dataset (DPHS_AccraGhana_Data) through the key variable neighbourhood_code.

Reference list: ENGSTROM, R., OFIESH, C., RAIN, D., JEWELL, H., AND WEEKS, J. (2013): “Defining neighborhood boundaries for urban health research in developing countries: A case study of Accra, Ghana”, Journal of Maps, 9(1), 36-42. ENGSTROM, R., D., PAVELESKU, T., TANAKA, A., AND WAMBILE (2017): “Monetary and non-monetary poverty in urban slums in Accra: Combining geospatial data and machine learning to study urban poverty,” Work in Progress, The World Bank. ERMAN, A., MOTTE, E., GOYAL, R., ASARE, A., TAKAMATSU, S., CHEN, X., MALGIOGLIO, S., SKINNER, A., YOSHIDA, N., AND HALLEGATTE, S. (2018): “The road to recovery: the role of poverty in the exposure, vulnerability and resilience to floods in Accra,” Policy Research Working Paper; No. 8469. World Bank, Washington, DC.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The survey questionnaire consists of 14 sections that were used to collect the survey data. See the attached questionnaire.

Cleaning operations

The following data editing was done for anonymization purpose: • Precise location data, such as GPS coordinates, were dropped • Identifying information, such as name, birth date and phone number were dropped • Furthermore, the number of reported religions was reduced from 8 to 3 categories, the number of ethnicities from 9 to 4 categories and household size exceeding seven household members was categorized as “above 7 members”. • Household member information for 7th member and above was dropped to avoid reconstruction of the household size variable.

Abstract

This report documents demographic characteristics and health conditions of Nairobi City's slum residents based on a representative sample survey of urban informal settlement residents carried out from February to June 2000. The aims of the "Nairobi Cross-sectional Slums Survey (NCSS)" were to determine the magnitude of the general and health problems facing slum residents, and to compare the demographic and health profiles of slum residents to those of residents of other urban and rural areas as depicted in the 1998 Kenya Demographic and Health Survey (KDHS). The NCSS is probably the first comprehensive survey explicitly designed to provide demographic and health indicators for sub-Saharan city slum residents.

Geographic coverage

Informal settlements in Nairobi county, Kenya: Central, Makadara, Kasarani, Embakasi, Pumwani, Westlands, Dagoretti and Kibera

Analysis unit

Individuals and Households

Universe

The survey covered all women aged 15-49 years and adolescent boys and girls aged 12-24 years resident in the househol

Sampling procedure

Based on census enumeration areas used in the 1999 Kenya National Census, a weighted cross-sectional sample was designed that is representative of households in all slum clusters of Nairobi. A two-stage stratified sample design was used. Sample points or enumeration areas (EAs) were selected at the first stage of sampling while households were selected from sampled EAs at the second stage. To generate a sampling frame, the NCSS used all the household listings for Nairobi province from the 1999 census. This listing contains the name of the division, location, sub-location, enumeration area as well as structure number, structure owner, number of dwelling units and use of structure (dwelling, business, dwelling/business). Processing of listing forms and identification of slum EAs were conducted in close collaboration with Central Bureau of Statistics (CBS) staff from both the headquarters and the different locations throughout Nairobi.

Before processing the data to generate a sampling frame, two important activities were undertaken. First, two of the EAs were selected and CBS maps were used to identify structures that were indicated and the name of the structure owner, and to assess the number of dwelling units in the structure. The objective of this exercise was to determine if field teams would be able to find selected structures and dwelling units using the CBS enumeration lists. The second activity sought to validate the completeness of the sampling frame. In this second activity, a random sample of one percent of the slum EAs were selected and a fresh listing of structures and dwelling units in each was conducted. A comparison of these structures and dwelling units with the original listing provided by the CBS showed a difference of only 0.7 percent.

Once the sampling frame was validated for completeness, a database of structures was generated from the listing forms and then expanded using the numbers of dwelling units in a given structure to create a sampling frame based on dwelling units. The frame consisted of 31 locations, with at least one slum enumeration area (EA), 48 sub-locations, 1,364 EAs, 29,895 structures, and 250,620 dwelling units.

The first stage of the sampling procedure yielded 98 EAs, while the second stage produced 5463 households. Since dwelling units were neither numbered nor was information collected on household headship during the listing exercise, a method was devised for identifying selected dwelling units within structures. After identifying the right structure (using the map, the name of the owner, the number of dwelling units, and any other physical landmarks noted on the map), fieldworkers identified the selected dwelling unit by first identifying all dwelling units and then counting from the left until they reach the selected number. A dwelling unit generally refers to one or more rooms occupied by the same household within one structure. Although this often corresponds to a room, a household may reside in more than one room. Interviewers were instructed to identify households occupying more than one room and then to count these as one dwelling unit before numbering and identifying the selected dwelling unit.

In each selected dwelling unit, a household questionnaire schedule was completed to identify household members and visitors who would be eligible for individual interviews. All female household members and visitors who slept in the house the previous night and are aged 12 to 49 years were eligible for individual female interviews while all male members and visitors aged 12 to 24 years old were eligible for male interviews. A full census of all sampled households was also carried out. In total, the NCSS administered interviews to 4564 households, 3256 women of reproductive age (15-49), and 1683 adolescent boys (Table 1.2). The 1,934 adolecent girls (whose results are compared with those for boys) comprise 316 aged 12-14 and 1,1618 aged 15-24. Details of the sample design are given in Appendix A.

• The household response rate is computed as the number of completed household interviews divided by the number of eligible households. For the NCSS, 90% of the sampled households (4856) were eligible (i.e. sampled households minus households that were vacant, destroyed, and where all members were absent).

Sampling deviation

None

Mode of data collection

Face-to-face [f2f]

Research instrument

The NCSS instruments were modified from KDHS instruments. Core sections of the 1998 KDHS were replicated without revision, but the service delivery exposure questions were modified so that questions were more relevant to the urban context. The similarity with the DHS questionnaires permitted direct comparison to national, urban, rural, and Nairobi-city results derived from the 1998 KDHS. The fact that the NCSS was carried out less than two years following the DHS ensured that findings were timely enough for useful comparison.

Three instruments were used in this survey: The first one was the household schedule, which enabled us to conduct a full household census from which all eligible respondents were identified. This instrument solicited information on background characteristics of households. The second instrument was for individual women age 12-49, and it had modules on their background and mobility, reproduction, contraception, pregnancy, ante-natal and post-natal care, child immunization and health, marriage, fertility preferences, husband's background and the woman's work and livelihood activities. Information on AIDS and other sexually transmitted infections was also sought, as was information on general and health matters.

The third instrument was the adolescent questionnaire for young women and men age 12-24. The adolescent questionnaire was designed to investigate health, livelihood, and social issues pertaining to adolescents in the slum communities.

NB: All questionnaires and modules are provided as external resources.

Cleaning operations

A total of 49 interviewers (37 women and 12 men), 3 office editors and 4 data-entry clerks were trained for two weeks, from February 17 through March 3, 2000. On the last day of training, the instruments were pre-tested and revised before finalizing them for fieldwork. Fieldwork started on March 5, 2000 and ended on June 4, 2000. Fieldworkers were sent to the field in six teams -each with at least one male interviewer, three or four female interviewers, one supervisor, and a field editor. Three trainees were retained as office editors to edit all questionnaires coming from the field before the questionnaires were sent for data entry.

Response rate

Households : 94.0%

Women (15-49) : 97.0%

Adolescents Girls (12-24): 88.1%

Adolescents Boys (12-24): 91.3%

Abstract

The first nationwide survey on the 'economic condition of slum dwellers in urban cities' was conducted by the NSSO in its 31st round (July 1976 - June 1977). The survey was restricted to (i) all the 'Class I' towns having 1971 census population one lakh or more and (ii) two 'Class II' towns, viz. Shillong and Pondicherry. The next survey on slum dwellers was carried out in the 49th round (January - June 1993), which covered rural as well as urban areas. After a gap of nearly ten years, the third survey was conducted in the 58th round (July-December 2002), covering only the urban slums.The last survey on slums, which, too, covered only urban areas, was carried out in the 65th round (July 2008 - June 2009). In the present round also, the survey is restricted to urban slums only. This survey, conducted during July 2012 to December 2012.

Slums: The word "slum" will refer to both notified slums and non-notified slums.

Notified slums: These are areas notified as slums by the concerned State governments, municipalities, corporations, local bodies or development authorities.

Non-notified slums: Also, any compact settlement with a collection of poorly built tenements, mostly of temporary nature, crowded together, usually with inadequate sanitary and drinking water facilities in unhygienic conditions, is considered a slum by the survey, provided at least 20 households live there. If such a settlement is not notified as a slum, it will be called a “non-notified slum”.

Both notified slums and non-notified slums will be covered by the survey.

Geographic coverage

The survey will cover the whole of the Indian Union. The rural areas such as (i) interior villages of Nagaland situated beyond five kilometres of the bus route and (ii) villages in Andaman and Nicobar Islands which remain inaccessible throughout the year were previously excluded from coverage. Henceforth, these areas will be covered in the survey after forming a State/UT level special stratum comprising these villages.

Analysis unit

Randomly selected urban slums based on sampling procedure.

Kind of data

Sample survey data [ssd]

Sampling procedure

The slum survey of the 69th round is a sample survey where the sampling units are urban blocks. There is no second stage of sampling. In case of each sample UFS block, any slum lying wholly or partly within the urban block is eligible for survey and has to be covered.

Sample Design

1.4.1 Outline of sample design: A stratified multi-stage design has been adopted for the 69th round survey. The first stage units (FSU) will be the census villages (Panchayat wards in case of Kerala) in the rural sector and Urban Frame Survey (UFS) blocks in the urban sector. The ultimate stage units (USU) will be households in both the sectors. In case of large FSUs, one intermediate stage of sampling will be the selection of two hamlet-groups (hgs)/ sub-blocks (sbs) from each rural/ urban FSU.

1.4.2 Sampling Frame for First Stage Units: For the rural sector, the list of 2001 census villages updated by excluding the villages urbanised and including the towns de-urbanised after 2001 census (henceforth the term 'village' would mean Panchayat wards for Kerala) will constitute the sampling frame. For the urban sector, the latest updated list of UFS blocks (2007-12) will be considered as the sampling frame.

1.4.3 Stratification: Within each district of a State/ UT, generally speaking, two basic strata will be formed: i) rural stratum comprising of all rural areas of the district and (ii) urban stratum comprising of all the urban areas of the district. However, within the urban areas of a district, if there are one or more towns with population 10 lakhs or more as per population census 2011 in a district, each of them will form a separate basic stratum and the remaining urban areas of the district will be considered as another basic stratum.

In case of rural sectors of Nagaland and Andaman & Nicobar Islands, the coverage has been extended to the entire State/UT from this round. In these two State/UTs, one separate special stratum will be formed within the State/UT consisting of all the interior and inaccessible villages which were not covered in previous rounds.

1.4.4 Sub-stratification:

Rural sector r: If 'r' be the sample size allocated for a rural stratum, the number of sub-strata formed will be 'r/2'. The villages within a district as per frame will be first arranged in ascending order of population. Then sub-strata 1 to 'r/2' will be demarcated in such a way that each sub-stratum will comprise a group of villages of the arranged frame and have more or less equal population.

Urban sector: Each stratum will be divided into 2 sub-strata as follows:

sub-stratum 1: all UFS blocks having area type 'slum area' sub-stratum 2: remaining UFS blocks

1.4.5 Total sample size (FSUs): 8000 FSUs will be surveyed for the central sample at all-India level. For the state sample, there will be 9112 FSUs for all-India. In addition to this, some more sample FSUs (in the form of sub-sample 3) will be allocated exclusively for slum schedule. State wise allocation of sample FSUs is given in Table 1, page A-16.

1.4.6 Allocation of total sample to States and UTs: The total number of sample FSUs will be allocated to the States and UTs in proportion to population as per census 2011 subject to a minimum sample allocation to each State/ UT. While doing so, the resource availability in terms of number of field investigators as well as the comparability with previous round of survey on the same subjects will be kept in view.

1.4.7 Allocation of State/ UT level sample to rural and urban sectors: State/ UT level sample size will be allocated between two sectors in proportion to population as per census 2011 with double weightage to urban sector subject to the restriction that urban sample size for bigger states like Maharashtra, Tamil Nadu etc. should not exceed the rural sample size. A minimum of 16 FSUs (minimum 8 each for rural and urban sector separately) will be allocated to each state/ UT.

1.4.8 Allocation to strata: Within each sector of a State/ UT, the respective sample size will be allocated to the different strata in proportion to the population as per census 2011 wherever the information is available, failing which information on population as per census 2001 will be used. Allocations at stratum level will be adjusted to multiples of 2 with a minimum sample size of 2.

For special stratum in Nagaland and A & N Islands, 8 FSUs will be allocated to each.

1.4.9 Allocation to sub-strata:

1.4.9.1 Rural: Allocation will be 2 for each sub-stratum in rural.

1.4.9.2 Urban: Stratum allocations will be distributed among the two sub-strata in proportion to the number of FSUs in the sub-strata. Minimum allocation for each sub-stratum will be 2. Equal number of samples will be allocated among the two sub-rounds.

 Also, an additional sample of FSUs in the form of sub-sample 3, equal to number of sample FSUs in each of the sub-samples 1 & 2 in the sub-stratum 1 only, will be allocated. 

1.4.10 Selection of FSUs:

For the rural sector, from each stratum/ sub-stratum, required number of sample villages will be selected by probability proportional to size with replacement (PPSWR), size being the population of the village as per Census 2001.

For the urban sector, UFS 2007-12 phase will be used for all towns and cities and from each stratum/sub-stratum FSUs will be selected by using Simple Random Sampling Without Replacement (SRSWOR).

Both rural and urban samples are to be drawn in the form of two independent sub-samples and equal number of samples will be allocated among the two sub rounds. For urban sub-stratum 1, additional samples will be drawn in the form of sub-sample 3 independently.

1.4.11 Selection of hamlet-groups/ sub-blocks - important steps

1.4.11.1 Proper identification of the FSU boundaries: The first task of the field investigators is to ascertain the exact boundaries of the sample FSU as per its identification particulars given in the sample list. For urban samples, the boundaries of each FSU may be identified by referring to the map corresponding to the frame code specified in the sample list.

1.4.11.2 Criterion for hamlet-group/ sub-block formation: After identification of the boundaries of the FSU, it is to be determined whether listing will be done in the whole sample FSU or not. In case the approximate present population of the selected FSU is found to be 1200 or more, it will be divided into a suitable number (say, D) of 'hamlet-groups' in the rural sector and 'sub-blocks' in the urban sector by more or less equalising the population as stated below.

approximate present population of the sample FSU no. of hg's/sb's to be formed

less than 1200 (no hamlet-groups/sub-blocks) 1
1200 to 1799 3
1800 to 2399 4
2400 to 2999 5
3000 to 3599 6

…………..and so on .

For rural areas of Himachal Pradesh, Sikkim, Uttarakhand (except four districts Dehradun (P), Nainital (P), Hardwar and Udham Singh Nagar), Poonch, Rajouri,

Urban slums are hotspots of infectious diseases like COVID-19 as was seen in the waves of 2020 and 2021. One of the primary reasons why slums are disproportionately affected is their location in inaccessible and uninhabitable zones, crowded and poorly ventilated living spaces, unsanitary conditions and common facilities (water taps, common toilets, etc.). Staying at home during pandemics is hardly an option for slum dwellers as it often means giving up work and even basic necessities. This paper aims to understand the habitat vulnerabilities of slums in the two Indian megacities of Pune and Surat which were the worst hit during both waves. The study is done at the level of wards, which is the smallest administrative boundary, taking the habitat vulnerability (congestion and access to basic services). To identify the explanatory variables which increase the vulnerability of slums to infectious diseases, literature study is done on the triggering factors which affect habitat vulnerability derived from common characteristics and definitions of slum. The aim of the research is to categorize the slums into 3 levels of risk zones and map them subsequently. This study will help in formulating a model to prioritize the allocation of sparse resources in developing countries to tackle the habitat vulnerabilities of the slum dwellers especially during health emergencies of contagious diseases like COVID-19.

GEM's Global Socio-Economic Vulnerability Maps

The Global Social Vulnerability Map (viewable here: https://maps.openquake.org/map/sv-global-human-vulnerability) is a composite index that was developed to measure characteristics or qualities of social systems that create the potential for loss or harm. Here, social vulnerability helps to explain why some countries will experience adverse impacts from earthquakes differentially where the linking of social capacities with demographic attributes suggests that communities with higher percentages of age-dependent populations, homeless, disabled, under-educated, and foreign migrants are likely to exhibit higher social vulnerability than communities lacking these characteristics. Other relevant factors that affect the social vulnerability of populations include in-migration from foreign countries, population density, an accounting of slum populations, and international tourist arrivals.

The Global Economic Vulnerability Map (viewable here: https://maps.openquake.org/map/sv-global-economic-vulnerability) is a composite index that was designed primarily to measure the potential for economic losses from earthquakes due to a country’s macroeconomic exposure. This index is also an appraisal of the ability of countries to respond to shocks to their economic systems. Relevant indicators include the density of exposed economic assets such as commercial and industrial infrastructure. Metrics used to measure the ability of a country to withstand shocks to its economic system include reliance on imports/exports, government debt, and purchasing power. The economic vulnerability category also considers the economic vitality of countries since the economic vitality of a country can be directly related to the vulnerability and resilience of its populations. The latter includes measurements of single-sector economic dependence, income inequality, and employment status.

The Recovery/Reconstruction Potential Map (viewable here: https://maps.openquake.org/map/sv-global-recovery-and-reconstruction) is closely aligned with the concept of disaster resilience. Enhancing a country’s resilience to earthquakes is to improve its capacity to anticipate threats, to reduce its overall vulnerability, and to allow its communities to recover from adverse impacts from earthquakes when they occur. The measurement of recovery and reconstruction potential includes capturing inherent conditions that allow communities within a country to absorb impacts and cope with a damaging earthquake event, such as the density of the built environment, education levels, and political participation. It also encompasses post-event processes that facilitate a population’s ability to reorganize, change, and learn in response to a damaging earthquake.

Criteria for indicator selection
To choose indicators contextually exclusive for use in each map, the starting point was an exhaustive review of the literature on earthquake social vulnerability and resilience. For a variable to be considered appropriate and selected, three equally important criteria were met:

- variables were justified based on the literature regarding its relevance to one or more of the indices.
- variables needed to be of consistent quality and freely available from sources such as the United Nations and the World Bank; and
- variables must be scalable or available at various levels of geography to promote sub-country level analyses.

This procedure resulted in a ‘wish list’ of approximately 300 variables of which 78 were available and fit for use based on the three criteria.

Process for indicator selection
For variables to be allocated to an index, a two-tiered validation procedure was utilized. For the first tier, variables were assigned to each of the respective indices based on how each variable was cited within the literature, i.e., as being part of an index of social vulnerability, economic vulnerability, or recovery/resilience. For the second tier, machine learning and a multivariate ordinal logistic regression modelling procedure was used for external validation. Here, focus was placed on the statistical association between the socio-economic vulnerability indicators and the adverse impacts from historical earthquakes on a country-by country-basis.

The Global Significant Earthquake Database provided the external validation metrics that were used as dependent variables in the statistical analysis. To include both severe and moderate earthquakes within the dependent variables, adverse impact data was collected from damaging earthquake events that conformed to at least one of five criteria: 1) caused deaths, 2) caused moderate damage (approximately 1 million USD or more), 3) had a magnitude 7.5 or greater 4) had a Modified Mercalli Intensity (MMI) X or greater, or 5) generated a tsunami. This database was chosen because it considers low magnitude earthquakes that were damaging (e.g., MW >=2.5 & MW<=5.5) and contains socio-economic data such as the total number of fatalities, injuries, houses damaged or destroyed, and dollar loss estimates in USD.

Countries not demonstrating at least a minimal earthquake risk, i.e., seismicity <0.05 PGA (Pagani et al. 2018) and <$10,000 USD in predicted average annual losses (Silva et al. 2018) were eliminated from the analyses so as not to include countries with minimal to no earthquake risk. A total study area consists of 136 countries.

Frederick Urban Renewal Authority is responsible for eliminating slum or blighted areas within the Town of Frederick and clearing such areas for development or redevelopment. If an urban renewal plan is approved for a specific area, Tax Increment Financing may be used to remove the blight.

Urban slums are hotspots of infectious diseases like COVID-19 as was seen in the waves of 2020 and 2021. One of the primary reasons why slums are disproportionately affected is their location in inaccessible and uninhabitable zones, crowded and poorly ventilated living spaces, unsanitary conditions and common facilities (water taps, common toilets, etc.). Staying at home during pandemics is hardly an option for slum dwellers as it often means giving up work and even basic necessities. This paper aims to understand the habitat vulnerabilities of slums in the two Indian megacities of Pune and Surat which were the worst hit during both waves. The study is done at the level of wards, which is the smallest administrative boundary, taking the habitat vulnerability (congestion and access to basic services). To identify the explanatory variables which increase the vulnerability of slums to infectious diseases, literature study is done on the triggering factors which affect habitat vulnerability derived from common characteristics and definitions of slum. The aim of the research is to categorize the slums into 3 levels of risk zones and map them subsequently. This study will help in formulating a model to prioritize the allocation of sparse resources in developing countries to tackle the habitat vulnerabilities of the slum dwellers especially during health emergencies of contagious diseases like COVID-19.

Abstract

African Population and Health Research Center (APHRC) had from 2005 to 2010, conducted a longitudinal survey in two formal settlements (Harambee and Jericho) and two informal (slum) settlements (Korogocho and Viwandani) in Nairobi to understand the uptake and patterns of school enrolment after the introduction of the Free Primary Education (FPE) in Kenya. The results of the study showed increased utilization of private informal schools among slum households as compared to the formal settlements.

That is, by 2010, almost two thirds of pupils in the slum settlements were enrolled in private informal schools while in Harambee and Jericho, more than three quarters of the pupils were enrolled in government primary schools with the remaining portion attending high-end private schools.

In 2012, ERP conducted a cross-sectional survey across six major urban centers to investigate, within the context of FPE, if the pattern of school enrolment observed in Korogocho and Viwandani slums could also be observed in other urban slums in Kenya. Below are some key facts from this study. Data is manly disaggregated by school type - government schools (FPE schools), and non-government schools, specifically the formal private schools and low-cost schools.

The study tried to answer four broad questions: What is the impact of free primary education (FPE) on schooling patterns among poor households in urban slums in Kenya? What are the qualitative and quantitative explanations of the observed patterns? Is there a difference in achievement measured by performance in a standardized test on literacy and numeracy administered to pupils in government schools under FPE and non-government schools?

Geographic coverage

Kenya - in six urban slums of Nairobi spread across 6 towns - Nairobi, Mombasa, Nyeri, Eldoret, Nakuru and Kisumu. In total 5854 households and 230 schools were covered.

Analysis unit

A cross-sectional survey focusing on households with individuals aged between 5 and 19, as well as schools and pupils in grades 3 and 6. Data therefore exits at household, individuals, schools and student levels.

Universe

This is a cross sectional study that was conducted in seven slum sites spread across six towns namely Nairobi, Mombasa, Kisumu, Eldoret, Nakuru and Nyeri and targetted hoseholds with individuals aged between 5 and 19 years and schools located within the study site or within a 1KM radius. For the schools to be included in the study they had to have both grade 3 and 6, which were target grades for this study.

Sampling procedure

This was a cross-sectional study involving schools and households. The study covered six purposively selected major towns (Eldoret, Kisumu, Mombasa, Nairobi, Nakuru and Nyeri) in different parts of Kenya (see Map 1) to provide case studies that could lead to a broader understanding of what is happening in urban informal settlements. The selection of a town was informed by presence of informal settlements and its administrative importance, that is, provincial headquarter or regional business hub. A three-stage cluster sampling procedure was used to select households in all towns with an exception of Nairobi. At the first stage, major informal settlement locations were identified in each of the six towns. The informal settlement sites were identified based on enumeration areas (EAs) designated as slums in the 2009 National Population and Housing Census conducted by the Kenya National Bureau of Statistics (KNBS). After identifying all slum EAs in each of the study towns, the location with the highest number of EAs designated as slum settlements was selected for the study. At the second stage of sampling, 20% of EAs within each major slum location were randomly selected. However, in Nakuru we randomly selected 67% (10) EAs while in Nyeri all available 9 EAs were included in the sample. This is because these two towns had fewer EAs and therefore the need to oversample to have a representative number of EAs. In total, 101 EAs were sampled from the major slum locations across the five towns. At the third stage, all households in the sampled EAs were listed using the 2009 census' EA maps prepared by KNBS. During the listing, 10,388 households were listed in all sampled EAs. Excluding Nairobi, 4,042 (57%) households which met the criteria of having at least one school-going child aged 5-20 years were selected for the survey. In Nairobi, 50% of all households which had at least one school-going child aged between 5 and 20 years were randomly sampled from all EAs existing in APHRC schooling data collected in 2010. A total of 3,060 households which met the criteria were selected. The need to select a large sample of households in Nairobi was to enable us link data from the current study with previous ones that covered over 6000 households in Korogocho and Viwandani. By so doing, we were able to get a representative sample of households in Nairobi to continue observing the schooling patterns longitudinally. In all, there were 7,102 eligible households in all six towns. A total of 14,084 individuals within the target age bracket living in 5,854 (82% of all eligible households) participated in the study. The remaining 18% of eligible households were not available for the interview as most of them had either left the study areas, declined the interview, or lacked credible respondents in the household at the time of the data collection visit or call back.

For the school-based survey, schools in each town were listed and classified into three groups based on their location: (i) within the selected slum location; (ii) within the catchment area of the selected slum area - about 1 km radius from the border of the study locations; and (iii) away from a selected slum. In Nairobi, schools were selected from existing APHRC data. During the listing exercise, lists of schools were also obtained from Municipality/City Education Departments in selected towns. The lists were used to counter-check the information obtained during listing. All schools located within the selected slum areas and those situated within the catchment area (1 km radius from the border of the slum) were included in the sample as long as they had a grade 6 class or intended to have one in 2012. The selection of schools within an informal settlement and those located within 1 km radius was because they were more likely to be accessed by children from the target informal settlement. Two hundred and forty-five (245) schools met the selection criteria and were included in the sample. Two hundred and thirty (230) primary schools (89 government schools, 94 formal private, and 47 low-cost schools) eventually participated in the survey. A total of 7,711 grade 3, 7,319 grade 6 pupils and 671 teachers of the same grades were reached and interviewed. All 230 head teachers (or their deputies) were interviewed on school characteristics.

Mode of data collection

Face-to-face [f2f]; Focus groups; Assessment; Filming (classroom observation).

Research instrument

Five survey questionnaires were administered at household level:

(i). An individual schooling history questionnaire was administered to individuals aged 5 - 20. The questionnaire was directly administered to individuals aged 12 - 20 and administered to a proxy for children younger than 12 years. Ideally, the proxy was the child's parent or guardian, or an adult familiar with the individual's schooling history and who usually resides in the same household. The questionnaire had two main sections: school participation for the current year (year of interview), and school participation for the five years preceding the year of interview (i.e. 2007 to 2011). The section on schooling participation on the current year collected information on the schooling status of the individual, the type, name and location of the school that the individual was attending, grade, and whether the individual had changed schools or dropped out of school in the current year. Respondents also provided information on the reasons for changing schools and dropping out of school, where applicable. The section on school participation for previous years also collected similar information. The questionnaire also collected information on the individual's year of birth and when they joined grade one.

(ii). A household schedule questionnaire was administered to the household head or the spouse. It sought information on the members of the household, their relationship to the household head, their gender, age, education and parental survivorship.

(iii). A parental/guardian perception questionnaire was administered to the household head or the parent/guardian of the child. It collected information on the parents/guardians' perceptions on Free Primary Education since its implementation, household support to school where child(ren) attends and household schooling decision.

(iv). A parental/guardian involvement questionnaire was strictly administered to a parent or guardian who usually lives in the household and who was equipped with adequate knowledge of the individual's schooling information (i.e. credible respondent). The questionnaire was completed for each individual of the targeted age bracket (5-20 years). The information on the child comprised questions on the gender of the child, parental/guardian aspirations for the child's educational attainment, and parental beliefs about the child's ability in school and their chances of achieving the aspired level.

(v). A household amenities and livelihood questionnaire was administered to the household head or the spouse or a member of the household who could give reliable information. The questionnaire collected information on duration of stay in the

Abstract

The 2018 Dhaka Low Income Area Gender, Inclusion, and Poverty (DIGNITY) survey attempts to fill in the data and knowledge gaps on women's economic empowerment in urban areas, specifically the factors that constrain women in slums and low-income neighborhoods from engaging in the labor market and supplying their labor to wage earning or self-employment. While an array of national-level datasets has collected a wide spectrum of information, they rarely comprise all of the information needed to study the drivers of Female Labor Force Participation (FLFP). This data gap is being filled by the primary data collection of the specialized DIGNITY survey; it is representative of poor urban areas and is specifically designed to address these limitations. The DIGNITY survey collected information from 1,300 urban households living in poor areas of Dhaka in 2018 on a range of issues that affect FLFP as identified through the literature. These range from household composition and demographic characteristics to socioeconomic characteristics such as detailed employment history and income (including locational data and travel details); and from technical and educational attributes to issues of time use, migration history, and attitudes and perceptions.

The DIGNITY survey was designed to shed light on poverty, economic empowerment, and livelihood in urban areas of Bangladesh. It has two main modules: the traditional household module (in which the head of household is interviewed on basic information about the household); and the individual module, in which two respondents from each household are interviewed individually. In the second module, two persons - one male and one female from each household, usually the main couple, are selected for the interview. The survey team deployed one male and one female interviewer for each household, so that the gender of the interviewers matched that of the respondents. Collecting economic data directly from a female and male household member, rather than just the head of the household (who tend to be men in most cases), was a key feature of the DIGNITY survey.

Geographic coverage

The DIGNITY survey is representative of low-income areas and slums of the Dhaka City Corporations (North and South, from here on referred to as Dhaka CCs), and an additional low-income site from the Greater Dhaka Statistical Metropolitan Area (SMA).

Analysis unit

• Household
• Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling procedure followed a two-stage stratification design. The major features include the following steps (they are discussed in more detail in a copy of the study's report and the sampling document located in "External Resources"):

FIRST STAGE: Selection of the PSUs

Low-income primary sampling units (PSUs) were defined as nonslum census enumeration areas (EAs), in which the small-sample area estimate of the poverty rate is higher than 8 percent (using the 2011 Bangladesh Poverty Map). The sampling frame for these low-income areas in the Dhaka City Corporations (CCs) and Greater Dhaka is based on the population census of 2011. For the Dhaka CCs, all low-income census EAs formed the sampling frame. In the Greater Dhaka area, the frame was formed by all low-income census EAs in specific thanas (i.e. administrative unit in Bangladesh) where World Bank project were located.

Three strata were used for sampling the low-income EAs. These strata were defined based on the poverty head-count ratios. The first stratum encompasses EAs with a poverty headcount ratio between 8 and 10 percent; the second stratum between 11 and 14 percent; and the third stratum, those exceeding 15 percent.

Slums were defined as informal settlements that were listed in the Bangladesh Bureau of Statistics' slum census from 2013/14. This census was used as sampling frame of the slum areas. Only slums in the Dhaka City Corporations are included. Again, three strata were used to sample the slums. This time the strata were based on the size of the slums. The first stratum comprises slums of 50 to 75 households; the second 76 to 99 households; and the third, 100 or more households. Small slums with fewer than 50 households were not included in the sampling frame. Very small slums were included in the low-income neighborhood selection if they are in a low-income area.

Altogether, the DIGNITY survey collected data from 67 PSUs.

SECOND STAGE: Selection of the Households

In each sampled PSU a complete listing of households was done to form the frame for the second stage of sampling: the selection of households. When the number of households in a PSU was very large, smaller sections of the neighborhood were identified, and one section was randomly selected to be listed. The listing data collected information on the demographics of the household to determine whether a household fell into one of the three categories that were used to stratify the household sample:

i) households with both working-age male and female members; ii) households with only a working-age female; iii) households with only a working-age male.

Households were selected from each stratum with the predetermined ratio of 16:3:1. In some cases there were not enough households in categories (ii) and (iii) to stick to this ratio; in this case all of the households in the category were sampled, and additional households were selected from the first category to bring the total number of households sampled in each PSU to 20.

The total sample consisted of 1,300 households (2,378 individuals).

Sampling deviation

The sampling for 1300 households was planned after the listing exercise. During the field work, about 115 households (8.8 percent) could not be interviewed due to household refusal or absence. These households were replaced with reserved households in the sample.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaires for the survey were developed by the World Bank, with assistance from the survey firm, DATA. Comments were incorporated following the pilot tests and practice session/pretest.

Cleaning operations

Collected data was entered into a computer by using the customized MS Access data input software developed by Data Analysis and Technical Assistance (DATA). Once data entry was completed, two different techniques were employed to check consistency and validity of data as follows:

1. Five (5%) percent of the filled-in questionnaire was checked against entered data to measure the transmission error or typos, and;
2. A logical consistency checking technique was employed to identify inconsistencies using SPSS and or STATA software.

Goal 11: Make cities and human settlements inclusive, safe, resilient, and sustainableHalf of humanity – 3.5 billion people – lives in cities today. By 2030, almost 60% of the world’s population will live in urban areas.828 million people live in slums today and the number keeps rising.The world’s cities occupy just 2% of the Earth’s land, but account for 60 – 80% of energy consumption and 75% of carbon emissions. Rapid urbanization is exerting pressure on fresh water supplies, sewage, the living environment, and public health. But the high density of cities can bring efficiency gains and technological innovation while reducing resource and energy consumption.Cities have the potential to either dissipate the distribution of energy or optimise their efficiency by reducing energy consumption and adopting green – energy systems. For instance, Rizhao, China has turned itself into a solar – powered city; in its central districts, 99% of households already use solar water heaters.68% of India’s total population lives in rural areas (2013-14).By 2030, India is expected to be home to 6 mega-cities with populations above 10 million. Currently 17% of India’s urban population lives in slums.This map layer is offered by Esri India, for ArcGIS Online subscribers, If you have any questions or comments, please let us know via content@esri.in.

The share of the urban population in the Philippines has continued to rise over the years. In 2022, the urban population accounted for roughly 48 percent of the entire population. In the Philippines, urbanized areas were primarily found in Metro Manila, located in the National Capital Region (NCR).

 Urban population growth in the Philippines

Urban areas in the Philippines have a high influx of people due to better infrastructure and employment opportunities available. From 2011 to 2015, the urban population growth rate was over two percent. However, from 2016 to 2020, the population growth rate decreased and has been at around 1.9 percent since the Philippine government introduced “Back to the Province” program to reduce overcrowding in Manila.

Lack of affordable housing in the urbanized areas in the Philippines

Poverty has been one of the reasons for slum dwellings in the Philippines. Despite better infrastructures in urban areas, there is also a lack of affordable housing for people living below the poverty level in urban areas. As a result, 43 percent of the urban population live in slums in the Philippines, one of the highest urban population living in slums across the Asia Pacific.