Depicts the City of Cape Town CBD Area with a Polygon; An Area subject to specified provisions.All spatial layers are served live from internal systems, an item's "Last Updated" or "Publish Date" refers to the Metadata only.

Abstract

The 2001 migration study was commissioned by the Department of Planning, Local Government and Housing of the Provincial Government of the Western Cape, and managed by a steering committee comprising senior academics from the Universities of Cape Town, Stellenbosch and the Western Cape together with provincial officials. The purpose of the study was to establish a reliable profile of migration into and within the Western Cape province, and to identify a method that could be employed by municipal officials for the systematic monitoring of future migration streams. Migration within the province was analysed along two dimensions: in terms of the three settlement categories: rural, small town and ‘metro’, and, spatially, in terms of migration between the five new ( i.e. 2001) District boundaries.

Geographic coverage

The survey covered the Western Cape Province

Analysis unit

Units of analysis in the survey include households and individuals

Universe

All residents of the Western Cape Province were included in the study

Kind of data

Sample survey data [ssd]

Sampling procedure

A sample survey of 2016 randomly-selected WC provincial residents was designed. First, a list of all Enumerator Areas (EAs) falling within the province (n=7203) was obtained from the Statistics South Africa (SSA) office in Cape Town. These EAs are available according to District (and Unicity). Secondly, using random selection techniques, EAs were selected in the six geographic Districts shown in the table (A.1) below. These were then listed, identified as settlement areas in terms of their urban places' former group area racial classification and rank-ordered so as to conform to the needs of the table below. Subsequently, for those entered into the table, EA maps were obtained from SSA and dwelling units shown on each map were selected on a random spatial basis. 32 dwellings in each EA were selected in this fashion. The survey research team subsequently visited each of these areas during the period July - October 2001. 1621 survey interviews were completed, in the dominant language of the neighbourhood (Afrikaans, Xhosa or English).

Mode of data collection

Face-to-face [f2f]

Research instrument

A questionnaire which had been applied in earlier migration studies was used as a point of departure to develop a questionnaire for the survey. This instrument focused on households and was designed so that the head of household (HoH) provides information about other members of the household as well as the migration history of the household (assumed to be the same as the migration history of the HoH respondent). Two separate datasets are generated by using such an instrument – one comprising household data and a second individual data. The questionnaire was designed to cover the following relevant issues: household member and socio-economicdata, housing and infrastructure, migration history of HOH, issues relating to social capital, retirement plans and potential for ‘moving on’, and HoH attitudes to migration.

Abstract

The current study was undertaken by the Sociology Department of the University of Stellenbosch and the Cape Metropolitan Council (CMC) to support spatial development within the Cape Metropolitan Area (CMA).

At the time, Cape Town faced considerable challenges affecting the outcome of its urban undertaking. These challenges centred on (1) outside migration and its impact on the local economy, delivery needs and the spatial structure of the city; and (2) internal population movements, especially those associated with the informally housed population and the more settled poor. These trends had potential outcomes which were difficult to predict accurately and carry the threat of upsetting the delicate planning models, which were being introduced. Housing delivery for the disadvantaged sectors of the CMA population was falling further behind as informal occupation of land and informal housing continued to spread and proliferate (CMC, 1997a). Housing lists were not moving, and land invasions continued to take place. This study tried to address the uncertainty around inside and outside migration in relation to settlement, and to contribute to the refinement of the CMA's spatial planning and implementation initiative.

Geographic coverage

The survey covered the Cape Metropolitan Area (CMA) within the Western Province of South Africa.

Analysis unit

Units of analysis in the survey were persons

Universe

The survey covered households in the Cape Metropolitan Area.

As part of the pre-survey qualitative research, 25 settlement areas were selected on the basis of a search for as much socio-economic and cultural diversity as possible. A ‘housing polygon’ map was made available to the research team in the selection. Distribution of these areas across the six Cape Metropolitan Area local council areas was also taken into consideration.

Kind of data

Sample survey data [ssd]

Sampling procedure

A sample survey of 1000 randomly- selected Cape Metropolitan Area residents was designed. First, a list of all Enumerator areas falling within the CMA was obtained from the Statistics South Africa office in Cape Town. Secondly, the number of Cape Metropolitan Enumerator areas falling within each magisterial district was counted and a stratified random sample of 25 settlement areas (using electronically- generated random number selection techniques) was selected. Thirdly, detailed EA maps (showing residential units, streets names as well as public and other non-residential buildings) were obtained from Statistics South Africa for each of the 25 selected areas and 40 dwelling units were selected on a random spatial basis from each of these enumerator areas maps.

Mode of data collection

Face-to-face [f2f]

The City Health Sub- Districts dataset represent the boundaries of the 8 Health Sub- Districts within the City of Cape Town. The boundaries serve to inform administrative delineation.All spatial layers are served live from internal systems, an item's "Last Updated" or "Publish Date" refers to the Metadata only.

Project MAP (Measuring Access and Performance) aims to increase the coverage, quality of coverage, access, and efficiency of social marketing product and service delivery systems. SFH undertook a project MAP study in Johannesburg, Cape Town, and Durban, which are the three main intervention areas of SFHs condom social marketing program. The main goal of this study was to ascertain the coverage, quality of coverage, and access for Trust and Lovers Plus condoms among the general population and among the population residing in selected high transmission areas. The primary sampling unit for the study is sub places (SPs). Only SPs that are classified as urban or suburban were studied. Lot quality assurance sampling was employed, which allows for reasonably accurate estimates of coverage and quality of coverage for an entire supervision area. In each city, a representative samples of high transmission areas (HTAs) and non-high transmission areas (non-HTAs) were randomly selected. Maps for HTAs were acquired from researchers in the South African government. However, where a list of high transmission SPs was not available, an appropriate number of priority HTAs were purposively selected in the same way as hot zones are selected. Within the selected SPs all outlets present were targeted and an audit sheet was administered to collect data. The collected data was entered into an Excel spreadsheet before being processed in SPSS and Microsoft Access.

The nominated DCPC includes all the ocean waters and land below and air above within Barnstable County, starting from a line drawn 0.3 nautical miles seaward from Mean High Water (MHW) around Barnstable County and extending to 3 nautical miles from MHW, or the state jurisdictional boundary, whichever is farther. This area (521,552 acres total) is coincident with the planning area as defined in the Massachusetts Draft Ocean Management Plan and excludes the Cape Cod Canal and several bays, harbors and embayments as shown on the attached map. Where the bounds of the municipal corporations intersect with a neighboring town, the district boundary ends with the municipal corporation boundary. On January 21, 2010, the Cape Cod Commission will vote whether to accept the DCPC nomination for consideration.

Ocean Management Planning DCPC. The Barnstable County Commissioners nominated the Ocean Management Planning District of Critical Planning Concern (DCPC) on December 16, 2009. The Commissioners made the nomination in anticipation of the final Massachusetts Ocean Management Plan on December 31, 2009. The state created the Ocean Management Plan to coordinate and promote certain types of development within Massachusetts ocean waters

The four adjacent Outer Cape communities of Eastham, Truro, Provincetown, and Wellfleet have built an intermunicipal partnership to pursue a regional approach to shoreline management. This partnership promotes short- and long-term science-based decisions that will maximize the effectiveness and efficiency of community responses to the increased threat of coastal hazards. This map set is a product of that partnership, the Intermunicipal Shoreline Management Project, a project first initiated in 2019 with funding from CZM's Coastal Resilience Grant Program.Extreme coastal flooding events threaten low-lying areas and roadways of the Outer Cape. To assist with short-term storm preparation and mitigation planning, low-lying roadways have been identified for the 4-town planning area using methodology developed by CCS to evaluate storm tide pathways (STPs). Vulnerable areas were identified at an inundation scenario representing a total water level higher than the current storm of record for Provincetown, MA. This storm produced a total water level of 9.77 ft. NAVD88 in Provincetown Harbor on January 4th, 2018. To capture the effects of this storm as well as future, possibly larger storms, a total water level of approximately 10.5 ft NAVD88 or 16 ft MLLW (10.1-10.5 ft NAVD88 for the planning area) was used. All roads with the potential to be affected by mapped STPs at this total water level have been identified and included in this general inventory of low-lying roadways.

The data described here are from Odoulami et al. (2023): Odoulami, R.C., Wolski, P., and New, M. (2023). Attributing the driving mechanisms of the 2015-2017 drought in the Western Cape (South Africa) using self-organising maps. Environmental Research Letters. Here is the abstract:

Abstract The Southwestern Cape (SWC) region in South Africa experienced a severe rainfall deficit between 2015-2017. The resulting drought caused the City of Cape Town to almost run out of water during the summer of 2017-2018. Using the self-organising maps approach, we identify and classify the synoptic circulation states over Southern Africa known to influence the local climate in the SWC into three groups (dry, intermediate, and wet circulation types) using large ensembles of climate model simulations with anthropogenic forcing and natural forcing. We then assessed the influence of anthropogenic climate change on the likelihood of these circulation types and associated rainfall amounts over the SWC during the drought. Our findings suggest that during the drought, the frequency of dry (wet) circulation types increases (decreases) across all models under anthropogenic forcing relative to the natural forcing. While there was no clear direction in the associated rainfall change in the dry circulation types, rainfall decreased across most models in wet nodes. All models agree that anthropogenic climate change has increased the likelihood of dry circulation types (median probability ratio (PR): 0.93 to 0.96) and decreased that of wet circulation types (median PR: 1.01 and 1.12), indicating a shift towards lesser (more) wet (dry) synoptic circulation states and associated rainfall during the drought. The long-term climatology also depicts similar patterns indicating the drought may result from long-term changes in the frequency of wet circulations and their associated rainfall. This study further explains the anthropogenic influence on the dynamic (synoptic circulation states) and thermodynamic (rainfall) factors that influenced the SWC 2015-2017 drought.

The Self-Organising Maps (SOMs) datasets were generated by applying the Laboratory of Computer and Information Science at Helsinki University of Technology’s SOM package (see Kohonen et al., 1996 for more details) to CMIP6 and ERA-Interim daily geopotential height data. The associated characteristics of the SOMs (node frequency and rainfall amount) were also generated. All data are in netCDF format.

The dataset contains three folders: (i) "1-SOMsGeneratedData" This folder contains the SOMs-generated outputs for each model ensemble and ERA-Interim. (ii) "2-SOMsNodes_NodesFrequency" This folder contains the frequency of each SOMs node for each model ensemble and ERA-Interim. (iii) "3-SOMsNodes_NodesRain" This folder contains rainfall associated with each SOMs node for each model ensemble and ERA-Interim.

This dashboard organizes and summarizes beach nourishment activities along the shoreline of Eastern Cape Cod Bay and within the ISM planning area. Records were provided by the towns of Provincetown, Truro, Wellfleet and Eastham.In this dashboard the map view and charts are linked Filter data by town using the left sidebar Zoom to an area of interest on the map to update chart and table totalsClick on a chart or a table feature to see locations on the mapLast update: 6/6/2025 (Eastham), 5/13/2025 (Truro), 3/19/2024 (Provincetown), 12/31/2021 (Wellfleet).

These files contain classified topobathy lidar elevations generated from data collected by the Coastal Zone Mapping and Imaging Lidar (CZMIL) system. Data are classified as 1 (valid non-ground topographic data), 2 (valid ground topographic data), and 29 (valid bathymetric data). Classes 1 and 2 are defined in accordance with the American Society for Photogrammetry and Remote Sensing (ASPRS) cla...

Giving life to the stipulations in the Cape Town Zoning Scheme Chapeter 19.1 depicts areas with relaxed Parking Requirements.All spatial layers are served live from internal systems, an item's "Last Updated" or "Publish Date" refers to the Metadata only.

Abstract

The Afrobarometer project assesses attitudes and public opinion on democracy, markets, and civil society in several sub-Saharan African.This dataset was compiled from the studies in Round 3 of the Afrobarometer survey, conducted from 2005-2006 in 18 African countries (Benin, Botswana, Cape Verde, Ghana, Kenya, Lesotho, Madagascar, Malawi, Mali, Mozambique, Namibia, Nigeria, Senegal, South Africa, Tanzania, Uganda, Zambia, Zimbabwe).

Geographic coverage

The Afrobarometer surveys have national coverage

Botswana Lesotho Malawi Namibia South Africa Zambia Zimbabwe Ghana Mali Nigeria Tanzania Uganda Cape Verde Mozambique Senegal Kenya Benin Madagascar

Analysis unit

Basic units of analysis that the study investigates include: individuals and groups

Universe

The sample universe for Afrobarometer surveys includes all citizens of voting age within the country. In other words, we exclude anyone who is not a citizen and anyone who has not attained this age (usually 18 years) on the day of the survey. Also excluded are areas determined to be either inaccessible or not relevant to the study, such as those experiencing armed conflict or natural disasters, as well as national parks and game reserves. As a matter of practice, we have also excluded people living in institutionalized settings, such as students in dormitories and persons in prisons or nursing homes.

What to do about areas experiencing political unrest? On the one hand we want to include them because they are politically important. On the other hand, we want to avoid stretching out the fieldwork over many months while we wait for the situation to settle down. It was agreed at the 2002 Cape Town Planning Workshop that it is difficult to come up with a general rule that will fit all imaginable circumstances. We will therefore make judgments on a case-by-case basis on whether or not to proceed with fieldwork or to exclude or substitute areas of conflict. National Partners are requested to consult Core Partners on any major delays, exclusions or substitutions of this sort.

Kind of data

Sample survey data [ssd]

Sampling procedure

A new sample has to be drawn for each round of Afrobarometer surveys. Whereas the standard sample size for Round 3 surveys will be 1200 cases, a larger sample size will be required in societies that are extremely heterogeneous (such as South Africa and Nigeria), where the sample size will be increased to 2400. Other adaptations may be necessary within some countries to account for the varying quality of the census data or the availability of census maps.

The sample is designed as a representative cross-section of all citizens of voting age in a given country. The goal is to give every adult citizen an equal and known chance of selection for interview. We strive to reach this objective by (a) strictly applying random selection methods at every stage of sampling and by (b) applying sampling with probability proportionate to population size wherever possible. A randomly selected sample of 1200 cases allows inferences to national adult populations with a margin of sampling error of no more than plus or minus 2.5 percent with a confidence level of 95 percent. If the sample size is increased to 2400, the confidence interval shrinks to plus or minus 2 percent.

Sample Universe

The sample universe for Afrobarometer surveys includes all citizens of voting age within the country. In other words, we exclude anyone who is not a citizen and anyone who has not attained this age (usually 18 years) on the day of the survey. Also excluded are areas determined to be either inaccessible or not relevant to the study, such as those experiencing armed conflict or natural disasters, as well as national parks and game reserves. As a matter of practice, we have also excluded people living in institutionalized settings, such as students in dormitories and persons in prisons or nursing homes.

What to do about areas experiencing political unrest? On the one hand we want to include them because they are politically important. On the other hand, we want to avoid stretching out the fieldwork over many months while we wait for the situation to settle down. It was agreed at the 2002 Cape Town Planning Workshop that it is difficult to come up with a general rule that will fit all imaginable circumstances. We will therefore make judgments on a case-by-case basis on whether or not to proceed with fieldwork or to exclude or substitute areas of conflict. National Partners are requested to consult Core Partners on any major delays, exclusions or substitutions of this sort.

Sample Design

The sample design is a clustered, stratified, multi-stage, area probability sample.

To repeat the main sampling principle, the objective of the design is to give every sample element (i.e. adult citizen) an equal and known chance of being chosen for inclusion in the sample. We strive to reach this objective by (a) strictly applying random selection methods at every stage of sampling and by (b) applying sampling with probability proportionate to population size wherever possible.

In a series of stages, geographically defined sampling units of decreasing size are selected. To ensure that the sample is representative, the probability of selection at various stages is adjusted as follows:

The sample is stratified by key social characteristics in the population such as sub-national area (e.g. region/province) and residential locality (urban or rural). The area stratification reduces the likelihood that distinctive ethnic or language groups are left out of the sample. And the urban/rural stratification is a means to make sure that these localities are represented in their correct proportions. Wherever possible, and always in the first stage of sampling, random sampling is conducted with probability proportionate to population size (PPPS). The purpose is to guarantee that larger (i.e., more populated) geographical units have a proportionally greater probability of being chosen into the sample. The sampling design has four stages

A first-stage to stratify and randomly select primary sampling units;

A second-stage to randomly select sampling start-points;

A third stage to randomly choose households;

A final-stage involving the random selection of individual respondents

We shall deal with each of these stages in turn.

STAGE ONE: Selection of Primary Sampling Units (PSUs)

The primary sampling units (PSU's) are the smallest, well-defined geographic units for which reliable population data are available. In most countries, these will be Census Enumeration Areas (or EAs). Most national census data and maps are broken down to the EA level. In the text that follows we will use the acronyms PSU and EA interchangeably because, when census data are employed, they refer to the same unit.

We strongly recommend that NIs use official national census data as the sampling frame for Afrobarometer surveys. Where recent or reliable census data are not available, NIs are asked to inform the relevant Core Partner before they substitute any other demographic data. Where the census is out of date, NIs should consult a demographer to obtain the best possible estimates of population growth rates. These should be applied to the outdated census data in order to make projections of population figures for the year of the survey. It is important to bear in mind that population growth rates vary by area (region) and (especially) between rural and urban localities. Therefore, any projected census data should include adjustments to take such variations into account.

Indeed, we urge NIs to establish collegial working relationships within professionals in the national census bureau, not only to obtain the most recent census data, projections, and maps, but to gain access to sampling expertise. NIs may even commission a census statistician to draw the sample to Afrobarometer specifications, provided that provision for this service has been made in the survey budget.

Regardless of who draws the sample, the NIs should thoroughly acquaint themselves with the strengths and weaknesses of the available census data and the availability and quality of EA maps. The country and methodology reports should cite the exact census data used, its known shortcomings, if any, and any projections made from the data. At minimum, the NI must know the size of the population and the urban/rural population divide in each region in order to specify how to distribute population and PSU's in the first stage of sampling. National investigators should obtain this written data before they attempt to stratify the sample.

Once this data is obtained, the sample population (either 1200 or 2400) should be stratified, first by area (region/province) and then by residential locality (urban or rural). In each case, the proportion of the sample in each locality in each region should be the same as its proportion in the national population as indicated by the updated census figures.

Having stratified the sample, it is then possible to determine how many PSU's should be selected for the country as a whole, for each region, and for each urban or rural locality.

The total number of PSU's to be selected for the whole country is determined by calculating the maximum degree of clustering of interviews one can accept in any PSU. Because PSUs (which are usually geographically small EAs) tend to be socially homogenous we do not want to select too many people in any one place. Thus, the Afrobarometer has established a standard of no more than 8 interviews per PSU. For a sample size of 1200, the sample must therefore contain 150 PSUs/EAs (1200 divided by 8). For a sample size of 2400, there must be 300 PSUs/EAs.

These PSUs should then be allocated

This 2008 MAP (Measuring Access and Performance) study allows programmers to assess condom availability and accessibility using pre-defined criteria for measuring coverage and the quality of that coverage. The aim is to improve the efficiency of social marketing product and service delivery systems by providing feedback to programmers. In 2006, SFH conducted a similar MAP study on condom availability in Johannesburg, Cape Town and Durban. The goal of the study was to determine coverage, quality of coverage and access to Lovers Plus and Trust condoms amongst the general population and residents in High Transmission Areas (HTAs) for HIV. Strategic adjustments were made to the condom social marketing delivery system based on these findings. A second MAP study was conducted in 2007 to measure changes in coverage and quality of coverage, using the 2006 MAP results as a baseline. A third round of MAP was completed in late 2008 with the aim of monitoring trends in condom availability since 2006. The specific objectives of the 2008 MAP study were to: 1) Assess the geographical coverage and quality of coverage of Lovers Plus and Trust condoms in Johannesburg, Durban and Cape Town in 2008; 2) Monitor trends in condom availability using the 2006 MAP results as a baseline. Measures of penetration (by outlet type) and price levels of SFH condoms were additional expected outputs. The availability of the free government condoms (Choice) and other brands were also estimated.

The purpose of the fourth round 2010 Measuring Access and Performance (MAP) survey in South Africa is to provide evidence for social marketing decision making in the areas of product and service delivery systems. Specifically, the MAP study identifies areas of poor coverage and estimates access in high risk areas. This information can be vital for the sales and marketing teams in prioritizing their efforts. The main objective for this study was to assess the geographical coverage and quality of coverage of Lovers Plus, Trust and the public sector Choice condoms in High Transmission Areas (HTAs) and 19 non-HTAs in the three main cities of South Africa. In addition, the study also estimated levels of penetration (by outlet type) and the availability of other condom brands.

Taken from sections of the report:

The aim of the survey and mapping program on V5.1 was to carry out various surveying tasks at Mawson and Casey as listed later in this report. The vessel used in Voyage 5.1 was The Polar Queen.

Voyage 5.1 left Capetown on Thursday 18th February and arrived Fremantle Friday 19th. March.

Depart Capetown Thursday 18th February Arrive Mawson Sunday 28th February Depart Mawson Tuesday 2nd March Arrive Davis Thursday 4th March Depart Davis Thursday 4th March Arrive Casey Monday 8th March Depart Casey Thursday 11th March Arrive Fremantle Thursday 18th March

The survey team was: Henk Brolsma Australian Antarctic Division - surveyor. John Hyslop Australian Antarctic Division - volunteer surveyor.

The surveying at Mawson and Casey included bringing the data representing the station infrastructure up to date. The station infrastructure data is available for download in GIS format (shapefiles) from Related URLs below. The data resulting from this survey has a Dataset_id of 15. The data is formatted according to the SCAR Feature Catalogue. For data quality information about a particular feature use the Qinfo number of the feature to search for information using the 'Search datasets and quality' tab at a Related URL below.

Matt King, Rachel Manson and Lee Palfrey assisted with survey work at Casey. They carried out GPS surveys for aerial photo control, Casey and Wilkes, tide gauge bench marks at Casey, buildings detail at Wilkes and route markers around the station. Their work is not covered in this report.

This is a orthographic image of the Haghpat Monastery in the Lori region of the country of Armenia. It is a top down, ortho-corrected, georeferenced image of the monastery that can be used to as a map when opened in spatially aware software. .Haghpat Monastery, founded in the 10th century in Armenia's Lori Province, is a prime example of Armenian religious architecture. Situated above the Debed River, the complex includes the Cathedral of Surb Nshan along with chapels, a bell tower, and a scriptorium. Recognized for its cultural significance, Haghpat was inscribed as a UNESCO World Heritage Site in 1996.The Zamani Project seeks to increase awareness and knowledge of tangible cultural heritage in Africa and internationally by creating metrically accurate digital representations of historical sites. Digital spatial data of cultural heritage sites can be used for research and education, for restoration and conservation and as a record for future generations. The Zamani Project operates as a non-profit organisation within the University of Cape Town.Thank you to the Scientific Research Center of Historical and Cultural Heritage in Armenia, particularly Khachik Harutyunyan, for their funding and collaboration during the documentation of this heritage site.Special thanks to Duncan Saville, whose long term support of the Zamani Project made this, and many other, heritage documentation projects possible.

This data archive is a collection of GIS files and FGDC metadata prepared in 1995 for the Northampton County Planning Office by the Virginia Coast Reserve LTER project at the University of Virginia with support from the Virginia Department of Environmental Quality (DEQ) and the National Science Foundation (NSF). Original data sources include: 1:100,000-scale USGS digital line graph (DLG) hydrography and transportation data; 1:6,000-scale boundary, road, and railroad data for the town of Cape Charles from VDOT; 1:190,000-scale county-wide general soil map data and 1:15,540-scale detailed soil data for the Cape Charles area digitized from printed USDA soil survey maps; a land use and vegetation cover dataset (30 m. resolution) created by the VCRLTER derived from a 1993 Landsat Thematic Mapper satellite image; 1:20,000-scale plant association maps for 10 seaside barrier and marsh islands between Hog and Smith Islands, inclusive, prepared by Cheryl McCaffrey for TNC in 1975 and published in the Virginia Journal of Science in 1990; and 1993 colonial bird nesting site data collected by The Center for Conservation Biology (with partners The Nature Conservancy, College of William and Mary, University of Virginia, USFWS, VA-DCR, and VA-DGIF). Contents: HYDROGRAPHY Based on USGS 1:100,000 Digital Line Graph (DLG) data. Files: h100k_arc_u84 (streams, shorelines, etc.) and h100k_poly_u84 (marshes, mudflats, etc.). Note that the hydrographic data has been superseded by the more recent and more detailed USGS National Hydrography Dataset, available for the entire state of Virginia at "ftp://nhdftp.usgs.gov/DataSets/Staged/States/FileGDB/HighResolution/NHDH_VA_931v210.zip" (see http://nhd.usgs.gov/data.html for more information). A static 2013 version of the NHD data that includes shapefiles extracted from the original ESRI geodatabase format data and covering just the watersheds of the Eastern Shore of VA can also be found in the VCRLTER Data Catalog (dataset VCR14223). TRANSPORTATION Based on USGS 1:100,000 Digital Line Graph (DLG) data for the full county, and 1:6,000 VDOT data for the Cape Charles township. Files: 1:100k Transportation (lines) from USGS DLG data: rtf100k_arc_u84 (roads), rrf100k_arc_u84 (railroads), and mtf100k_arc_u84 (airports and utility transmission lines). Files: 1:6000 street, boundary, and rail line data for the town of Cape Charles, 1984, prepared by Virginia Department of Highways and Transportation Information Services (Division 1221 East Broad Street, Richmond, Virginia 23219). Streets correct through December 31,1983. Georeferencing corrected in 2014 for shapefiles only, using same methodology described for VCR14218 dataset. File : town_u84_adj (town_arc_u84old is the older unadjusted data). Note that the transportation data has been superseded by more recent and more detailed data contained in dataset VCR14222 of the VCRLTER Data Catalog. The VCR14222 data contains 2013 U.S. Census Bureau TIGER/Line road and airfield data supplemented by railroad and transmission lines digitized from high resolution VGIN-VBMP 2013 aerial imagery and additionally has boat launch locations not available here. SOILS General soil map for Northampton county (1:190k), and detailed soil map for Cape Charles and Cheriton areas (1:15,540) from published the USDA Soil Conservation Service's 1989 "Soil Survey of Northampton County, Virginia" digitized at UVA by Ray Dukes Smith: soilorig_poly_u84 (uses original shorelines from source maps), soil_poly_u84 (substitutes shorelines from 1993 landcover classification data), and cc_soil_poly_u84 (Cape Charles & Cheriton detailed data, map sheets 13 and 14). Note that the soil data has been superseded by more recent and more detailed SSURGO soil data from the USDA's Natural Resources Conservation Service (NRCS), which has seamless soil data from the 1:15,540 map series in tabular and GIS formats for the full county, as well as for all counties in VA and other states. A static 2013 version of the SSURGO data that contains merged data for Accomack and Northampton Counties can be found in the VCRLTER Data Catalog (dataset VCR14220). LANDUSE/LANDCOVER VCR Landuse and Vegetation Cover, 1993, created by Guofan Shao (VCRLTER) based on 30m resolution Landsat Thematic Mapper (TM) satellite imagery taken on July 28, 1993. Cropped to include just Northampton County. Landcover is divided into 5 classifications: (1) Forest or shrub, (2) Bare Land or Sand, (3) Planted Cropland, Grassland, or Upland Marsh, (4) Open Water, and (5) Low Salt Marsh. File = nhtm93s3_poly_u84. No spatial adjustments necessary. An outline of the county showing the shorelines based on the above 1993 TM classification is included as the shapefile:outline_poly_u84; however, no spatial adjustment has been applied. Note that a similar landuse/landcover classification based on the same 1993 Landsat

Abstract

The purpose of the Ageing, Wellbeing and Development Project (Brazza2) was to investigate the impact on poverty and vulnerability within beneficiary households in Brazil and South Africa of grants, social pensions and the like. The survey aimed to help researchers interrogate the extent to which social assistance was enhancing quality of life, and whether income from old-age pensions and other social grants enhanced the material and perceived well-being of social pensioners and members of households.The study also inquired into perceptions of fortune and misfortune, to provide clues to the role of social assistance in boosting poorer households' resilience and their independence from the State.

Analysis unit

Households and individuals

Universe

South Africa: the survey covered all members of black households in the rural Eastern Cape and black and colored households in urban Western Cape.

Kind of data

Sample survey data [ssd]

Sampling procedure

South Africa: In South Africa, a company called Development Research Africa were commissioned to conduct the data collection. To conduct the sampling for this, they requested a list of EAs from Stats SA that satisfied the following criteria:

1. Predominantly black or colored EAs
2. Predominantly defined (by Stats SA) as urban (formal or informal) in the Western Cape
3. Predominantly defined (by Stats SA) as tribal or semi urban in the Eastern Cape; and
4. Did not contain institutions or farming areas (these EAs were excluded)

These CEAs were sent to DRA in several excel spreadsheets under the following headings for each magisterial district:

1. Geographical areas by population group of head of household for person weighted (African/Black or Colored)
2. Geographical areas by enumeration area type for person weighted (rural: tribal villages, urban: formal or urban: informal)
3. Geographical areas by age for person weighted (56 years and older)
4. Geographical areas for household weighted (which provided the total number of households per CEA).

These data files were collated and then merged into three separate spreadsheets reflecting the respondent categories. All CEAs containing less than eighty households were deleted to further ensure that institutions or farming areas (as well as urban areas in the Eastern Cape) would not become eligible and also to limit the possibility of selecting CEAs with no eligible respondent households. These three databases became the three sample frames used to select the sample.

All the remaining CEAs were sorted in ascending order. A PSS sampling method was used to select the sample. This means that CEAs with a larger number of households have a greater chance of being selected into the sample. The two CEAs directly below the selected EAs were included as possible substitutions. Once the EA numbers were selected the maps were sourced from Stats SA. Only then could one determine the location of these CEAs. Because of the PPS methodology, EAs from smaller magisterial districts fell short of being selected into the sample whilst larger magisterial districts had more than one EA selected. In the Western Cape, the EAs could relatively easily be found on Cape Town street maps.

Twenty clusters or EAs were selected per respondent category. The target per category was about 333 interviews. It follows that about 17 interviews (333/20=17) had to be done per CEA. The desired number of households that need to be approached in a cluster or EA was the segment size. The segment size was dependent on the percentage of households that contain at least one person aged 55 years and over and on the response rate assumed. The segment size for each of the CEAs in the sample was calculated individually. For example, if 33 persons aged 55 or older resided in the CEA with 120 households and assuming a 95% response rate, 59 households would have to be approached (17/(15/120)*0.95) in the CEA in order to obtain 17 successful interviews per CEA. One limitation to the study here was that this formula does not take into consideration the possibility of two or more persons in this age category residing in a household.

Once the maps were acquired from Stats SA, they were verified and updated by the fieldworker through identifying the EA boundaries and by entering any features or changes to the map. The number of households were then counted and divided into segments with approximately equal number of households. One calculates the number of segments by dividing the segment size (described in the previous paragraph) by the actual number of households found and recorded in the EA. Some EAs may have only one segment (if segment size > total number of households in EA) or may have as many as five or six segments. One segment is then randomly selected. All the households in a particular segment were approached and all target households identified and surveyed. Finally, within the households, the person most knowledgeable about how money is spent in the household was selected as the first respondent. Thereafter all individuals 55 years of age and over were interviewed. The fieldworkers had to make three visits per household where the respondents were not available to maximize the possibility that the interview would be completed with the selected respondent. The project manager monitored the number of completed interviews. In instances where it seemed that the overall target of 333 interviews per respondent category area was unlikely, the fieldworkers had to survey the whole EA.

The twenty randomly-selected EAs in the rural Eastern Cape were located in the former Transkei and Ciskei 'homelands' in the magisterial districts of Zwelitsha, Keiskammahoek, Engcobo, Idutywa, Kentani, Libode, Lusikisiki, Mqanduli, Ngquleni, Nqamakwe, Port St Johns, Qumbu, Cofimvaba, Tabankulu, Tsomo, Willowvale and Lady Frere. The twenty randomly-selected EAs in the Cape Town metropole targeting urban black households were located in the magisterial districts of Goodwood, Wynberg, Mitchell's Plain (which includes the sprawling township of Khayelitsha) and Kuils River. The twenty randomly selected EAs targeting urban coloured households were located in the same magisterial districts in Cape Town metropole as those targeting urban black households with the addition of Bellville.

The 2002 sample design prescribed that all households selected in the last stage, in the EA segment, had to be interviewed. As a result, a larger sample size was achieved in 2002 than the originally planned sample of 1000 interviews. A total of 1111 interviews was realised in 2002: 374 in rural black households, 324 in urban black households and 413 in urban coloured households.

Approximately 79% of households included in the 2009 survey were the same ones that participated in the earlier 2002 wave. A significantly higher proportion of rural black (94%) households than urban black (72%) and urban colored (71%) ones were traced. A household that could not be traced was replaced by another older household in the same enumerator area. An estimated 69% of the 4199 household members enumerated in 2002 were traced to 2009. In total, 1286 individuals could not be traced. In this group 18% were reportedly temporarily absent, 55% had moved away permanently, and 27% (or 346 individuals) had died. This paper is based on information supplied by a total of 1059 households in the 2009 survey: 362 rural black households, 299 urban black households, and 398 urban colored households.

Brazil: Note that some of the information on sampling for the following section was taken from a document originally written in Portuguese and translated using Google translate. The original document is available with this dataset and is titled: "Benefícios Não-Contributivos e o Combate à Pobreza de Idosos no Brasil"

The approach taken in Brazil was similar to the one taken in South Africa, as the territorial expansiveness made it difficult to obtain a nationally representative sample of with a relatively small number of households. The alternative was to seek to expand the regional coverage as far as possible within the research budget. Two large regions were selected for field research. The first was the metropolitan area of Rio de Janeiro, in which the population of Rio de Janeiro state is most heavily concentrated. This is one of the most developed states in the country. Four counties were chosen within the metropolitan area. Three neighboring counties, Duke Caxias, Nova Iguaçu and São João de Meriti, were also selected. To represent the elderly population of the poorest regions of the country, a state in the Northeast was selected. Three possibilities were considered: Bahia, Pernambuco and Ceara. These have the the largest populations in the Northeast. The state of Bahia was chosen because of its proximity to Rio de Janeiro (making it more affordable to process the data). Of the major cities of Bahia, Ilheus was chosen as it had a more rural population, which the study aimed to capture.

The sample target was defined at around a thousand households with at least one person aged 60 or over in the household. Aiming to diversifying the population surveyed, the sample was divided into four groups, each with about one fourth of the sample. Thus, the state of Rio de January was half of the sample, and the rest distributed in the three counties in the Rio de Janeiro metropolitan area. The other half was divided in two, half being in the urban, and the other rural, in the municipality of Ilheus.

To select of households within each municipality the Brazilian 2000 Census data was used. Sectors with low income and high population of elderly, maximizing the probability of finding elderly not receiving contributory benefits, were chosen. The criteria used were:

1. At least

Floor area factor is the amount of floor space an owner can develop on the land unit. The factor is expressed as a proportion of the land unit size. These floor factors are ‘special floor factors’ that are applicable in addition to their base zone because they are part of an overlay.All spatial layers are served live from internal systems, an item's "Last Updated" or "Publish Date" refers to the Metadata only.

This layer represents the areas that have been identified for heritage conservation and formally protected under either the Municipal Planning Bylaw (i.e. Heritage Protection Overlay) or the National Heritage Resources Act (i.e. Heritage Areas). This layer also identifies those areas which have been identified for investigation for such formal protection and are designated as proposed Heritage Protection Overlay or proposed Heritage Areas.As a result of identifying areas for heritage conservation, the City is also identifying those areas where heritage value is low and where development can be encouraged with a minimal negative impact on the heritage qualities of a place. In order to facilitate the ease of doing business in these areas, the City is applying for exemptions in terms of S34 and S38 of the National Heritage Resources Act. These exemptions are accommodated within the National Heritage Resources Act and will contribute to reducing administrative burden.This feature class must be read together with the Heritage Inventory Objects feature class and the Heritage Inventory feature class. Together these three feature classes make up the City's Heritage Inventory.All spatial layers are served live from internal systems, an item's "Last Updated" or "Publish Date" refers to the Metadata only.