Linking Synthetic Populations to Household Geolocations: A Demonstration in Namibia

Whether evaluating gridded population dataset estimates (e.g., WorldPop, LandScan) or household survey sample designs, a population census linked to residential locations are needed. Geolocated census microdata data, however, are almost never available and are thus best simulated. In this paper, we simulate a close-to-reality population of individuals nested in households geolocated to realistic building locations. Using the R simPop package and ArcGIS, multiple realizations of a geolocated synthetic population are derived from the Namibia 2011 census 20% microdata sample, Namibia census enumeration area boundaries, Namibia 2013 Demographic and Health Survey (DHS), and dozens of spatial covariates derived from publicly available datasets. Realistic household latitude-longitude coordinates are manually generated based on public satellite imagery. Simulated households are linked to latitude-longitude coordinates by identifying distinct household types with multivariate k-means analysis and modelling a probability surface for each household type using Random Forest machine learning methods. We simulate five realizations of a synthetic population in Namibia’s Oshikoto region, including demographic, socioeconomic, and outcome characteristics at the level of household, woman, and child. Comparison of variables in the synthetic population were made with 2011 census 20% sample and 2013 DHS data by primary sampling unit/enumeration area. We found that synthetic population variable distributions matched observed observations and followed expected spatial patterns. We outline a novel process to simulate a close-to-reality microdata census geolocated to realistic building locations in a low- or middle-income country setting to support spatial demographic research and survey methodological development while avoiding disclosure risk of individuals.

Linking Synthetic Populations to Household Geolocations: A Demonstration in Namibia

Whether evaluating gridded population dataset estimates (e.g. WorldPop, LandScan) or household survey sample designs, a population census linked to residential locations are needed. Geolocated census microdata data, however, are almost never available and are thus best simulated. In this paper, we simulate a close-to-reality population of individuals nested in households geolocated to realistic building locations. Using the R simPop package and ArcGIS, multiple realizations of a geolocated synthetic population are derived from the Namibia 2011 census 20% microdata sample, Namibia census enumeration area boundaries, Namibia 2013 Demographic and Health Survey (DHS), and dozens of publicly available spatial datasets. Realistic household latitude-longitude coordinates are manually generated based on public satellite imagery. Simulated households are linked to latitude-longitude coordinates by identifying distinct household types with multivariate kmeans analysis, and modelling a probability surface for each household type using Random Forest machine learning methods. We simulate five realizations of a synthetic population in Namibia's Oshikoto region, including demographic, socioeconomic and outcome characteristics at the level of household, woman, and child. Comparison of variables in the synthetic population were made with 2011 census 20% sample and 2013 DHS data by primary sampling unit/enumeration area. We found that synthetic population variable distributions matched observed observations and followed expected spatial patterns. We outline a novel process to simulate a close-to-reality microdata census geolocated to realistic building locations in a low- or middle-income country setting to support spatial demographic research and survey methodological development while avoiding disclosure risk of individuals.

Residential Neighbourhoods, Perceived Social Support and Subjective Wellbeing

Research on subjective wellbeing has mainly focused on personality and demographic variables. The influence of residential neighbourhoods are usually not considered. This study, investigates the influence of types of neighbourhoods and perceived social support on life satisfaction among residents in Ibadan metropolis. Using a 2-way factorial design and multistage sampling technique, five of the eleven Local Government Areas (LGAs) in the metropolis were purposively selected for the study. Ten enumeration areas were selected from each LGA using simple random technique. The number of participants in the selected enumeration areas were determined using enumeration area maps. Two hundred and twenty house-owners and renters each were then selected from the low, medium and high density areas of the LGAs using systematic technique, making a total of 1,100 participants. The selected household heads were sampled. A structured questionnaire focusing on socio-demographic profile, life satisfaction scale (r=0.74) and a multi-dimensional scale of perceived social support (r=0.87) was administered to the participants. Data were analyzed using descriptive statistics and analysis of variance at 0.05 level of significance. Three hypotheses were tested. The results reveal that social support did not have significant main influence on life satisfaction (F (1,237) =.04; p>.05) while neighbourhood types significantly influenced life satisfaction (F (1,237) = 10.79; p<.05). There were significant interaction effects of neighbourhood and social support on life satisfaction (F(1,237) = 4.15). The findings suggest that the places we live are important for improvement of our life satisfaction.

Data Integration for Assessing Census Coverage

ABSTRACTObjectiveSince the 70’s census, the Brazilian Institute of Geography and Statistics –IBGE has been conducting a post enumeration survey – PES to assess census coverage. In 2010 the survey was conducted in a sample of enumeration areas in each of the 27 states and matching was performed for data from Census and PES. One of the biggest improvements of the 2010 Brazilian Census was the incorporation of new methodologies and technologies. Use of handheld devices in the 2010 Census and PES facilitated automatic matching of PES to the Census. MethodA matching system was designed aiming to find as much as possible the enumerated units by both Census and PES – the true matches. An accurate matching process was essential as the number of matches/unmatches had an effect on the coverage rates so that the levels of false positive (false matches) was strongly controlled during matching operation and the number of false negative (missed true matches) was minimised by successive steps in the matching system. The matching system comprised three stages: automatic, assisted and reconciliation. The automatic matching step was based on the probabilistic linkage theory and a probabilistic model was developed to identify true matches of persons and housing units from census and post enumeration survey data files. Scores were computed according to agreement and disagreement probabilities of selected variables in the pairs of records. The assisted step was held for all housing units and persons classified as unmatch or possible match at the end of automatic step. The procedures included revision of possible matches and matching “unmatched” pairs. This step was run through an application developed in house. The last step was the field reconciliation. Field team double checked the data collected on the unmatched housing units and persons from both Census and PES and searched for new matches. ResultsNew true matches were found while carrying out field checks, especially in rural areas where the addressing system is not standardized. The matching system has been fully implemented immediately after the completion of data collection in each enumeration area. The performance of automatic step was impressive as Brazil is eight million squares kilometers country with huge regional differences and the automatic step was based in a single model for the whole country. Automatic matching resulted in 76% of the total of pairs, with regional differences under 10%, while assisted allowed for 20% and reconciliation 3% of the final pairs.

Small Areas in the Canadian Census

The spatial framework for which the Census of Canada provides data has evolved, through successive censuses, to meet the need of users. Because of the changing nature of the electoral districts of the early censuses, a more permanent spatial framework, namely, the county, and an equivalent area called the census division, was introduced in the 1921 and 1931 censuses. After World War II, users of census data requested a smaller spatial framework. In. 1951 data were available at the municipality level and, in 1961, all census data taken on a 100 per cent basis were available at the enumeration area level. Currently, the ability to provide census data for user-specified areas is being developed through a geocoding system.

Data Banks and Population Statistics

The major source of population statistics in Canada is the census, taken every five years. Data are published for the county or census division, for parts of certain towns, for census tracts and, finally, for enumeration areas, the smallest administrative units. The enumeration area data are available in two forms: in computer printouts and on computer tapes. For the 1971 census, plans are being made for the geographical coding of places of residence and of work. For this purpose, it is most important to have uniform, unique and standard geo-codes applied at the central statistical organization, particularly because of the intention in 1971 to have a large part of the country covered by self-enumeration. The system of geo-coding woidd be of enormous benefit if extended to other population statistics, such as registration of births, deaths and marriages, and various forms of registration by local authorities for electoral purposes. Address registers and geo-coding woidd accelerate the availability of census data for user-designated areas. Furthermore, geo-codes have a crucial role in the development of the social sciences. There is a difference between the type of matching and record linkage in which surveyors are interested and that which social scientists woidd be using. The former is anchored to geo-coded addresses for the matching of events and persons for study purposes; the latter is more concerned with the standard demographic characteristics available on people, which are not tied to geographical location. Of the latter, those with the highest discriminating power are the social insurance number and the birth number. Canada has played a leading role in the development of matching procedures using these characteristics. Present Canadian legislation, by attempting to safeguard the privacy of individuals, unfortunately restricts the development of data for purposes of social investigation.