PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 12341361

Where do people live?

C Taeuber
View original source ↗Export metadata ↓

Abstract

"Population counts for small areas, as in a census, are directly affected by the rules that determine where each person is to be counted. Clear rules are needed for groups such as migratory workers, persons whose work requires long absences from the family home, homeless persons, military personnel, persons maintaining two or more homes, persons in hospitals, correctional, or other institutions, crews of sea-going vessels, and others. The rules may have a bearing on family and household statistics and statistics on socioeconomic status as well as on counts of numbers of people."

Explore related subjects

CensusesData CollectionDemographyGeographyMilitary PersonnelPopulationPopulation CharacteristicsResearchResidence CharacteristicsStatistics as TopicTransients and MigrantsArea AnalysisCensusCensus MethodsGeographic FactorsMigrantsPopulation StatisticsResearch MethodologySpatial DistributionWorldJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

C Taeuber. 1986. Where do people live?. https://pubmed.ncbi.nlm.nih.gov/12341361/

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2007

Modeling the probability distribution of positional errors incurred by residential address geocoding.

BACKGROUND: The assignment of a point-level geocode to subjects' residences is an important data assimilation component of many geographic public health studies. Often, these assignments are made by a method known as automated geocoding, which attempts to match each subject's address to an address-ranged street segment georeferenced within a streetline database and then interpolate the position of the address along that segment. Unfortunately, this process results in positional errors. Our study sought to model the probability distribution of positional errors associated with automated geocoding and E911 geocoding. RESULTS: Positional errors were determined for 1423 rural addresses in Carroll County, Iowa as the vector difference between each 100%-matched automated geocode and its true location as determined by orthophoto and parcel information. Errors were also determined for 1449 60%-matched geocodes and 2354 E911 geocodes. Huge (> 15 km) outliers occurred among the 60%-matched geocoding errors; outliers occurred for the other two types of geocoding errors also but were much smaller. E911 geocoding was more accurate (median error length = 44 m) than 100%-matched automated geocoding (median error length = 168 m). The empirical distributions of positional errors associated with 100%-matched automated geocoding and E911 geocoding exhibited a distinctive Greek-cross shape and had many other interesting features that were not capable of being fitted adequately by a single bivariate normal or t distribution. However, mixtures of t distributions with two or three components fit the errors very well. CONCLUSION: Mixtures of bivariate t distributions with few components appear to be flexible enough to fit many positional error datasets associated with geocoding, yet parsimonious enough to be feasible for nascent applications of measurement-error methodology to spatial epidemiology.

Censuses↗
PubMed2007

Advantages and challenges of using census and multiplier methods to estimate the number of female sex workers in a Chinese city.

Using census and multiplier methods to estimate the size of the population of female sex workers (FSWs) in a small city in western China, this study compared the advantages and challenges of the two methods. It was estimated that there were about 1,500 FSWs within the urban area using the census method, which was significantly lower than that estimated by the multiplier method (2,500). Each method has advantages and limitations, and could be applied to different situations. The census method is less time and resource consuming in smaller regions and has a tendency to underestimate, and therefore, the result can be viewed as a low limit. It is useful in a local setting, for example, when estimations are needed for planning HIV/AIDS prevention programmes in a single city. Using existing information or resources, multiplier method could be used to produce estimates for a large geographic area or at a national level.

Censuses↗