scholarly journals Advances in mapping population and demographic characteristics at small-area levels

2020 ◽  
Vol 49 (Supplement_1) ◽  
pp. i15-i25
Author(s):  
Daniela Fecht ◽  
Samantha Cockings ◽  
Susan Hodgson ◽  
Frédéric B Piel ◽  
David Martin ◽  
...  
Keyword(s):  
Big Data ◽  
Small Area ◽  
Census Data ◽  
Economic Status ◽  
Demographic Data ◽  
Population Data ◽  
Demographic Characteristics ◽  
Population Characteristics ◽  
Population Census ◽  
The Us

Abstract Temporally and spatially highly resolved information on population characteristics, including demographic profile (e.g. age and sex), ethnicity and socio-economic status (e.g. income, occupation, education), are essential for observational health studies at the small-area level. Time-relevant population data are critical as denominators for health statistics, analytics and epidemiology, to calculate rates or risks of disease. Demographic and socio-economic characteristics are key determinants of health and important confounders in the relationship between environmental contaminants and health. In many countries, census data have long been the source of small-area population denominators and confounder information. A strength of the traditional census model has been its careful design and high level of population coverage, allowing high-quality detailed data to be released for small areas periodically, e.g. every 10 years. The timeliness of data, however, becomes a challenge when temporally and spatially highly accurate annual (or even more frequent) data at high spatial resolution are needed, for example, for health surveillance and epidemiological studies. Additionally, the approach to collecting demographic population information is changing in the era of open and big data and may eventually evolve to using combinations of administrative and other data, supplemented by surveys. We discuss different approaches to address these challenges including (i) the US American Community Survey, a rolling sample of the US population census, (ii) the use of spatial analysis techniques to compile temporally and spatially high-resolution demographic data and (iii) the use of administrative and big data sources as proxies for demographic characteristics.

The Estimation of Population Microdata by Using Data from Small Area Statistics and Samples of Anonymised Records

1998 ◽  
Vol 30 (5) ◽  
pp. 785-816 ◽  
Author(s):  
P Williamson ◽  
M Birkin ◽  
P H Rees
Keyword(s):  
Small Area ◽  
Census Data ◽  
Population Data ◽  
Combinatorial Optimisation ◽  
Population Characteristics ◽  
Test Statistics ◽  
Enumeration District ◽  
Small Area Statistics ◽  
Using Data ◽  
Combining Information

Census data can be represented both as lists and as tabulations of household/individual attributes. List representation of Census data offers greater flexibility, as the exploration of interrelationships between population characteristics is limited only by the quality and scope of the data collected. Unfortunately, the released lists of household/individual attributes (Samples of Anonymised Records, SARs) are spatially referenced only to areas (single or merged districts) with populations of 120 000 or more, whereas released tabulations are available for units as small as single enumeration districts (Small Area Statistics, SAS). Intuitively, it should be possible to derive list-based estimates of enumeration district populations by combining information contained in the SAR and the SAS. In this paper we explore the range of solutions that could be adapted to this problem which, ultimately, is presented as a complex combinatorial optimisation problem. Various techniques of combinatorial optimisation are tested, and preliminary results from the best performing algorithm are evaluated. Through this process, the lack of suitable test statistics for the comparison of observed and expected tabulations of population data is highlighted.

scholarly journals Monitoring streets through tweets: Using user-generated geographic information to predict gentrification and displacement

2021 ◽  
pp. 239980832110253
Author(s):  
Karen Chapple ◽  
Ate Poorthuis ◽  
Matthew Zook ◽  
Eva Phillips
Keyword(s):  
Big Data ◽  
San Francisco ◽  
Census Data ◽  
San Francisco Bay Area ◽  
Demographic Data ◽  
Activity Patterns ◽  
Neighborhood Change ◽  
Housing Affordability ◽  
Bay Area ◽  
Media Data

The new availability of big data sources provides an opportunity to revisit our ability to predict neighborhood change. This article explores how data on urban activity patterns, specifically, geotagged tweets, improve the understanding of one type of neighborhood change—gentrification—by identifying dynamic connections between neighborhoods and across scales. We first develop a typology of neighborhood change and risk of gentrification from 1990 to 2015 for the San Francisco Bay Area based on conventional demographic data from the Census. Then, we use multivariate regression to analyze geotagged tweets from 2012 to 2015, finding that outsiders are significantly more likely to visit neighborhoods currently undergoing gentrification. Using the factors that best predict gentrification, we identify a subset of neighborhoods that Twitter-based activity suggests are at risk for gentrification over the short term—but are not identified by analysis with traditional census data. The findings suggest that combining Census and social media data can provide new insights on gentrification such as augmenting our ability to identify that processes of change are underway. This blended approach, using Census and big data, can help policymakers implement and target policies that preserve housing affordability and protext tenants more effectively.

PERANCANGAN APLIKASI SENSUS PENDUDUK PADA BADAN PUSAT STATISTIK KOTA MAKASSAR BERBASIS ANDROID

2021 ◽  
Vol 6 (1) ◽  
pp. 48
Author(s):  
Nurkhalik Wahdanial Asbara
Keyword(s):  
Data Collection ◽  
Census Data ◽  
Population Data ◽  
Accurate Information ◽  
Population Census ◽  
Male Population ◽  
Female Population ◽  
Technological Developments ◽  
The Government ◽  
Population Ratio

Technological developments and changes in government systems are developing rapidly. Both of these lead to efforts to carry out duties, protect functions and serve the community. This encourages the government to take various adjustment steps quickly in line with the dynamics of development that occur. One of them is through a population census. The population census is an important issue that must be handled properly. The population census in this study takes population data in an area based on the number of male population, female population, ratio, and population density. The data was taken and submitted to the Makassar City Statistics Agency. Population Census is a presentation of information that has the ability to present accurate information, and helps facilitate the search for a population census data. The population census is carried out every 5 years which is carried out by census officers to carry out data collection to each resident's house, the data collection process is carried out by conventional recording and submitting it to the central statistics agency for database entry. With this application, it is expected to provide convenience to Population census officers to perform the process of inputting population data and the data is directly stored in the database without having to return to the office to input again.

scholarly journals Demographic Losses Due To Alcohol-Related Mortality in Lithuania: Results Obtained Through Combining Death Records with the 2011 Population Census Data

2014 ◽  
Vol 53 (1) ◽  
pp. 15-23
Author(s):  
Daumantas Stumbrys ◽  
Domantas Jasilionis ◽  
Dalia Ambrozaitienė ◽  
Vlada Stankūnienė
Keyword(s):  
Marital Status ◽  
Mortality Risk ◽  
Census Data ◽  
Population Data ◽  
Mortality Data ◽  
Population Census ◽  
The Republic ◽  
The Individual ◽  
Education Groups ◽  
Related Mortality

This paper presents the results of a study on sociodemographic mortality differentials in Lithuania based on censuslinked mortality data. Population data come from the individual records of the 2011 Population and Housing Census of the Republic of Lithuania. The results of the research demonstrate that education and marital status are very strong predictors of alcohol-related mortality. Among males aged 30 and older, the alcohol-related mortality risk in non-married groups is up to 3.4 times as high as in the group of married males. The alcohol-related mortality risk in lower-education groups is up to 3.7 times as high as in the group of those with higher education. The findings of the study suggest that the elimination of educational differences would allow avoiding 55.7 %, the elimination of marital status differences – 40.2 %, the elimination of ethnic group differences – 11.1 % of alcohol-related deaths.

scholarly journals Models for Combining Aggregate-Level Administrative Data in the Absence of a Traditional Census

2015 ◽  
Vol 31 (3) ◽  
pp. 431-451 ◽  
Author(s):  
Dilek Yildiz ◽  
Peter W.F. Smith
Keyword(s):  
Administrative Data ◽  
Census Data ◽  
Geographical Area ◽  
Population Data ◽  
Population Characteristics ◽  
Data Sources ◽  
Aggregate Level ◽  
Administrative Data Source ◽  
Data Source ◽  
Population Counts

Abstract Administrative data sources are an important component of population data collection and they have been used in census data production in the Nordic countries since the 1960s. A large amount of information about the population is already collected in administrative data sources by governments. However, there are some challenges to using administrative data sources to estimate population counts by age, sex, and geographical area as well as population characteristics. The main limitation with the administrative data sources is that they only collect information from a subset of the population about specific events, and this may result in either undercoverage or overcoverage of the population. Another issue with the administrative data sources is that the information may not have the same quality for all population groups. This research aims to correct an inaccurate administrative data source by combining aggregate-level administrative data with more accurate marginal distributions or two-way marginal information from an auxiliary data source and produce accurate population estimates in the absence of a traditional census. The methodology developed is applied to estimate population counts by age, sex, and local authority area in England and Wales. The administrative data source used is the Patient Register which suffers from overcoverage, particularly for people between the ages of 20 and 50.

scholarly journals Trends in Childhood Viable Pregnancy and Risk of Stillbirth in the United States

2021 ◽  
Author(s):  
Sahra Ibrahimi ◽  
Deepa Dongarwar ◽  
Korede K. Yusuf ◽  
Sitratullah Olawunmi Maiyegun ◽  
Hamisu M. Salihu
Keyword(s):  
Proportional Hazards ◽  
Census Data ◽  
The United States ◽  
Population Based ◽  
Cox Proportional Hazards ◽  
Early Years ◽  
Teen Mothers ◽  
Population Census ◽  
Hazard Ratios ◽  
The Us

Abstract The objective of this study was to assess trends in childhood viable pregnancy over the previous three decades as well as the risk of stillbirth in these highly vulnerable child mothers. We conducted a population-based retrospective cohort study that used Birth datasets, Fetal Death datasets, and the US population census data: 1982-2017. To assess the association between various socio-demographic and maternal comorbidities and stillbirth, we generated adjusted hazard ratios (AHR) from Cox Proportional Hazards Regression models. Overall, there were declines in the stillbirth rates in both teens (15-19 years old) and child mothers aged ≤ 14 years, but the rate remained consistently higher among child mothers. Compared to teen mothers, childhood pregnancy was modestly associated with elevated risk for stillbirth. Childhood pregnancy is a risk factor for stillbirth. These findings further underscore the need for sustained efforts and policies to prevent pregnancies in the early years of reproductive development.

scholarly journals Creating Unbiased Machine Learning Models by Design

2021 ◽  
Vol 14 (11) ◽  
pp. 565
Author(s):  
Joseph L. Breeden ◽  
Eugenia Leonova
Keyword(s):  
Machine Learning ◽  
Credit Card ◽  
Census Data ◽  
Demographic Data ◽  
Machine Learning Model ◽  
The Us ◽  
Protected Class ◽  
Credit Card Data ◽  
Class Status ◽  
Us Census

Unintended bias against protected groups has become a key obstacle to the widespread adoption of machine learning methods. This work presents a modeling procedure that carefully builds models around protected class information in order to make sure that the final machine learning model is independent of protected class status, even in a nonlinear sense. This procedure works for any machine learning method. The procedure was tested on subprime credit card data combined with demographic data by zip code from the US Census. The census data serves as an imperfect proxy for borrower demographics but serves to illustrate the procedure.

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