Chart and table of Jordan population density from 1950 to 2025. United Nations projections are also included through the year 2100.

Jordan JO: Population Density: People per Square Km data was reported at 109.285 Person/sq km in 2017. This records an increase from the previous number of 106.508 Person/sq km for 2016. Jordan JO: Population Density: People per Square Km data is updated yearly, averaging 38.475 Person/sq km from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 109.285 Person/sq km in 2017 and a record low of 11.028 Person/sq km in 1961. Jordan JO: Population Density: People per Square Km data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Jordan – Table JO.World Bank.WDI: Population and Urbanization Statistics. Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.; ; Food and Agriculture Organization and World Bank population estimates.; Weighted average;

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Jordan: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).

WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset.

Datasets are available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc-seconds (approximately 1km at the equator)

-Unconstrained individual countries 2000-2020: Population density datasets for all countries of the World for each year 2000-2020 – derived from the corresponding Unconstrained individual countries 2000-2020 population count datasets by dividing the number of people in each pixel by the pixel surface area. These are produced using the unconstrained top-down modelling method.
-Unconstrained individual countries 2000-2020 UN adjusted: Population density datasets for all countries of the World for each year 2000-2020 – derived from the corresponding Unconstrained individual countries 2000-2020 population UN adjusted count datasets by dividing the number of people in each pixel, adjusted to match the country total from the official United Nations population estimates (UN 2019), by the pixel surface area. These are produced using the unconstrained top-down modelling method.

Data for earlier dates is available directly from WorldPop.

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00674

126.8 (people per sq. km) in 2022. Population density is midyear population divided by land area in square kilometers.

544 (Persons) in 2016.

93 (Persons) in 2016.

Abstract

The Syrian crisis has caused one of the largest episodes of forced displacement since World War II and some of the densest refugee-hosting situations in modern history. Syria's immediate neighbors host the bulk of Syrian refugees. The host countries were dealing with impact of inflow of refugees as well as consequences of the Syrian conflict such as disruption on trade and economic activity and growth and spread of the Islamic State. This survey was designed to generate comparable findings on the lives and livelihoods of Syrian refugees and host communities in Jordan, Lebanon and Kurdistan, Iraq.

The goals of the survey originally were: - to assess the socio-economic and living conditions of a representative sample of the Syrian refugee and host community population. - to understand the implications in terms of social and economic conditions on the host communities. - to identify strategies to support Syrian refugees and host communities in the immediate and longer term.

Geographic coverage

Syrian refugee and host community in Jordan

Analysis unit

Refugee household and individual

Kind of data

Sample survey data [ssd]

Sampling procedure

Jordan has carried out Population and Housing Censuses on regular intervals, with the last one in late 2015. What was particularly attractive about the latest census from the perspective of sampling was that it explicitly asked about the nationality of all residents. This would have allowed stratification of areas by density of Syrians. However, the original design could not be implemented because we could not access the new sample frame based on the 2015 Jordanian census. The design was then amended to include a representative sample of the Azraq and Za'atari camps (which account for the vast majority of Syrian refugees in camps in Jordan). This sample was complemented by purposive samples of the surrounding governorates, Mafraq and Zarqa, where the sample included areas physically proximate to the camp and other areas with a high number of Syrian refugees. In Amman Governorate, a purposive sample was drawn, combining a geographically distributed sample with a sample of areas with a high prevalence of Syrian refugees per the 2015 census, as indicated by the Jordanian Department of Statistics. Analytically, this implies the insights from Jordan will be limited to camp residents, neighboring areas of the camps, and Amman governorate. For this reason, Amman is left out of the rest of the discussion, where our focus is on relating the innovative approaches that we followed to obtain near-representative sample in absence of recent sampling frame.

Note: A more detailed description of the sample design is presented in Section 2 of "Survey Design and Sampling: A methodology note for the 2015-16 surveys of Syrian refugees and host communities in Jordan, Lebanon and Kurdistan, Iraq" document.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey instrument was administered across Lebanon, Jordan, and KRI, with slight modifications depending on the structure of refugee living conditions. The survey includes detailed questions on demographics, employment, access to public services, health, migration, and perceptions.

295 (Persons) in 2016.

The full estimation results data in a spreadsheet csv file for the detailed results and power values. (CSV)

Jordan administrative division with aggregated population. Built from Kontur Population: Global Population Density for 400m H3 Hexagons on top of OpenStreetMap administrative boundaries data. Enriched with HASC codes for regions taken from Wikidata.
Global version of boundaries dataset: Kontur Boundaries: Global administrative division with aggregated population

431 (Persons) in 2016.

Abstract

In order to develop various methods of comparable data collection on health and health system responsiveness WHO started a scientific survey study in 2000-2001. This study has used a common survey instrument in nationally representative populations with modular structure for assessing health of indviduals in various domains, health system responsiveness, household health care expenditures, and additional modules in other areas such as adult mortality and health state valuations.

The health module of the survey instrument was based on selected domains of the International Classification of Functioning, Disability and Health (ICF) and was developed after a rigorous scientific review of various existing assessment instruments. The responsiveness module has been the result of ongoing work over the last 2 years that has involved international consultations with experts and key informants and has been informed by the scientific literature and pilot studies.

Questions on household expenditure and proportionate expenditure on health have been borrowed from existing surveys. The survey instrument has been developed in multiple languages using cognitive interviews and cultural applicability tests, stringent psychometric tests for reliability (i.e. test-retest reliability to demonstrate the stability of application) and most importantly, utilizing novel psychometric techniques for cross-population comparability.

The study was carried out in 61 countries completing 71 surveys because two different modes were intentionally used for comparison purposes in 10 countries. Surveys were conducted in different modes of in- person household 90 minute interviews in 14 countries; brief face-to-face interviews in 27 countries and computerized telephone interviews in 2 countries; and postal surveys in 28 countries. All samples were selected from nationally representative sampling frames with a known probability so as to make estimates based on general population parameters.

The survey study tested novel techniques to control the reporting bias between different groups of people in different cultures or demographic groups ( i.e. differential item functioning) so as to produce comparable estimates across cultures and groups. To achieve comparability, the selfreports of individuals of their own health were calibrated against well-known performance tests (i.e. self-report vision was measured against standard Snellen's visual acuity test) or against short descriptions in vignettes that marked known anchor points of difficulty (e.g. people with different levels of mobility such as a paraplegic person or an athlete who runs 4 km each day) so as to adjust the responses for comparability . The same method was also used for self-reports of individuals assessing responsiveness of their health systems where vignettes on different responsiveness domains describing different levels of responsiveness were used to calibrate the individual responses.

This data are useful in their own right to standardize indicators for different domains of health (such as cognition, mobility, self care, affect, usual activities, pain, social participation, etc.) but also provide a better measurement basis for assessing health of the populations in a comparable manner. The data from the surveys can be fed into composite measures such as "Healthy Life Expectancy" and improve the empirical data input for health information systems in different regions of the world. Data from the surveys were also useful to improve the measurement of the responsiveness of different health systems to the legitimate expectations of the population.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was a multi-stage random probability sample representative of the population residing in urban and rural areas of Jordan. An advanced sample design method in 2 stages was used: 1. Jordan is administratively divided into 12 Governorates, each of which is subdivided into four regions. The survey was carried out in all four regions. 2. Selection of households within the Primary Sampling areas.

The sample structure was based on the estimated population structure elaborated on the basis of the data from the Jordan census of 1994. Statistical data acquired from the Block census had been used in the sample design of this study. The density of the population was classified into three categories: high, medium and low density areas.

The number of sampling units assigned for interviewing per Administrative Unit adequately represented the population density.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data Coding At each site the data was coded by investigators to indicate the respondent status and the selection of the modules for each respondent within the survey design. After the interview was edited by the supervisor and considered adequate it was entered locally.

Data Entry Program A data entry program was developed in WHO specifically for the survey study and provided to the sites. It was developed using a database program called the I-Shell (short for Interview Shell), a tool designed for easy development of computerized questionnaires and data entry (34). This program allows for easy data cleaning and processing.

The data entry program checked for inconsistencies and validated the entries in each field by checking for valid response categories and range checks. For example, the program didn’t accept an age greater than 120. For almost all of the variables there existed a range or a list of possible values that the program checked for.

In addition, the data was entered twice to capture other data entry errors. The data entry program was able to warn the user whenever a value that did not match the first entry was entered at the second data entry. In this case the program asked the user to resolve the conflict by choosing either the 1st or the 2nd data entry value to be able to continue. After the second data entry was completed successfully, the data entry program placed a mark in the database in order to enable the checking of whether this process had been completed for each and every case.

Data Transfer The data entry program was capable of exporting the data that was entered into one compressed database file which could be easily sent to WHO using email attachments or a file transfer program onto a secure server no matter how many cases were in the file. The sites were allowed the use of as many computers and as many data entry personnel as they wanted. Each computer used for this purpose produced one file and they were merged once they were delivered to WHO with the help of other programs that were built for automating the process. The sites sent the data periodically as they collected it enabling the checking procedures and preliminary analyses in the early stages of the data collection.

Data quality checks Once the data was received it was analyzed for missing information, invalid responses and representativeness. Inconsistencies were also noted and reported back to sites.

Data Cleaning and Feedback After receipt of cleaned data from sites, another program was run to check for missing information, incorrect information (e.g. wrong use of center codes), duplicated data, etc. The output of this program was fed back to sites regularly. Mainly, this consisted of cases with duplicate IDs, duplicate cases (where the data for two respondents with different IDs were identical), wrong country codes, missing age, sex, education and some other important variables.

28 (Persons) in 2016.

JO：人口密度：每平方公里人口在12-01-2017达109.285Person/sq km，相较于12-01-2016的106.508Person/sq km有所增长。JO：人口密度：每平方公里人口数据按年更新，12-01-1961至12-01-2017期间平均值为38.475Person/sq km，共57份观测结果。该数据的历史最高值出现于12-01-2017，达109.285Person/sq km，而历史最低值则出现于12-01-1961，为11.028Person/sq km。CEIC提供的JO：人口密度：每平方公里人口数据处于定期更新的状态，数据来源于World Bank，数据归类于全球数据库的约旦 – 表 JO.世行.WDI：人口和城市化进程统计。

21 (Persons) in 2016.

207 (Persons) in 2016.

5 (Persons) in 2016.

Quantification of (i) areas suitable for Phlebotomus papatasi occurrence in relation to the whole country; (ii) the population at risk of cutaneous leishmaniasis using different population grids; and (iii) the population at risk in relation to the total population, using different suitability cut-off values.

R2 values derived from the simple linear regression analysis between weather station climate data recordings and modelled climate data using historical recordings. Overall, R2 values were higher for the warmer months of the year (April-September).