variables: 959844

This table contains all the variables of all the datasets we have at Our World In Data. Note that this includes only the information about the variables like name and default display settings, not the data itself.

This data as json

id name unit description createdAt updatedAt code coverage timespan datasetId sourceId shortUnit display columnOrder originalMetadata grapherConfigAdmin shortName catalogPath dimensions schemaVersion processingLevel processingLog titlePublic titleVariant attributionShort attribution descriptionShort descriptionFromProducer descriptionKey descriptionProcessing licenses license grapherConfigETL type sort dataChecksum metadataChecksum
959844 Urban population people   2024-07-30 12:02:10 2024-07-30 12:02:10     1816-2016 6645     
{
    "name": "Urban population",
    "unit": "people",
    "tolerance": 5,
    "numDecimalPlaces": 0
}
 0     upop grapher/cow/2024-07-26/national_material_capabilities/national_material_capabilities#upop   2 minor   Urban population         Urban population is the size of a state’s urban population in each year for the period 1816-2016. _Data Acquisition and Generation_ "Urban population" is a difficult concept to specify and operationalize for a professional demographer, let alone an international relations researcher. What criterion best captures the meaning of the term? A common approach is to include all cities that exceed a size threshold. Many such thresholds, ranging from 5,000 to 100,000 inhabitants, have been advanced. By virtue of its simplicity, we adopted the threshold criterion using the upper value of 100,000. This choice has the advantage of facilitating data completeness, which is problematic at lower values. It has the corresponding liability that, in the early 1800s, many areas that one might consider "urban" did not contain 100,000 people. Moreover, the approach appears less well suited for the contemporary period, when build-up areas frequently are comprised, in large part, of many smaller cities and unincorporated places. While the best data came from national censuses, several of them do not tabulate urban population. Some developed nations take sample surveys to construct reasonable estimates of urban population while multinational sources and demographic experts also publish data based on their own estimation procedures. We used such estimates whenever they did not contradict formal census figures. The data reflect varying national definitions of what constitutes an incorporated city or urban area; we used these figures where alternatives were unavailable. Occasionally, a source changed its city definition, thus creating a discontinuity in the time series. In instances before 1945 where more than one alternative was offered as to the boundaries of a city, we adopted the one more closely reflecting the built-up area. Otherwise, we entered the data as it was reported. Occasionally, the data reflect a mix of and de jure information. In some states, it was the case that there would be de facto data for one urban area while there would only be de jure data for another urban area of within a state. For instance, looking to Russian urban data, it is rather easy to find recorded urban population data for the Moscow urban area; finding recorded data on St. Petersburg or Vladivostok is much more challenging. Usually we found only one or the other; secondary sources offered scant clarification in order to present a series with as much documented data as possible. Faced with this ambiguity, we averaged across de facto and de jure totals. For the occasional country that mixes data from different years in the same report, the project used interpolation and extrapolation to estimate the referent year. Often, the value of the same urban population datum is revised from one demographic yearbook to the next. Presuming that revised data are more accurate, we used them. When, as often was the case, this introduced a discontinuity between the first year appearing in the revised series and the previous year appearing in the old, we performed log-linear regression on all the old data in our pooled series and adjusted the regression line to match the revised data points. When we encountered numbers from other sources significantly different from the United Nations series, we used the U.N. figures unless they were irregular. In the latter cases, we used the log-linear regression method on available data points, the United Nations and otherwise. For cases of recently declining urbanization (e.g. Belgium and the Netherlands in the 1970s), we filled the data gaps in the same way using a constant negative growth rate. We conceive of urbanization as a continuous process, for which the growth rate should vary smoothly. On the other hand, the inclusion of additional cities, as they exceed the population threshold, introduces discontinuities in the census totals. Moreover, some cities appear in one enumeration, but are absent from the next. Cities also occasionally make first-time appearances bearing totals well over the threshold population value. Secondary sources remedied the situation to a limited extent. Since interpolated and extrapolated values can be dominated by such irregularities, we frequently used log-linear regression as a means of smoothing the data obtained by the above methods to obtain a final estimate. 
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 8f5c84850e7b37a7747d72ad32f2d04a e55a2b3321e8458e0c4e1651366b3f56

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