This is the text from the People and Place poster

People and PLace

by Steven R Holloway, James Schumacher and Roland L Redmond


To better understand patterns of human settlement, migration, and related economic activities, social scientists traditionally have relied on data gathered directly from individuals and their families. Such "census" data can provide information about the socioeconomic characteristics of people living in different geographic areas at different times. In the United States, complete census data are collected every 10 years from people residing in geographic units defined as census "blocks". These collection units are of variable size and shape, but each are delineated to include about 100 people. Census blocks are nested hierarchically within block groups, census tracts, counties, and states. Choropleth maps of population densities are made by simply dividing the number of occurrences found in the enumeration unit (i.e. block group) by either the size of the unit or the significant population unit. Such maps are used, for example, to show distributions of people per square mile, housing units by age or value per unit area, or even births per 1000 women.
Even though census data are collected at the "block" level, it would be difficult to analyze or display all the data for these smallest collection units, especially for an entire state or region of the country. Consequently, and for reasons of privacy, population number is the only variable available for individual blocks. At higher levels of organization, like the block group, census tract, or county, more detailed information about people and households becomes available. This information can be used to provide valuable socioeconomic profiles of population groups.

The hierarchical relationship among census blocks, block groups, and tracts is shown to the right for an area around Missoula, Montana. The 74 block groups contain 2,238 blocks and fall within 18 census tracts and one county. Again, because individual blocks contain approximately the same number of people, it should be no surprise that blocks and block groups increase in size with increasing distance from the urban center. To the far right is a choropleth map showing population density for the area.

Choropleth maps are the easiest and most common way to display tabular census data. Despite their simplicity, these maps have limited utility for detailed spatial analysis of socioeconomic data, in part because by definition the boundaries of both the enumeration units and the mapping units are the same (i.e., the census block groups). In western North America, human populations are concentrated in relatively few towns and cities, found at lower elevations and along major river corridors. Relatively large expanses of land are essentially uninhabited, especially block groups in tracks that lie farther away from urban areas. When population density, or any other socioeconomic variable, is mapped by traditional choropleth techniques, the results often tell us more about the size and shape of the block group, or other enumeration unit, than about the people actually living and working within them.

Dasymetric maps can circumvent many of the above limitations because the boundaries of their mapping and enumeration units are independent. Described below is the process of creating a dasymetric map of population density based on land cover (and use), land ownership, and topography. Note that the enumeration units are still the census block groups, but the mapping units become land cover polygons within each block group. The first step (A) removes portions of all block groups uninhabited by people. These areas were identified by selecting: 1) all census blocks with zero population; 2) all lands owned by the state or federal government; and 3) all corporate timberlands. Once uninhabited areas are removed, populations in each block group can be reassigned to areas based on land cover, land use, and slope (B). Four general land cover/land use classes were selected: Urban, Agriculture, Forested, and Open. All Urban and Agriculture polygons were assumed to be populated, whereas only those areas of Forested or Open land with a slope of less than or equal to 15% were assigned people (C). People are then assigned to land cover/land use polygons on a per unit area basis (D) - urban polygons were assumed to have higher densities and were given a relative weighting of 80 people per 100 population, whereas open polygons were weighted at 10, and agriculture and forested polygons at 5 each. The number of people assigned to each mapping unit (land cover/land use type) within the enumeration polygon (block group) was based on the formula shown below.
The dasymetric population map shows spatial patterns much more precisely than is possible with traditional choropleth maps. The same process can be applied to other census variables, such as income, gender, race, religion, occupation, housing units, etc., and then analyzed across regions that extend over multiple states and encompass numerous interdependent economies. This approach will allow much more accurate and revealing analyses of spatial relationships between humans and the natural landscape. For example, we can examine settlement patterns over time and in relation to distances from water or forest or wilderness, and thereby better understand why people live where they do in the Rocky Mountain West. Finally, when coupled with other broad scale natural resource data that are becoming available in digital form, the database can serve as a predictive tool to identify areas most vulnerable to any negative consequences of future development or land use change.

Missoula County occupies an area of 2,618 square miles in northwestern Montana with a population in 1990 of 78,687. The bar charts below each map legend show how much area in the entire county is occupied by each class (see the companion poster). The choropleth map shows five classes of population density; the lowest class, "less than 100", covering 2,531 sq. mi. (97%). The dasymetric map, on the other hand, identifies a sixth class, unoccupied lands or "none", which covers 2,354 sq. mi (90%). The dasymetric map's "less than 100" class is only 185 sq. mi. (7%), a far more accurate picture of population density in the county than the 97% indicated by the choropleth map. Changes in class size in the higher classes appear relatively small at this scale, indicating that most of the diVerences in the two mapping techniques are due to the exclusion of unoccupied lands (A) and to a lesser extent slope restrictions (B). This suggests that the database for excluded lands be developed with some care. The use of land cover/land use (C) and relative densities (D) filters are CPU intensive and careful consideration of their effectiveness should be made.

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