Mapping of Population Density

Abstract

The Bureau of the Census listing of geographical coordinates of centroids of all enumeration districts together with population counts from the U. S. 1970 Census of Population was used to contruct via computer five nationwide geographical grids of population density with sector dimensions of 0.01, 0.02, 0.04, 0.1, and 0.25 degrees of latitude and longitude. The entire population of a district was assigned to a grid sector if the coordinates of the district centroid fell within the boundaries of the sector. The sectors were then rank-ordered according to population density, and listings were made of sector population, population density, geographical location, cumulative population, area of sector, and cumulative area. The five sets of data were synthesized into single equations describing population as a function of density in one case and of area in another. From these data it was found, for example, that about 800,000 people live in 19 sectors of 0.01-degree dimensions with a population density of 100,000 people per square mile or greater (nearly all in Manhattan); about 10 million live in 183 sectors of 0.02-degree dimensions with a population density of 23,000 per square mile or greater; and about half of the total U. S. population, that is, about 100 million people, reside within about 0.6 percent of the area of the United States, that is, within 20,000 square miles.Four representative displays of population density are shown for the Northeast Corridor, including isometric views and a contour map.

Exploring the relationship between population density and maternal health coverage

“…The rate of decrease is governed by the cosine function. So the rate of decrease is initially slow, but then speeds up as grids approach the poles [21]. We do not believe this change in area is relevant to our analysis, for two reasons.…”

Estimating Hilly Areas Population Using a Dasymetric Mapping Approach: A Case of Sri Lanka’s Highest Mountain Range

“…This variability regarding population mapping can also be acknowledged in the existing body of literature. A number of studies have been introduced for population estimation and mapping in different geographic levels using various data sources such as remote sensing imagery and road network, as well as GIS derived data [3,5,[24][25][26][27][28][29][30][31][32]. Meanwhile, it is noted that many studies have deployed a dasymetric approach as one of main techniques for estimating population distribution.…”