In this article, we will delve into the fascinating world of Geographic Information Systems (GIS) and explore the Bigfoot sightings data. GIS allows us to analyze and visualize spatial data, providing valuable insights into various phenomena. We will be using ArcGIS, a popular software for spatial analysis, to examine the Bigfoot sightings data and answer some intriguing questions. So, let’s get started!
Understanding the Coordinate System
The first step in our analysis is to understand the coordinate system used for the Bigfoot sighting points. In ArcGIS, we can view the Bigfoot sightings data, which covers all of North America. Additionally, we have County data, which includes population and County information, limited to the contiguous U.S. and Hawaii. Interestingly, the County data excludes Alaska.
To determine the population of the Bigfoot sighting points, we need to locate the properties Source tab and navigate to the spatial reference section. Here, we find that the projected coordinate system used is the North America Equidistant Conic projection. Additionally, the North American datum of 1983 is listed as the geographic coordinate system originally used for plotting the data. It is important to note that all spatial data must have a geographic coordinate system or datum as the starting point. In this case, the coordinate values have been transformed from latitude and longitude into planar coordinates measured in meters.
Calculating Population Density
Next, we turn our attention to calculating the population density. To do this, we rely on the County data set, specifically the attribute table. Within the attribute table, we find information about the counties, including a population field labeled UE PE 001.
To calculate population density, we need to determine the area. While there is a land data set available, it is recommended to calculate new geometry for accuracy. We create an area field and use square meters as the unit. By calculating the geometry, we obtain the area values for each county.
Now, we are ready to calculate the population density. Considering the population field and the newly calculated area field, we initiate a field calculation. The result is a new field labeled “pop Den” (population density) that represents the number of people per unit area.
Analyzing the Results
With the population density values at hand, we can analyze the data to find interesting insights. Sorting the population density in descending order, we discover that New York County, New York has the highest population density. However, there are numerous counties with a population density of zero, despite having inhabitants such as Loving County, Texas, which has 87 people living in it. This discrepancy may have occurred due to the calculation involving squared meters. To validate this theory, we decide to recalculate the population density using a larger unit, such as hectares. Interestingly, this adjustment yields non-zero values for Loving County, Texas and provides accurate results.
Continuing our analysis, we now turn our attention to Utah. We want to determine which Utah County has the lowest population density among the selected counties. By filtering the data to only show Utah counties, we discover that Garfield County has the lowest population density based on our earlier calculations.
In this first part of our vector challenge, we explored the Bigfoot sightings data using GIS. We examined the coordinate system used for the data, calculated population density, and analyzed the results. Through this analysis, we determined that New York County, New York has the highest population density while Garfield County, Utah has the lowest population density among the selected counties.
In the next part of the vector challenge, we will continue our exploration of the Bigfoot sightings data. Stay tuned for more exciting insights and analysis!