4.2 Demographic Data

You will need to present two demographic variables of your choosing. Think of these broadly (i.e. poverty rates and the percentage of a particular racial group, or median income and population density). The demographic measures you select should be related to your outcomes, so think critically about what possible socio-demographic factors might be related to what you are interested in. You should use the tidycensus package’s load_variables() function (see the tidycensus website and the Meeting 2-2 materials for details) to search for the relevant concepts.

Each demographic data point (i.e. population density or median income or poverty rate) should be combined into a single file prior along with the geometry for your spatial unit of analysis to export as a .geojson file. For example, if you were to access data on race and poverty, these two measures would need to be joined together into a demographics.geojson file and exported with sf::st_write().

For projects using a non-standard data set: Ideally, the level of aggregation you choose here should match the level of aggregation applied to your point data in Vignette 4 (i.e. if you have demographic data at the census tract level, aggregate your point data in Vignette 4 to the tract level as well). For most projects, then, these data will be collected at the census tract level. See Chris if you have questions about this.

In terms of identifying demographic data, most countries have some form of annual census that can be used to obtain the needed demographic data. Again, see Chris if you have trouble finding these data.