Geo-Spatial Data and Maps
In the world of data science there will often be times where you will need to look at data from a broader perspective. You may need to compare values for towns, cities, states, countries, continents or even longitude and latitude. Looking at Geo-spatial data can unlock trends and solutions to world problems that would otherwise go unnoticed. There are always times when data is collected from different parts of the world and in order to compare them, maps are a necessity to help explain shifts, tendencies or whatever else is noticeable across the globe. A data scientist must be able to use Geo-spatial data because without it, we may never find solutions to the worlds unsolved problems.
How do I know what maps to use?
In order to be able to use maps and Geo-spatial data, a data scientist must be able to understand the different types of maps and how to create these maps from the data. A data scientist would need to be able to determine whether a heat map, choropleth map, point map, etc. would be the most useful when creating representations of the data. These are basic types of maps that would need to be recognizable by someone who wanted to look at trends across different areas. One example is point maps which can be used to show the different locations of things such as the different Sonic restaurants in the state of Virginia. Another example is a choropleth map which can be used to show how certain Geo-spatial data is aggregated in certain areas. For example which state has the most sports facilities is something that could be shown on a choropleth map. A final important example is a heat map which can be used to show things like are there any “hotspots” for internet connection in a city.
How do I create these maps?
In order to create these types maps, someone could use many coding methods. One of the more easily usable methods is geopandas which, in python, gets imported by a couple commands and allows you to create maps for your data.
There are many other ways to create these maps, but a good basic understanding of geopandas will help a data scientist out incredibly. There also basic tools available to help with mapping. Through documentation there are many global tools such as worlds and cities that can help someone show certain areas that are most important to them. The can be used in python by reading them into your program.
In the end, a data scientist needs to understand the basic types of maps, how to create these maps and what tools are available in order to be able to use Geo-spatial data. Geo-spatial data and maps are a very important aspect of data science because of the need to find solutions and explanations for many of the world’s and United States’s problems. It can be useful for a variety of reasons because it allows to looks at where specific things are and how everything compares in different areas. The ability to look at how things compare in different places is essential to being able to look at how data varies all over the world.
