Data visualization leverages maps as compelling tools for conveying geospatial information, and these can broadly be divided into static and interactive types. Static maps offer fixed visual portrayals of data, providing a snapshot in time, whereas interactive maps foster a dynamic user experience, allowing viewers to delve deeper into the presented data. Within these categories, we encounter specific types of maps tailored to represent data differently. Choropleth maps, for instance, utilize varying colors to signify statistical data across specific geographical areas such as countries or states, offering a visual representation of data variation across regions. Symbol maps, however, use symbols to represent data points at precise geographic locations, often varying in size or color to depict the magnitude or category of a specific variable. Lastly, locator maps are designed to help viewers find a particular location or navigate their way through a geographical area. Each of these maps plays a unique role in enhancing the comprehension of complex data sets in the field of data visualization.
This week we experimented with DataWrapper, a user-friendly tool for creating interactive charts, maps, and tables. We had to find three distinct data sets that corresponded with the three main types of maps. Then, we had to demonstrate our ability to bring the data we found into DataWrapper to make one of each type: choropleth map, symbol map, and locator map.
Choropleth Map
Choropleth maps are a powerful visual tool used in the field of geography and statistics to represent spatial data. They use various colors or shades within predefined geographic areas to illustrate the distribution and range of a particular attribute like population density, average income, election results, and so much more. By translating complex data sets into an easily understood visual format, choropleth maps allow us to make quick comparisons and gain insights across different regions.
Last week, I came across a news report on the heat wave that was coming to the Midwest, Northeast, and South of the United States. I decided to take the data presented in the report, clean it up and organize it in an excel sheet so that I could bring it into DataWrapper and create a choropleth map. I thought it would be interesting to be able to visually observe where the heat wave was going to hit and to what extent for the upcoming weekend.
Symbol Map
Symbol maps provide a compelling visual representation of geospatial data. They use symbols or markers of different sizes, shapes, or colors placed at specific geographical locations to depict variations in data values. The symbols might indicate anything from the population of a city to the number of businesses in an area. By presenting complex data in a visually intuitive manner, symbol maps allow viewers to observe patterns and trends easily.
I decided to make a symbol map on the population of various cities and towns in Connecticut. I made sure to spread it out across not only the state but each county because I wanted to depict the population distribution in each area. From looking at this symbol map, we can see which counties and cities are the largest in the state. For example, the largest counties are Fairfield, Hartford, and New Haven. To narrow it down even more, we can see that Bridgeport, Stamford, New Haven, Hartford, Waterbury, Norwalk, and Danbury are the largest cities.
Locator Map
Locator maps are like the ‘you are here’ signs on a map. They show you where a certain place is located within a larger area. So if you’re reading about a small town in a book or seeing a place on the news, a locator map can help you figure out where that town or place is in relation to a larger, well-known area.
For my locator map, I knew I wanted to make it simple and narrow it down to a small area. I recently went to an event at the Connecticut Convention Center so I thought it would be interesting to see the nearby places or attractions that were a short drive away or even within walking distance.