Open Source GIS platforms are available for download onto your PC or can be accessed online. The power of Open Source GIS software can transform en masse how we understand and view a geographic area of interest, and influence decision making because of its accessibility and instantaneous updating capability. It allows for fast production, sometimes an interactive environment and multi-editor capacity.
Here are examples of what you can do with datasets in order to create a choropleth map.
A GIS project will require using a storage file of spatial data, whether using shapefiles, or from a database. Usually in an academic course, the instructor provides a database or a set of shapefiles that will be used for a project. In order to seek this data on your own, think about the geographic area in which you are interested in and look for the city or state's GIS extension of their government webpage. If you are interested in a city, their local government may sometimes make these files available for public use.
Here are some sources where you can find geographic data for Philadelphia:
(Pennsylvania Spatial Data Access)
Within a city or county, there will be census tracts. Depending on the desired specificity of your project, you'll want to view available data for a smaller area within the geographic boundaries. You'll want to check for a few things once you obtain your files:
1) Are the properties within the file correct? Does it reflect the geographic area in which I need to work with?
2) If I have downloaded a file, can I correctly extract it, if necessary?
3) How can I keep track of all of the files that I need to use?
Once you have obtained your geographic data, ask yourself what kind of information you want to find by viewing it. For example, if I am working with Philadelphia, I would like to know about the demographics of the city.
Here are some resources where you can add census data to enhance your geographic data and create a small analysis:
You can use the data that they provide here to link onto the geographic data (e.g. per census tract within a county, per county within a state). Data can tell a story within the map, and it is important to know what kind of information you would like to show.
Once you have your geographic data and dataset that you are interested in, it's important to understand what you are looking at. Metadata is data about the data that you have just acquired. You will need to know what the field names are in the spreadsheet, and what their values represent. In any dataset that you download from American FactFinder, there is a metadata file within the same folder. After you extract your data from the file, you can open the metadata file for a better description for all of the field names and the type of information is represented.
Information that can be found in metadata:
Identification of Fields and Field Content
Attribute Data (String, Integer, Numeric, Float, Date, etc)
Spatial Reference System and Projection
Once you have all of the information you need for the GIS project, you'll want to simplify it after you know what the information means. This is as simple as renaming field names to a shorter name to remember what the field contains. Cleaning your data is helpful so that you have the information that you need in order to perform the spatial data tasks and analysis that you need.
Once data is collected (both geographic and your desired dataset) and once data is cleaned so that it makes sense to you, you are ready to view this data on the screen. You first have to choose a GIS software to use. Librarian Greg McKinney has a page on how to find and use GIS software. There is an ArcGIS software trial available to students, as well as availability in computer labs in Anderson and at the TECH Center. If you are more inclined to try to use open source software such as QGIS, you are able to download this onto your laptop or PC.
Once you have decided on the software you would like to use, adding your spatial data across the previous platforms mentioned are fundamentally the same and straightforward. Joining the dataset of your liking requires more exploration of the tools and familiarity of the data type compatibility with the software.