Mapping Census Data
It is surprisingly easy to obtain census data and to make colorful maps, yet making maps that are meaningful requires attention to some details that elude many beginning users of GIS. This page covers the following techniques:
- Develop a conceptual model that is worth of exploration
- Prepare a topological framework for your Study
- Understand the elements of census data
- Explore the the Distribution of Quantitative values in YOur Area of Interest
- Apply intuitive mechanisms for graphic understanding of aggregated measures of raw quantity versus measures of intensity or concentration
- Transform aerial units, if necessary, to readily understandable terms.
- Explore the issues and impacts of granularity of data on understanding.
- Avoid all-too-common unsupported assertions made by people who misunderstand aggregated data.
- Make a series of broad-scale thematic maps that reveal patterns that are useful to portray as representations of concepts in your model
- Explore the utility of your at a fine scale with a series of fine-scale maps.
References:
- Understanding Census Data
- Downloading Census Data from the GSD's Network
- Understanding Map Projections
- Mapping with Quantitative Data
- The ArcMap user's guide, Using_ArcMap.pdf has detailed procedures that we will refer to with references highlight like this.
The Tutorial Dataset
Download the tutorial dataset and unpack it to a new directory on your local hard drive. This file contains data from several sources, including some census data for the Boston Area taken from the Geolytics 2000 Longform CD (Blockgroups) and the 2000 Short Form CD (Blocks.) The dataset includes data and metadata from various other sources. It is all organized according to the prinicples for organizing place-based data, discussed in a separete tutorial.
Develop a conceptual model that merits exploration of data
References
Prepare a topological framework for your Study
References
Understand the elements of census data
The census has thousands of columns, they are very detailed in order to be most versatile. Because there are so many columns, they are given coded names that have to be looked up in a Data Dictionary that geolytics extracts into a handy text file that you will find in your tutorial dataset as a text file associated with each dataset n your Census folder.
References
Explore the the Distribution of Quantitative values in Your Area of Interest
References
- Use the Calculate Statistics tool to examine various summary statistics related to the numbers of households in each blockgroup
- Use the Select Tool to Select the blockgroups that are within your window.
- Right-Click on your layer and choose Selection > Save Selection as New Layer to create a layer theat references just the subset of selected blockgroups.
- Now look at thje summary statistics and note how theyve changed.
Portrayal of aggregated measures of raw quantity
Now lets try to understand some issues of quantity. For example, we want to understand which areas have more rental housing units. Of course, we should try to anticipate the predicatble question: Compared to What? We will get to that, but first, we will look at some census data, and its metadata. Then we will look at some map symbolization techniques that ae apropriate for comparison of measures of quantity.
References
- Mapping Quantitative Statistics
- Drawing features to show quantities In particular, see the entries about mapping with Charts.
Portraying Measures of Raw Qauntity with Symbols Scaled in a Single Dimension
- Open the Metadata file for the census blockgroup data, and find columns related to housing tenure
- Open the attribute table for the Blockgroups in ArcMap and find the attributes in question.
- Go to the symbology properties for the Blockgroups and choose to map Quantities by Charts.
- Use a Stacked Bar Chart to map the count of rental housing units and the count of owner-occupied units per Blockgroup.
- Rename the map layer and its legend heading apropriately
- Contemplate the elegance of using a symbol that scales in one dimension to represent quantities so that they can be compared.
Portayal of Measures of Intensity or Concentration
The next mapping problem we will address here is the mapping of relative intensity data. We have discussed the issues of normalization and intensive statistics in class and in the web page, Mapping Quantitative Statistics. Now comes the technical side: How to normalize information according to data that are given, and also how to normalize data according to new data fields that are calculated by you. Then of course, we will need to choose the way that the intensities are symbolized and presented in the map legend.
References
- Mapping Quantitative Statistics
- How to symbolize quantitative data In particular, see entry for Representing quantity with color.
- Elements of Catrtographic Style
- Setting a classification
- Standard classification schemes
Create a Normalized Map
- Create a choropleth map of housing units per square mile.
Transform aerial units and legend categories into readily understandable terms
Take a look at the legend for the map we just made and try to visualize some number of households in a square mile of land. Can you visualize 1280 people in a square mile? This is difficult. It would be much easier to visualize if we simply changed the terms to 2 People per Acre, or 5 people per Hectare. Wouldn't it? Unfortunately, the census gives the land area for each area in either Square Miles or Meters. You should now have compassion for your map users who will have the same difficulty that you do. It would be much easier to visualize what thise statistics mean if we could convert our Square Miles to Acres (Miles x 640) or Hectares (Miles x 259). For this, we will need to create a new column in our attribute table to hold a more intuitive aerial unit and then we will calculate the converted units. Because ArcMap can sometimes truncate the results of a calculation, we will check our results to make sure our new numbers seem right.
Here is a handy conversion table:
| Handy Conversion Factors | ||
| You Have: | Acres | Hectares |
| Square Miles | * 640 | * 259 |
| Square Meters | / 4,047 | / 10,000 |
| 0.001 Square Kilometers | * 4.047 | * 10 |
YOu may want to check your work with this handy online area conversion calculator
Try to think of meaningful classifications that people typically use. For example, you might convert the Households per Acre to Acres Per Household considering that these are the terms that people commonly use (e.g 10-Acre Lots, Quarter-Acre Lots...). See Portland Maps for an example.
References
Adding a Field to an Attribute Table and Calculating its Value
- Open the Attribute Table for your BlockGroups shape file.
- Sort the table's rows in descending order according to the number of Miles per blockgroup. Make a mental note of the magnitude of the largest blockgroup.
- Choose Options > Add Field to add a new field named Acres Make the new column a double precision type that will be able to hold the area of the largest and smallest areas of blockgroups in the table.
- Now right-click on the new column and calculate Acres as Miles times 640.
- Check the largest and smallest blockgroups to make sure our calculated values have not been truncated.
- Now make a map normalized by Acres or Hectares
- Look at the map. Does it present details such as "Where are the very lowest density areas?" "Where are the very highest density Areas?" "What is the trend, and the typical higher and typical lower density areas?
- Adjust the legend breaks as necessary to show details at both ends of the range.
- Use the Classify Button to look at the various options for choosing classification breaks.
- Choose your own classification breaks using common terms for "Acres per Unit."
- Adjust your legend headings and class for your classes that make sense and are clear about the spatial units (blockgroups) and the units for figures in your legend breaks.
Explore the issues and impacts of granularity of data on understanding.
References
Add critical source information to your map layout
Many choices go into making a map with quantitiative data. Since your map will be interpreted by other people, it is important that it include specific information about the data:
- The source and date for the data
- The projection used
- The level of aggregation used -- e.g. tract, blockgroup or block
- The name of the cartographer.
Write a Caption that Disclaims misinterpretations and unsupported assertions of aggregated spatial data.