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Effective Cartography

Mapping Census Data

It is 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:

References:


Download and Explore The Tutorial Dataset

Download the tutorial dataset and unpack it to a new directory on your local hard drive. The dataset includes data and metadata from various sources. It is all organized according to the Primer on Organizing Data and Metadata with Arcgis. If you open the file work_pbc/arcmap/docs/compilation2.mxd you will see that a nice graphical hierarchy has been created for our study of Somerville. following the tutorial Elements of Topographic Mapping The idea behind this framework is to provide labels for the major political and physical elements that tie oue area of interest together with its regional context.


Understand the Elements of Census Data

Use your file system browser to look at the files in the folder sources/social_explorer These files resulted from the procedure described in Downloading Census Data. Our data came from Social Explorer, but it is in the same basic form (comma delimited text) as would have resulted from a download from the Census Bureau's American Factfinder site. As you see, there is a Data Dictionary in the folder which will be handy in a few minutes. The data came form the 2005-2009 American Community Survey. The spatial units of the dataset are Blockcgroups. These terms are described in the Primer on Census Data

Now check out the files in the tiger. What you see here is a shape file of Massachusetts Blockgroups downloaded frrmo the Census Bureau's TIGER site. . This layer has metadata formatted as an FGDC compliant xml file, which can be read within arcmap (provided youhave the arcmap 10 service pack 2 or greater installed.

References


Join your Census Data Table with the BlockGroups Feature Class

As you see, the census data is in a comma delimited text file and the blockgroups are in a shape file. Before we can start mapping these, we need to join them together. This procedure uses the FIPS code (federal information processing standard) as the linking file to uniquely identify the blockgroups in each table. IN the Social Explorer table the FIPS code is named GEOID10.

References

  1. Use the ArcMap Add Data button Open the csv file from the social explorer folder and the shape file from the tiger folder.
  2. Open the tables associated with each of these datasets. Open the data dictionary from the social_exploer file for reference.
  3. Right-Click your TIGER shape file and choose Joins and Relates < Join
  4. Fill in the blanks in the Join dialog to choose your social exploerer table as the table to join to. Choose FIPS and GEOID10 as the linking fields, respectively.
  5. Once the join has been done, inspect the attribute table for your tiger shape layer.
  6. Just for fun, adjust the Symbology properties of your new joined layer to mmap the quantities associated with the column V0T003002 according to the data dictionary this is the Land Area for each blockgroup in Square Miles.
  7. Zoom To layer on your new blockgroups Layer.

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 predictable 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

Portraying Measures of Raw Quantity with Symbols Scaled in a Single Dimension

  1. Open the Metadata file for the census blockgroup data, and find columns related to housing tenure
  2. Open the attribute table for the Blockgroups in ArcMap and find the attributes in question.
  3. Go to the symbology properties for the Blockgroups and choose to map Quantities by Charts.
  4. Use a Stacked Bar Chart to map the count of rental housing units and the count of owner-occupied units per Blockgroup.
  5. Rename the map layer and its legend heading appropriately
  6. Contemplate the elegance of using a symbol that scales in one dimension to represent quantities so that they can be compared.

Portrayal 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

Create a Normalized Map

  1. 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 these 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:AcresHectares
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

  1. Open the Attribute Table for your BlockGroups shape file.
  2. 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.
  3. 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.
  4. Now right-click on the new column and calculate Acres as Miles times 640. YOu can find plenty of unit conversion tools on the web, but some of the more useful conversion factors are provided in the Page about mapping with Quantitative Data
  5. Check the largest and smallest blockgroups to make sure our calculated values have not been truncated.
  6. Now make a map normalized by Acres or Hectares
  7. 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?"
  8. Adjust the legend breaks as necessary to show details at both ends of the range.
  9. Use the Classify Button to look at the various options for choosing classification breaks.
  10. Choose your own classification breaks using common terms for "Acres per Unit."
  11. 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.

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: