Workbook Exercises for ArcView 9 - Penn Engineeringese502/extra_materials/ArcView_9.3... · School...

Amy Hillier University of Pennsylvania School of Design Cartographic Modeling Lab

Workbook Exercises for

ArcView 9.3

GIS TRAINING CARTOGRAPHIC MODELING LAB

EXERCISE 1: INTRO TO ARCGIS, SPATIAL DATA FORMATS, and SYMBOLOGY GOALS for SESSION 1 At the end of this session, participants should be able to:

Add data to ArcGIS, reorder map layers, pan and zoom;

Distinguish vector and raster data;

Save and move ArcGIS files; IN‐CLASS EXERCISES PHILADELPHIA BLOCK PARTIES Open Phila_block_parties.mxd Work with pan and zoom tools and re‐order of map layers. Use the map tips and identify (“i”) tool to identify street and neighborhood names. Investigate the difference between vector layers (points—block parties, lines—block party streets, and polygons—PHMC neighborhoods) and raster layers Add orthophotograph of North Philadelphia (georectified) and Phila_1954 (not georectified) Explore symbology options for point and polygon (areal) data TAKE‐HOME EXERCISES PHILADELPHIA IN THE 1890s These data are part of the Mapping the Du Bois’ Philadelphia Negro (www.mappingdubois.org). Rather than focusing on the Seventh Ward where W.E.B. Du Bois conducted his research for The Philadelphia Negro, these data cover the entire city. Begin by opening the file Philadelphia_1890s.mxd in the PHILA_Phila_Negro folder. You can double‐click on the file name or start the ArcMap software, then from the File menu, choose “Open,” navigate to the file, highlight it, and click “Open.”


Look at the map layers. Which map layers represent points, polygons, and raster GIS? The first layer, PA_hospital_admissions include detailed information about 3544 admissions to Pennsylvania Hospital in 1900, as recorded at the Pennsylvania Hospital Archives. Look at the variables included with the data by right clicking on the name and going to “Open attribute table.”

Unfortunately, the data do not indicate the race/ethnicity of the patient, but they do indicate the diagnosis and the result of the care received at Pennsylvania hospital. Take a look at the next two map layers, scarlet_fever_1896 and consumption_1897. These data were collected at the City of Philadelphia Archives from records of contagious disease. Today we refer to “consumption” as tuberculosis. The scarlet fever data include race/ethnicity. What proportion of white vs. black people suffered from scarlet fever? Sort the RACE column and highlight the records for black people (to highlight multiple records, click in the gray square to the left of the column furthest to the left, then scroll down; you can also select individual records by holding down the Ctrl key.

Look at the bottom of the table to see the number of records highlighted (5) and total number of records (1066). Does it make sense that so few blacks would have suffered from


scarlet fever? Probably not. Most likely, all cases of scarlet fever were not recorded, and those who were less likely to receive formal health services would have been less likely to show up in this kind of register. Clear the selected records (from the Selection menu above or Options menu at the bottom of the table). What was the average age of the people with scarlet fever? Right click on the AGE column and choose “Statistics.” The mean age was just 6.66 years old. The frequency distribution shows that scarlet fever affected mostly children. Compare this with the average age for people with consumption (35 years old). TB impacted a very different age group.

Turn on the scarlet_fever_1896 and consumption_1897 layers (put a check mark in the box in the table of contents). What do you make of the patterns? Can you identify clusters? Scroll to the bottom of the table of contents and turn on the consumption and scarlet fever density layers. These are raster GIS layers, something you can confirm by zooming way in until you see the square pixels.

Return to the full extent by clicking on the blue arrow (previous extent) or globe (full extent). When analyzing the spatial patterns in the density maps, one obvious question is,


how did population density vary across the city? You need to know that in order to determine if some of the dark shaded areas have unusually high proportions of sick people. Notice the very regular shape circle in West Philadelphia, just across the Schuylkill River, in the consumption density layer. Turn on the consumption_1897 point layer to see what is causing this cluster. Change the color of the consumption_1897 points in order to see them better (click on the red dot to bring up the Symbol Selector). Notice that there is one point directly in the middle, but very few other points. Highlight the point in the middle using the highlight tool (draw a box around the point). Open the consumption attribute table.

Notice that you actually selected 32 points, not just one point. Click on the “selected” button at the bottom of the table to view just the selected records. Why were there so many sick people at 34th & Spruce Street? The original address listed in the contagious disease records was 34th & Pine, but that address no longer exists. Philadelphia General Hospital (originally Blockley Almshouse) was located at the present sight of Children’s Hospital of Philadelphia. Clear the highlighted records and return to the full extent. Turn off the consumption and scarlet fever layers and take a look at the 1890 and 1900 ward data. Click back and forth between the “Negro” variables for the 1890 wards and 1900 wards. Where did the black population increase the most during the intervening decade? Open the attribute tables and determine the proportion of the population that was black (Negro) in each decade, using the Statistics tool (In 1890, 39,371/1,046,964 people were black, or 3.7%; in 1900, 62,613/1,293,697 people were black, or 4.8%). Philadelphia’s total population was growing, but not as fast as the black population. Look at the percent of foreign born people in 1900. This was a high point for immigration in Philadelphia. What do you know about those wards along the eastern edge of the city? These neighborhoods were home to small row houses and lots of jobs relating to shipping and manufacturing. To compare the distribution of foreign born, blacks, and people who were admitted to Pennsylvania Hospital, create a map showing population density in 1900 by ward. Double‐click on the map layer showing foreign born. Change the variable selected in “Value” to “TOT_POP” and “Normalization” to “Area.” Choose “5” from the “Classes” dropdown menu and click “OK.”


Turn on the Pennsylvania Hospital admissions layer and zoom in to the area with the most points. Note that the points do largely reflect population density, but they also likely reflect the location of Pennsylvania Hospital at 8th & Pine Streets, in the middle of the big cluster on the eastern side.


EXERCISE 2: THEMATIC MAPPING GOALS for SESSION 2 At the end of this session, participants should be able to:

Symbolize area (polygon) and location (point) data using categorical and continuous variables;

Distinguish between counts and rates and choose appropriate symbology for each; IN‐CLASS EXERCISES 1. The Old Seventh Ward The map layers Du_Bois_parcels_1900.shp and Seventh_ward_streets.shp are from the Mapping the Du Bois Philadelphia Negro project and cover the area that Du Bois surveyed for his 1899 book, The Philadelphia Negro. The data Du Bois collected through his door‐to‐door survey were not saved, so 1900 U.S. Census data was used to recreate the area as he found it (or close to it). The values for the variables are listed in Data_Dictionaries.xls. Note that “99999” represents missing data and “9999” indicates not applicable.

Make the data frames toolbar visible and rotate the layers so the streets run perfectly east‐west.

Create categorical maps using the parcel layer and variables BldgType, DOM_GROUP, and DuBoisGrad. What patterns do you see?

Create thematic maps with quantities using the variables CHILD, SERVANT, BOARDERS, PERCHILDDIE. What patterns do you see?

Map two variables at once, using two different map layers with the top layer symbolized using graduated circles, outlines, or hatching. What relationships can you see?

Use the identify tool to identify properties of interest (such as outliers) and street names.

2. US Diabetes US_cities.shp uses the centroid of one of the city’s zip codes for the location and includes population data based on 2007 US Census Bureau estimates. US_states_diabetes.shp includes self‐report data about diabetes between 1993 and 2006 from the Center for Disease Control & Prevention http://209.217.72.34/aging/TableViewer/tableView.aspx?ReportId=432

Create a choropleth (graduated color) maps showing the three‐year average for 1993‐1995 and 2004‐2006. Where was diabetes the highest?


Create a choropleth map showing the change in the diabetes rates (graduated color and bar chart). Where has there been the greatest increase? Has there been a decrease anywhere? What might account for these patterns?

Symbolize the cities using graduated and proportional circles. Which works better? Is there any relationship between diabetes rates and large cities?

TAKE‐HOME EXERCISES Heath Care Services in Washington State The layers used in this exercise are typical of the spatial health data available on the Web. Without additional attribute data (that a researcher would typically collect and attach to data like these), these data do not tell much of a story, so imagine what additional data you might add—hypothetically—as you move through the exercise. 1. Start ArcMap and from the folder called “WA_health_services,” add the hospitals, primary_care_shortages layers, and local jurisdiction areas. 2. Open the attribute table for the hospitals shapefile to see what attribute data has been provided. If the variable names are not obvious, open ArcCatalog to look at the metadata (you can launch ArcCatalog from the start menu or using the ArcCatlog button in ArcMap)

3. Symbolize hospitals using Unique Values to show the different type of hospital. After choosing “type” from the “Value Field” drop‐down menu, be sure to click on the “Add All

ArcCatalog button (looks the same in ArcMap)

Expand contents of directory “WA_health_services” and highlight “Hospitals.shp”

Click on “Metadata” tab to see available metadata


Values” button. Take the check mark out of “All other Values” and delete the next category which has no name type (and only one hospital) by right clicking and choosing “Remove Value(s).” Click on the symbols next to each type to change the shape, size, and color.

4. Make a second copy of the hospitals layer by either (1) adding the layer again from the “WA_heath_services” folder or (2) right clicking on the layer and choosing “copy,” then

Choose the variable “type” Click “Add All Values” Click on dots to change shape, size, and color


right clicking on the word “Layers” in the table of contents and choosing “Paste Layer(s).” Symbolize this layer using proportional symbols based on the variable “bed_total.” From the “Display” tab, choose 50% transparency for this layer, so you will be able to see data underneath in subsequent steps.

5. Symbolize the local jurisdiction areas so you only see the outline.

6. Symbolize the primary care shortage areas using Unique Values and the “primcare” variables. Consult the metadata using ArcCatalog to determine the meaning of the codes. Type the code into the “Label” section. Note that the areas with no shortages have the most hospitals and hospital beds.

Click on the display tab to set the transparency Choose “proportional symbols” and “Bed Total”

Change symbol shape and color

No fill/hollow option (or choose “no color” from “Fill Color”


6. To further explore the demographic characteristics of these areas, add the census_block_group_sf1 layer from the “WA_health_services” folder. Note the warning you receive about the geographic projection of this new layers. Click close. In this case, the map layer will still draw with the other layers, but in other cases, you will not be so lucky. In most cases, you will want to check the map layer projection information in the “source” tab of the map layer’s properties. This layer is defined as “geographic” meaning that the data has not been projected into a known projection system. But it has been “defined” so ArcGIS knows how to draw it with the other map layers.

Type in the appropriate labels

Areas with “no shortages”


7. Symbolize the block groups using a choropleth (graduated colors) map. In order to distinguish areas with no population, click on “classify,” then “exclusion” and type "POP04_SQMI" =0. Click on the “Legend” tab and check off “show symbol for excluded data.” Choose a gray box for the symbol and “non‐residential” for the label, then click “okay.”

8. Round off the values by using the “Range” field. Note that you can only change the last value in the range. Add a comma separator and “/sq mile” text in the “Label” field. Choose a color ramp that shows increasing intensity. Click “okay” and look at your map. Note that you see little variation and nothing but gray (the outlines from the block groups) near the areas with the most hospitals. There is one large non‐residential area—Mt. Rainier National Park.


9. Return to the “Symbology” tab. Click on the word “Symbol” and choose “Properties for all symbols.” Eliminate the gray outline by decreasing the outline width to 0.

Adjust the ranges here. Adjust the labels here.

Mt. Rainier Park

Gray outline on block groups makes it difficult to see pop density values


10. Click on the “classify” button and explore some of the other classification choices (quantile, standard deviation). Which works best? Look at your map to see how closely hospitals follow population density. 11. Add the cities layer from the “WA_health_services” folder. Note that you get the same warning about the coordinate system that you did for the block group layer. This is because these two layers are from the US Census, which provides data in a geographic (unprojected) format. The other data layers were projected using the state plane system (NAD_1983_HARN_StatePlane_Washington_South_FIPS_4602_Feet). 12. Right click on the cities layer and choose “label features.” To make a halo around the city names so they are more visible, go to the “Labels” tab on the layer properties, click on “symbol” button, then “properties.” Click on the “Mask” tab, then click in the “Halo” radio button. Change the size to 1.5 and click “okay” (three times).


13. Make the lowest population density value color hollow (fill color “no color”) so you can see the areas of greatest population density on top of the primary care shortage areas. Are there areas with shortages that have high population densities?

14. Make a dot density map showing where Native Americans (Ameri_Es) live. Make each dot equal to 30 people. Remove the block group boundaries and fill color using the options under “Background.” Click okay. Because there are so many block groups, this could take ArcMap a minute or so to draw.

15. Save your ArcMap document with relative paths. From the “File” menu, choose “Document Properties.” Click on “data source options” and choose the radio button next to “store relative path names to data sources.” This will allow you to move your files more easily, without disrupting the path names upon which ArcMap depends. From the “File” menu, choose “Save.” Navigate to the “WA_Health_Services” directory and save the file as “WA_health_services.mxd.” 16. Close ArcMap. Move the “WA_health_services” directory to a new location, as though you were taking these files with you to work on a different computer. From the new

Make the fill “no color” and the outline 0 width

One dot is equal to 30 Native Americans/Eskimos


location, double‐click on the “”WA_health_services.mxd” file to open the saved ArcMap document. The file should open and the map should look just as you saved it. If it does not

open or you see red !, all of the files were not saved in the same directory or the .mxd file

was not saved with relative paths.


EXERCISE 3: INTRO TO ARCGIS, SPATIAL DATA FORMATS, and SYMBOLOGY GOALS for SESSION 3 At the end of this session, participants should be able to:

Distinguish between data that have and have not been georeferenced;

Georeference scanned historical maps; IN‐CLASS EXERCISES 1. Philadelphia HOLC Residential Security Maps FDR and the US Congress created the Home Owners’ Loan Corporation in 1933 to help homeowners avoid foreclosure. HOLC refinanced 1 million mortgages across the country, taking over the mortgages from private lenders. When homeowners failed to make payments to HOLC, HOLC foreclosed on them and resold the property. HOLC also created residential security maps for 239 cities across the country in the 1930s, color‐coding areas based on their the stages of the neighborhood life cycle and risk for real estate investment. Many scholars refer to these as “redlining” maps because the “hazardous” areas were colored red, but these maps were not widely distributed and there is no evidence that they were used to inform private lending decisions—although private lenders clearly used maps to decide where to make loans.

Georeference using city outline (PHILA_base_files folder); fine‐tune using streets file. Add HOLC_1935.shp. HOLC_1936.shp, and HOLC_1937.shp and symbolize them based on the map grade (1=green, 2=blue, 3=yellow, 4=red). How much did the maps change over time? (the changes likely represent changing survey standards rather than changes in the neighborhoods).

Add the HOLC_loans.shp and HOLC_foreclosures.shp. Where did HOLC made loans relative to the security grades? Where did HOLC foreclose on properties?

2. Philadelphia Expressways Map Expressways.tif (in the PHILA_historical_census folder) shows the plans for an extensive network of expressways for Philadelphia. Many of these were never built.

Georeference the map using the city_outline.shp.

Add 1950_tracts.shp (from the PHILA_historical_census folder) and create a choropleth map showing the black population. Where were the planned expressways relative to the concentrations of African Americans? Note in particular the expressway planned across South Street, just below Center City.


Add the current streets layer and create a definition query so that only the class 1 roads (highways) can be seen. Which of the planned expressways were actually built?

TAKE‐HOME EXERCISES Philadelphia’s Old Seventh Ward Add Seventh_ward_streets.shp from the PHILA_Phila_Negro folder, then add Du_Bois_original_map.jpg. Say “yes” to building pyramids. Zoom to the .jpg, which is not georeferenced so it does not line up with the streets, by right clicking on the map layer name and going to “Zoom to layer.” Zoom in to look at the map and the legend (bottom left).

Return to the previous extent, either by clicking on the blue arrow or by right clicking on the Seventh_ward_streets layer and choosing “Zoom to layer.” Right click on the Seventh_ward_streets layer and choose “Label features” so you can see the street names. Make the georeferencing toolbar visible (go to the View menu, toolbars, and select “georeferencing”) and choose “fit to display.” You should see the Du Bois map underneath the streets layer now. Make the streets red so they will show up better against the Du Bois map (right click on the line segment under the layer name to bring up the symbol selector). Zoom in to find locations that you recognize on both the Du Bois map and street layer. Try starting with Broad and South Street. Using the “Add control points” tool, click on the Du Bois map at Broad and South, then the streets layer. The map should shift a bit. Try Seventh and Spruce Streets next, then 25th and South Streets. Activate the Magnifier from the Window menu to look for additional places to add control points based on small variations in the map layers. If the maps move further apart rather than closer, you can delete the last control point by clicking on “View link table,” highlighting the last coordinate, and then clicking on the “X” button.


Once you are happy with the way the map layers line up, go to “rectify” from the Georeferencing menu. You’ll need to shorten the name so that it is 13 or fewer characters (try “Du_Bois_rectify”). Make sure you have permission to write to the folder where the rectified image will be saved. Click “save.” The file will be about 75 MG because the cell size was so small. If you are not able to save the file because it is too big, try saving with a smaller cell size. You will probably want to delete the file when you are done. Right click on the Du_Bois_original_map.jpg and choose “remove.” Add the rectified map layer.

For practice georeferencing a second map, add 1895_Bromley.jpg from the same folder. This is a fire insurance map of the same area Du Bois surveyed (in fact, Du Bois likely traced this or a similar Bromley map to create his map of social class). Say “yes” to building pyramids. Make sure you are zoomed to the full extent of the Seventh Ward (click on Seventh_ward_streets and choose “Zoom to layer” to make sure). From the Georefencing toolbar, choose 1895_Bromley.jpg, then from the Georeferencing menu, choose “Fit To Display.”


Turn off the rectified Du Bois map and make the streets yellow (instead of red) so you can see them against the Bromley map. To align the Bromley map with the streets and Du Bois map, first locate Broad & South Streets on both map layers (zoom in as needed). Add a control point—first click on Broad & South on the Bromley map, then the streets map. Now move to the top right corner (NE) and create a control point at 7th and Lombard Streets. Remember, start with the Bromley map, then click on the Streets map. If the maps move further apart rather than closer, you can delete the last control point by clicking on “View link table,” highlighting the last coordinate, and then clicking on the “X” button. Add control points at 25th and South and 25th and Spruce, then use the magnifier to fine‐tune the alignment. From the Georeferencing menu, choose “rectify” and save the Bromley with a name that has 13 or fewer characters. Remove the un‐rectified Bromley map and add the rectified version. You can view the Du Bois and Bromley maps together by making the Bromley map semi‐transparent. First, turn the rectified Du Bois map back on. Right click on the rectified Bromley map and choose “Properties,” then the “Display” tab. Change transparency to 50%. Using the pan (hand) tool to center the map on 21st and Lombard Streets. You can see from the Du Bois map that African Americans lived along the north side of Lombard Street, across the street from the Octavius Catto School for Colored. Catto was a prominent black teacher who served in the Union Army during the civil war and helped organize the Pythians, a black baseball league. Catto was murdered on election day in 1871 while returning home to put on his uniform to help keep order in the polls. This is the present day site of the Lombard Street Swim Club (check it out in Google Maps if you don’t believe me). Notice also that the Kershaw Factory took up much of the 2000 block of Naudain Street. Although blacks lived close to the factory, they probably did not work there as blacks were generally denied access to manufacturing jobs during this era.


EXERCISE 4: JOINING TABLES & CALCULATING TABLES GOALS for SESSION 4 At the end of this session, participants should be able to:

Add attributes to existing shapefiles by joining tables;

Calculate new values inside ArcGIS;

Calculate area, perimeter, length, and XY coordinates for shapefiles; IN‐CLASS EXERCISES Philadelphia Vital Statistics Open the file Vital Stats Events by CT.xls in Excel. Notice that there are multiple worksheets and extra columns and rows (such as a worksheet title at the top and total at the bottom). This is typical of attribute data you will find and need to reformat to make it GIS‐ready. Reformat the “Live Births” worksheet so that you can bring it into ArcView. Be sure to get rid of any punctuation or spaces in the column names (and keep them to 10 or fewer characters).

Add the table birth_outcomes_2006.xls from the Philadelphia vital statistics folder;

Add the shapefile for Philadelphia census tracts (tracts2000.shp) from the PHILA_2000_census folder.

Join birth outcomes table to census tracts using the Tract field.

Make the join permanent by exporting the table and adding it back in.

Create a new variable representing the odds ratio for low birth weight births.

Add the poverty table and join it to the census tract shapefile with the birth data attached. Make the join permanent.

Calculate a poverty odds ratio and compare it to the LBW odds ratio.

Create thematic maps of LWB and births to teen mothers by race/ethnicity. TAKE‐HOME EXERCISES U.S. Substance Abuse Open a new ArcMap document and add the shapefile “US_states.shp” from the “US_substance_abuse” directory. Open the attribute table. Notice that the only attribute available is the state name.


Add the table “SubstanceAbuse.dbf.” Open the table to explore the attributes. Look at the metadata for this table in ArcCatalog. (In case the metadata are missing, the data show Illicit Drug Use in Past Month, by Age Group and State. Age groups are 12‐17, 18‐25, and 26 and over. 95% prediction intervals are provided in parentheses. Totals are for total people 12 and over. See http://oas.samhsa.gov/2k5state/ageTabs.htm#Tab1 for more details). Compare the format of the state variable in the US_States shapefile and SubstanceAbuse table. Do you notice any differences? Look at how “Washington DC” is represented (“District of Colum”). Right click on the US_states shapeflie, choose “Join or Relate,” then “Join…” Use the “State” field from each table as the join field and click “OK.” Choose “yes” to automatically create an index for the join field. Open the attribute table for US_states.shp and make sure the join worked (any <null> values?). Make the join permanent by right clicking on US_states.shp, going to “Data” then “Export” and saving the shapefile with a new name.

The data on substance abuse can now be mapped. Note that you have count data which is more appropriate for graduated/proportional symbols, dot density, and chart maps than choropleth (graduated color). Try making each of these kinds of maps. Which representation tells you the most?


Dot density maps allow you to include multiple variables in the same map. The dots are intended to show the general distribution, but since they are based on aggregate data, the dots are places randomly within each state. For the chart map, use the three age groups as the three different pie slices. Remove the check mark next to “Display in 3D” from the “Properties” button. From the “Size” button, choose 8 pts under “Symbol” and the radio button next to “Vary size using the sum of the field values.” Remove the lines from the pies using the “Properties for All Symbols” under the word “Symbol.”

Graduated symbols Dot density showing different dots for different age groups


To create a choropleth map, you need a denominator. Add the table “US_populations_states.xls.” Because it is an Excel table, you must double‐click on the name, then select the worksheet (there is only one worksheet, called “Sheet0$”). Open the table. Note that the population categories are not identical to the population categories in the substance abuse data. This table was generated using the “custom table” option from American Factfinder, which gives you the most flexibility in constructing variables, but it was not possible to break the data at 25 years old. For the purpose of this exercise, we will ignore this discrepancy.

Join the population data to the shapefile using the “state” field. Open the attribute table for the shapefile. Notice that one record did not join: Washington DC. You will need to amend the name in the shapefile to match the format of the name in the population table (you cannot edit an .xls table; if you want to edit the population table, you need to save it as .dbf first, then add it back). Right click on the shapefile, go to “Join and Relate,” “Remove all Joins,” and “Remove all Joins.” Open the attribute table for the shapefile and highlight the District of Columbia. Right click on the “State” field, go to “Field Calculator,” and set the value = "District of Columbia.” Note the error message; there are not enough spaces in the field for the full name. That is why the name in the shapefile was truncated to “District of Colum.” Create a new column (options menu, “Add a field”) called “NewState” and formatted as text with 20 characters. Calculate the value for Washington DC as‘”District of Columbia”. From the options menu, reverse the selection. Set the value of “NewState” = [State] for all of the


other states. From the options menu, clear the selection. This should give you a column with all the state names that match the formatting of state names in the population table. Try the join again—be sure to use the “NewState” field for the join. This time the join for DC should work. Make the join permanent by exporting and making a new copy of the shapefile.

Make a choropleth map showing the percent of people 12 and over who used illicit drugs, by state. To do this, you can calculate a new variable for the percent (be sure to leave % and other symbols out of new field names) or you can calculate the value “on the fly” from the symbology tab by using “TOTAL” in the “values” field and the total population 12 and over as the “normalization” field.

Make a map showing the percent of people who used illicit drugs as a location quotient. To do this, you will need to calculate two new fields: one for the percent of people 12 and over using illicit drugs and one for the location quotient. Calculate the percent of people 12 and over using illicit drugs ([TOTAL]/Pop12over). Right click on this new column and go to


“statistics” to find the mean percent if illicit drug use for all states: 0.000083 (0.0083% or 83/10,000).

Create a new column called “DrugUseLQ” and format it as double, to allow a fraction. Calculate the LQ as the local/state rate (percent drug use) divided by the national rate (0.000083). Resulting values below 1.0 indicate below average; values above 1.0 indicate above average.

Create a map based on the location quotient. Use a divergent color ramp to distinguish above and below average rates. For more on divergent color ramps, visit http://www.personal.psu.edu/cab38/ColorBrewer/ColorBrewer.html and explore the “diverging” color ramps. Consider re‐labeling your LQs to make them easier to interpret.



EXERCISE 5: LABELING & LAYOUTS GOALS for SESSION 5 At the end of this session, participants should be able to:

Create labels map features individually or automatically;

Design a publication‐quality map layout;

Export map layout and insert into document or presentation; IN‐CLASS EXERCISES Philadelphia Intergroup Conflict The Philadelphia Commission on Human Relations records incidents of intergroup conflict which involve some incident between two or more people of different races, ethnicities, national origins, religions, sexual orientations, etc. They may or may not involve violence or actual crimes. Most of the incidents in this data set (1995‐1999) involved differences in race.

Add the layer “ig_conflict.shp” from the PHILA_intergroup_conflict folder.

Add 1970_tracts.shp from the PHILA_historical_census folder and create a choropleth map of % white population.

Add trcts2000.shp and Tracts2000.dbf from the PHILA_2000_census folder. Join these based on STFID and create a choropleth map showing the % white population in 2000.

Note that many of the areas with concentrations of intergroup conflicts (in South Philadelphia and Kensington/Frankfort) have experienced a decline in the percent of white residents. In other words, these areas have been in racial transition over the past few decades.

Create a map layout (landscape orientation) showing the 1970 and 2000 maps with a declarative title (“Intergroup Conflicts More Likely in Racial Transition Zones” or something like that), legend, north arrow, scale bar (in miles), note about data source (US Census and Philadelphia Commission on Human Relations), date map created, and map author.

Export the map as a 300 dpi .pdf and email it to yourself.

Export the map as a 300 dpi .jpeg and insert it into MS Word and MS PowerPoint.

TAKE‐HOME EXERCISES Philadelphia’s Old Seventh Ward Add the Seventh_ward_streets.shp and Du_Bois_parcels_1900_census.shp from the PHILA_Phila_Negro folder. From the View menu, go to Toolbars and select the “Data


Frames” toolbar. Using the “Rotate Data Frame” tool, rotate the map layers so they run perfectly horizontal, an adjustment of about 7.4 degrees.

Symbolize the parcel layer based on the variable DOM_GROUP. Right click on the map layer, go to the Symbology tab, and choose Categories. From the Value Field drop‐down menu, select DOM_GROUP. Click the “Add All Values” button. Open Data_dictionaries.xls to determine what the values stand for (see below). Type the dominant group name in the “label” section. white PA native 1

white southern migrant 2

white migrant 3

white Irish 4

white German 5

white English 6

white Russian/Polish 7

white immigrant other 8

black southern migrant 9

black Caribbean 10

black PA native 11

No One Group was Dominant 99

To create a map layer showing which households had servants, right click on the parcel layer and choose “copy,” then right click on the word “Layers” at the top of the Table of Contents and choose “paste.” Right click on this new parcel layer, choose “Properties,” and go to the “Symbology” tab. Choose “Quantities” and “Graduated symbols.” From the “Value” field, choose SERVANT which is the number of servants in the household. Choose 2 classes. Make the first class equal to 0 and the second equal to 0.0001 – 15. Change the symbol for the first category a solid black dot, 6pts, with “no color” so that you can’t see it. Make the symbol for the second category a solid black dot, 6pts.


Right click on the streets layer and choose “Label Features.” Make the streets “no color” so that you only see the labels. Right click on the streets layer, choose “Properties” and go to the “Labels” tab. Click on the “Placement Properties” button and select “On the Line” for Position, removing the check mark next to “Above,” then click OK. Now the labels will fit better in the white sections of the streets. Investigate the relationship between race/ethnicity/national origin and presence of servants. Almost all of the white households along Spruce and Delancey had servants while almost none of the smaller houses along the smaller streets to the South where African Americans had servants. Zoom in on the western half of the map (there’s no way to make a clear letter‐sized map that includes the entire Seventh Ward).

From the View menu, choose “Layout View.” From the “File” menu, go to “Print and Page Layout” and change the orientation to Landscape. Re‐size the box with the map so that it takes up the whole page. Use the pan and zoom tools to make sure the map in the Layout View includes the area you want.


Insert a title, north arrow (critical since we rotated the map), scale bar (you can change the map units by clicking on the “Properties” button inside the Scale Bar dialog box), data note (author, date, and data sources) and legend. Format the legend with two columns. Delete the word “Legend.” Once you have created the legend, right click on it and choose “convert to graphics,” then choose “ungroup.” Take out the words that are unnecessary and group the remaining items neatly.

Finally, create an overview map to indicate where this section of Philadelphia is located. From the Insert menu, go to “Data Frame.” Use the Add Data button to add map layers for the outline of the city and rivers (from the PHILA_base_files folder). Make the city outline hollow with a black outline and the rivers dark blue or black. Right click on “New Data Frame” and go to “Properties.” Click on the “Extent Rectangle” tab. Click on the word “Layers” on the left side and, using the arrow button in the middle, send it to the left. Click on the “Frame” button, change the frame to 2.0 Point black, and click OK.


Re‐size the new map on your layout. You can delete the frame outline by right clicking on the overview map, going to “Properties” and the “Frame” tab and selecting “none” for border.

From the File menu, go to “Export” and create a 300 dpi .pdf. Open the .pdf file. How is the quality? Look and see how big the .pdf file is (it should be small and the image should look crisp. This is the ideal format for emailing or posting on the Internet). Next, create a 300 dpi .jpg. This is ideal for inserting into documents and presentations. Open MS Word or PowerPoint and insert the file using the Insert menu. You may need to resize (from the corners, not the sides; you want to avoid distorting your map) and crop the map at this stage.



EXERCISE 6: MAP PROJECTIONS GOALS for SESSION 6 At the end of this session, participants should be able to:

Recognize whether map layers are projected or not;

Recognize whether map projections are defined;

Project map layers that are unprojected;

Define projections for projected map layers missing spatial reference information; IN‐CLASS EXERCISES

Download a Philadelphia shapefile from PASDA. Add it to ArcMap, look at the coordinates and spatial reference information.

Download a Philadelphia shapefile from the ESRI Tiger site http://arcdata.esri.com/data/tiger2000/tiger_download.cfm. Add it to a ArcMap (first the same .mxd where the PASDA shapefile is, then into a new ArcMap document), look at the coordinates and spatial reference information.

Define the census shapefile as North American Datum 1983, then project it into State Plane PA South (feet).

Delete the .prj file after projecting the census shapefile, then go through the process of defining a known projection.

TAKE‐HOME EXERCISES The shapefiles includes with this workbook have been defined and projected—meaning that they have been transformed to account for the curve of the earth, and those projection systems have been registered so that the ArcView GIS software can recognize them. Sometimes when you download data from the Internet or receive it from other sources, it will not be projected. This can cause problems with map layers lining up and defining distance units, and it can also make your map layers look distorted (thus identifying you instantly as a GIS novice). These exercises use data from the Internet to show you how to identify whether a map layer has been projected and how to project it if it is not. Most states have GIS repositories where you can download basic shapefiles such as roads, parcels, and aerial photographs. Penn State maintains this website for Pennsylvania: http://www.pasda.psu.edu/. Go to this website, click the “download data” tab at the top, and choose “Philadelphia” in the country drop‐down menu (or any other county). Scroll down to the Philadelphia data and choose a map layer (such as “Evacuation routes”) and


download by clicking the disk icon on the right. Accept the terms of the agreement and save the file to your desktop or other convenient location.

Unzip the file, open ArcMap, and add the shapefile. First, look at how the map draws. If you have looked at enough GIS maps of Philadelphia, the map will look “right.” Next, look at the coordinates in the bottom right corner. These coordinates shouldn’t necessarily mean anything to you, but note that they are not longitude‐latitude coordinates and have “feet” as their unit. Finally, right click on the map layer name, go to “Properties,” and choose the “Source” tab. Note that next to “Projected Coordinate Systems” there is a detailed description of the projection. All three of these things—the way the map looks, the coordinates, and the projection listed in the “Source” tab—all indicate that this map file has been projected AND defined, meaning that the projection has been registered so that the software recognizes it. You are lucky when you receive shapefiles like this.

While state GIS repositories generally provide data in a projected format, GIS data from the US Census Bureau is generally not projected. This is because individual states frequently have their own standard projections but the US Census Bureau does not and leaves it up to individual users to determine the most appropriate projection. You can download census shapefiles from the census website (www.census.gov) or the ESRI website (http://arcdata.esri.com/data/tiger2000/tiger_download.cfm). This exercise is based on downloading data from the ESRI website, but there shouldn’t be much of a difference. Choose “Pennsylvania” and then “Philadelphia” from the county drop‐down menu. Put a check mark next to one of the layers (such as “Voting Districts”), click the “Proceed to


Download” button, and then click the “download file” button. Save the file to your desktop or other convenient location.

Add the Voting Districts shapefile to the ArcMap document where you added your PASDA file. Note the warning that comes up, indicating that the spatial reference information is missing. Click “OK.” Note that the coordinates at the bottom right have not changed. Right click on the Voting Districts map layer, go to “Properties,” and choose the “Source” tab. Notice that next to “Coordinate System” it says “<undefined>.” These are all signs that ArcView does not know what kind of layer this is—projected or unprojected. The reason it draws fine is because ArcView forces this new map layer into the projection system of the PASDA map layer.

Create a new ArcMap document (from the File menu; do not save changes to the existing map document) and add the Voting Districts map layer. You won’t get the message about missing spatial reference information, but notice that the map looks skewed (leaning to the right). Notice, also, the coordinates in the bottom right corner with “decimal degrees” as the unit. These are longitude‐latitude coordinates which indicate that the map is not projected (longitude‐latitude are how we describe locations on a globe—which has not been flattened (“projected”). Right click on the Voting Districts map layer, go to “Properties” and look at the “Source” tab. Next to Coordinate System it says “<Undefined>” just as it did before. This indicates that, in addition to not being projected, the map layer is


not defined. Unprojected map layers can be defined, but the ones from the U.S. Census Bureau are not.

We need to define and project the map layer so that it matched the projection of the Evaculation routes. We need to launch ArcToolbox inside ArcMap in order to do this. Click the button with the red toolbox.

Expand the “Data Management Tools” section, then “Projections and transformations.” Before we can project the map layer, we need to define it. Double click on “Define Projection.” Select the Voting Districts layer from the first drop‐down menu, then click on the button to the right of the second drop‐down menu to define the Coordinate System. Click the “Select” button, then open the “Geographic Coordinate Systems” folder. “Geographic” is synonymous with “unprojected” in ArcGIS. Open the “North America” folder and select “North American Datum 1983.prj.” Click “Add,” then OK and OK again. Your map layer has now been projected. Confirm this by looking at the “Source” tab under the layer “Properties.” It should say “Geographic Coordinate System GCS_North_American_1983.”


Click on “Feature” to see more options, then double‐click on “Project.” Choose the Voting District layer from the first drop‐down menu. The second field is for naming the new shapefile that will be created (you may not want to accept the default name and location). Click the button to the right of the third field, the “Output Coordinate System.” Click the “Select” button, then open the “Projected Coordinate Systems” folder. Double‐click on the “state plane” folder, then the “NAD 1983 (feet)” folder, and choose “NAD 1983 StatePlane Pennsylvania South.” There are other projections that would work, as well, but this is the standard used by the City of Philadelphia. Click “Apply,” then OK, then OK again.

A new shapefile should appear in the table of contents. It will look just like the original shapefile because it’s forced into drawing in the same unprojected space since the coordinate system for the first shapefile added to ArcMap establishes the projection. Start a new ArcMap document (no need to save changes to the previous document) and add the projected map layer in. Notice that Philadelphia draws much straighter, the coordinates in the bottom right match the coordinates for our Evacuation routes layer, and the “Source” tab reports the state plane projection.


EXERCISE 7: WORKING WITH THE U.S. CENSUS GOALS for SESSION 7 At the end of this session, participants should be able to:

Distinguish between the Decennial Census short and long form and the American Community Survey;

Download and reformat census attribute data for any part of the country;

Download and project census shapefiles for any part of the country;

Join census attributes and shapefiles and create thematic maps; IN‐CLASS EXERCISES

Download census shapefiles from www.census.gov and/or http://www.esri.com/data/download/census2000_tigerline/index.html

Download census attribute data from American Factfinder http://factfinder.census.gov/home/saff/main.html?_lang=en

Create map layout in ArcMap based on census data downloaded in class

Export map layout to PowerPoint and MS Word TAKE‐HOME EXERCISES Creating a Map with US Census Data from Scratch First, you need to decide the county and level of geography for your map. Let’s try 2000 census tracts for Chicago. From http://arcdata.esri.com/data/tiger2000/tiger_download.cfm, choose Illinois and Cook County. Select 2000 census tracts and download the file to your desktop. Unzip the file and add it to ArcMap. Note that the file is not defined or projected. Open ArcToolbox (button with the red toolbox), expand the “Data Management Tools” and “Projections and Transformations.” Double‐click on “Define projection.” Select the Cook County census tract file from the first drop‐down menu, then click on the button to the right of the next field. Click the “Select” button, then “Geographic Coordinate Systems” folder, “North America” folder, and choose “North American Datum 1983.” Click “Add,” OK, and OK again. Your map layer is now defined. Now it needs to be projected. Expand the section under “Feature” in ArcToolbox and double‐click on the “Project” button. Select the Cook County census tracts in the first drop‐down. The second field will be grayed out. In the third field, rename the map layer “Cook_County_tracts.shp.” Click the button to the right of the third field, click the “Select” button, open the “Projected Coordinate Systems” folder, open the “state plane” folder, then the “NAD 1983 (feet)” folder, and “NAD 1983 State Plane Illinois East


projection.” Click “Add,” then OK, and OK again. Open a new ArcMap document and add the new projected shapefile.

Now we need to download attribute data. Go to www.census.gov and click on the button on the left side for “American Factfinder.” Choose “Data Sets,” “Decennial Census,” “Census 2000 Summary File 3—Sample Data,” and then the “Detailed Tables” link. For geographic type, choose “census tract,” then specify “Illinois” and “Cook County.” Choose “Select all Census Tracts” and click “Add.” You should now see all the census tracts for Philadelphia listed in “current geography selection” at the bottom of the page. Click “Next.”

Type “poverty” into the search box with the “keywords” tab active or scroll down to table P88: Ratio of Income in 1999 to Poverty Level. Highlight the table and click the “Add” button below, then “show results.” On the next screen, click on the “Print/Download” link along the blue banner and select “Download.” Choose the radio button next to “Microsoft Excel (.xls)” under the “Database Compatible” options and then “OK” to save the zipped file to your desktop.


Open the zipped file and extract the table called “dt_dec_2000_sf3_u_data1.xls.” You don’t need the other files. Open this with Excel. Delete the first row (because you can only have one row with variable names) and first column (because you only need one unique geographic identifier). Rename the second column “STIFD.” Delete the columns called “geographic summary level” and “geography” as well because these do not contain useful information. Notice that the data on poverty rate is broken into categories for less than 50% of poverty, 50‐74%, and 74‐100%, so you will need to combine the counts in these three columns and divide by the total number of people surveyed in order to determine the percent of individuals living below 100% of the poverty line. You can combine these categories in Excel or ArcMap, but it is probably easiest to do this in Excel. If you do it in ArcMap, you will need to change all of the column names so they have 10 characters or less with no spaces and no punctuation, then create a new column and calculate the % of individuals living in poverty. (At this point, you can create a new column in Excel called “Pov_Rate” and set it equal to the sum of 50%, 50‐74%, and 75‐100% divided by the variable “population for whom poverty status was determined.” Copy the formula for each row. Copy the new column, then “paste special” (right click option) in a new column. This will allow you to delete all the other columns—but keep the variable “population for whom poverty status was determined.” Notice that in 11 census tracts, the denominator is zero so the poverty rate is undefined. If you leave these poverty values as they are, they will appear as <null> values and be treated as zeros in ArcMap. You can also calculate the poverty rate in ArcGIS).


Rename the variable “geographic identifier” as “TractNum” and the denominator as “PovDenom.” Save the table under a new name (“Cook_county_poverty.xls”). ArcGIS 9.3 can read .xls files, but ArcGIS 9.1 and earlier cannot. Add “Cook_county_poverty.xls” to your ArcMap document using the “Add Data” tool. Double‐click on “Tract_poverty.xls” and then choose “Sheet0$.” If you try to add the “Cook_county_poverty.xls” table without double‐clicking and specifying the Excel worksheet, you will receive an error that the “selected item could not be added.”

Open both attribute tables (poverty and the census tract shapefile) to confirm that there is a common unique identifier (STFID). Close both attribute tables. Right click on the Cook_county_tracts shapefile and go to “join.” Choose “STFID” as the field for the join and click OK. It’s a good idea at this point to make your join permanent by right clicking on the


shapefile, going to “Data” and “Export data,” giving the file a new name (such as “Cook_tracts_poverty.shp”).

Next, combine the columns to create a single variable showing how many individuals live below 100% of the poverty line. First, create a new column (click the “Options” button and choose “Add field”) called “TotPov” that is formatted as a long integer. From the “Options” menu, choose “Calculate value.” Set “TotPov” = under 50% poverty + 5‐74% poverty + 75=99% poverty. Click OK.

Next, create a column calculating the poverty rate. From the “Options” menu, choose “Add field,” call the new variable “PovRate” and format it as a double. Highlight all the records (census tracts) where the denominator (all those for whom poverty status was determined)


is = 0. Switch the selection from the “Options” menu. Right click on the new column and choose “Field Calculator.” Set PovRate = PovTotal / PovDenom. Clear the selection and create a thematic map showing poverty rate.


EXERCISE 8: GEOCODING ADDRESSES & AGGREGATING POINT DATA GOALS for SESSION 8 At the end of this session, participants should be able to:

Format addresses for geocoding;

Create an address locator for any county in the U.S.;

Geocode addresses and carefully review unmatched records;

Assign points to polygons

Aggregate points by polygons; IN‐CLASS EXERCISES

1. Philadelphia Food Stores This list of stores come from the Pennsylvania Department of Welfare and includes all the stores that were certified to accept food stamps as of March 2007. It includes most, but not all, corner stores, and it likely includes all supermarkets. There are no useful attribute data distinguishing the type or size of store.

Create an address locator in ArcToolbox based on Philadelphia streets;

Geocode the addresses in Food_stores.xls, from the PHILA_food_stores folder;

Review the unmatched candidates in order to look for patterns; match the records that you can;

2. Historical Mortgages Mortgages_1941_1959.xls represents a random sample of mortgages made on properties in Philadelphia between 1941 and 1959. Berean.xls and Metlife.xls represent random samples from the same period for Berean Savings & Loan (a black‐owned business) and Metropolitan Life Insurance Company.

Create an address locator in ArcToolbox based on Philadelphia streets;

Geocode the addresses of the mortgages (using the ADDRESS field, not owner’s address);

Review the unmatched candidates in order to look for patterns; match the records that you can;

Look at the distribution of mortgages by lender type and interest rate relative to different 1950 census tract variables (L_type coding: 1=federal savings & loan, 2=savings & loan, 3=mortgage company, 4=life insurance company; 5=bank or trust; 6=savings fund; 7=individual; 8=other).

TAKE‐HOME EXERCISES


LA Alcohol Outlets Add the table “LA_alcohol.xls” to ArcMap. Be sure to double‐click on the table name, then click on the worksheet name (“m_tape911# txt$). Open the table and look for the columns corresponding to the street address and zip code.

In order to convert this tabular address data to a point shapefile, we need to create an “address locator” based on a street centerline file. Open ArcToolbox, expand the “Geocoding Tools” section and double‐click on “Create Address Locator.” Choose the address locator style by clicking on the folder icon to the left of the first dropdown menu. Select “US Streets with Zone.” This allows you to use both the street address and zip code (zone) for the address matching.

Click on the folder next to the “Reference Data” dropdown menu. Navigate to the “LA_Alcohol” folder and choose “LA_streets.shp.” This “reference” file will be the basis of your address locator. Click in the white space next to the column marked “Role” and choose “Primary Table.” Note that the circles with red X’s disappear. Check the name and location of the “Output Address Locator” at the bottom of the dialog box to make sure this is a good


place for the address locator (LA_Alcohol file). Click “OK.” Your address locator should not be complete so you are ready to geocode addresses.

From the “Tools” menu in ArcMap, go to “Geocode” and “Geocode Addresses.” You should see the LA_streets_CreateAddressLocator file listed first. If not, click “Add” and navigate to the folder where you saved it. Highlight the LA address locator and click “OK.” In the next dialog box, make sure the worksheet from your Excel table with the alcohol outlet addresses is listed in the “Address table” drop‐down (otherwise use the folder icon to navigate to the table). Choose “StreetAdd” for “Street or Intersection” and “Zip” for “Zone.” Give your new file a name (LA_Alcohol_outlets.shp) and location (in the LA_Alcohol folder). Click on “Geocoding Options.” Most of these defaults are fine. Note that the “&” sign can be used for intersection addresses. Take the check mark out of the box next to “Match if candidates tie.” Click “OK” and then “OK” again to start geocoding. When ArcMap is done geocoding the addresses, you will see a summary of the results.

Note that 223 (7%) records did not match in this first round. To look at these and match them one at a time, click on the “rematch” button and then select “unmatched addresses” from the drop‐down menu on the next screen. Examine each address to figure out the problem. Make modifications to the address in the “Street or Intersection” box and hit the “Enter” key on your keyboard. If possible “candidate” addresses appear at the bottom, you can select one and then click “match.” Then move on to the next address. When you have made all the corrections you can, click “close” and then “done.”


Add the shapefile “LA_block_groups.shp” from the folder called “LA_alcohol_outlets.” Open the attribute table (right click on the map layer name in the table of contents and choose “open attribute table”). Notice that there are no attributes, other than the block group number. Add the table “LA_block_group_data.dbf” to ArcMap. Open the table. If the variables are not obvious to you, consult the data dictionary included in the metadata in ArcCatalog. You must join the block group attribute data (.xls table) to the block group shapefile in order to map it. Right click on LA_block_groups.shp and go to “Join and Relates,” then “Join.” Make sure “Join an attribute table” is chosen from the first drop down menu and that “STFID” is selected as the join field for the block group shapefile and attribute table. Click “yes” to index the join field. Open the attribute table for the block group shapefile to inspect the join. Note that the variable names are a combination of the table name and variable name.

Make the join permanent so that the variable names can be shorter and you don’t have to join the data again. Right click on LA_block_group.shp and go to “Data” and “Export.” This process creates a new shapefile. Give the new file a name (LA_bg_census_data.shp) and location (LA_Alcohol folder). Click “yes” to add the new shapefile into ArcMap. Delete the old shapefile and attribute table. Double‐click on LA_bg_census_data.shp and from layer properties, go to the symbology tab. Create a choropleth (graduate color) map showing median household income. Be sure to round off the values and use “,” and “$” in the label section. Click on the word “symbol” and choose “properties for all symbols.” Make the outline width 0 so the block group boundaries don’t dominate the map. Click “OK.”


Add the map layers “LA_water” and “LA_city_boundary.” Symbolize the water using an appropriate color. Symbolize the city boundary so you only see the outline (choose “hollow” or “no fill color” and make the outline width 2). Zoom into LA city (the larger block group shapefile is for LA county). Create a copy of the block group shapefile by right clicking on it and choosing “copy,” then right click on the work “Layers” at the top of the table of contents and choose “Paste Layer(s).” Make a choropleth map of population density with this new layer. What is the relationship between the location of alcohol outlets and median income?

To further explore the relationship between the alcohol outlet locations and block group census data, we will attach (“link”) the block group data to each alcohol outlet. Right click on your new alcohol outlets shapefile, choose “Join or Relate” and then “Join.” From the first drop‐down box, choose “join data from another layer based on spatial location.” Choose LA_block_groups as the layer to join, then the radio button next to “is closest to it” (this choice works more consistently than the first choice). Give your new shapefile a name (Alcohol_outlets_bg_data.shp) and location (LA_alcohol folder) and click “OK.” Click “OK” to add the new shapefile to ArcMap. Open the attribute table for your new shapefile. Note that each record (alcohol outlet) is associated with a block group number (SFTID) and the census variables have been assigned. This kind of file can be used for statistical analysis.


We can also count the number of points (alcohol outlets) by polygons (zip codes, block groups, etc.) to represent this as aggregate data. We do this with a spatial join starting with the polygon shapefile instead of the points. Add “LA_zipcodes.shp” to ArcMap. Right click on the zip code map layer and go to “Joins and Relates,” then “Join.” Again, choose a spatial join. This time the other options will look different because you are joining points to polygons rather than polygons to points. Select the first radio button to create a count field identifying the number of alcohol outlets that fall in each zip code. Give your new shapefile a name and location and click OK. Click OK to add the new file to ArcMap. Open the attribute table and scroll to the far right and look at the column marked “Count.” This should indicate how many alcohol outlets fall in each LA zip code. Highlight one record and examine this zip code area on the map. Do you count the same number of alcohol outlets?

Create choropleth maps showing the number of alcohol outlets per person and per square mile. Is it easier to identify patterns in the spatial distribution of alcohol outlets this way? What do you lose with the aggregate data?


EXERCISE 9: CREATING BUFFERS & CALCULATING DISTANCES GOALS for SESSION 9 At the end of this session, participants should be able to:

Create single or multi‐ring buffers;

Calculate Euclidean distance between sets of locations;

Calculate network distance using Network Analyst; IN‐CLASS EXERCISES Philadelphia Outdoor Advertising Data in the “PHILA_outdoor_ads” folder are from a study of unhealthy outdoor ads in Philadelphia in 2005. The data are for five zip code areas chosen because they have different income and racial/ethnic compositions. For more about the study, see Amy Hillier, Brian Cole, Tony E. Smith, Antronette Yancey, Jerome Williams, Sonya Grier, and William Yancey (in review with Health & Place), “Concentration of Unhealthy Outdoor Advertisements around Child‐serving Institutions: A Three‐city Comparison.”

Create a choropleth map of population density by block group. Does this help explain the distribution of ads?

Create multiple‐ring buffers around the child‐serving institutions (rec centers, libraries, schools, day cares);

Aggregate the number of unhealthy ads (alcohol, tobacco, fast food, sugary beverage) within each of the buffers;

Calculate the distance between the unhealthy ads and closest child‐serving institution;

Conduct spatial and attribute queries (such as, which school has an alcohol ad within 500 feet?).

TAKE‐HOME EXERCISES University of Pennsylvania Campus From the UPENN_campus folder, add UPenn_footprint.shp and Footprint_streets.shp. Highlight the Van Pelt Library so that the Multiple Ring Buffer we create will only be around the Library. Launch ArcToolbox and use the “search” tab to find the Multiple Ring Buffer.


Double‐click to bring up the dialog box. From the first drop‐down menu, choose “university_footprint.” Specify a name and location for the new buffer shapefile you will create in the next field. Type distances, one at a time, in the next field, using the “+” to add each one. Then click OK.

To count the number of buildings within each of the buffers, right click on your new buffers layer and choose “Join.” From the first drop‐down menu, choose “Join data from another layer based on spatial location” and specify “University_footprints” as the layer. Specify a name and location for the new file which will be a second buffer shapefile and click OK. Open the attribute table for the new shapefile and notice the “Count_” field to the far right. This is a count of the total number of buildings in each buffer zone. Notice that the buildings are fairly evenly distributed in the first four buffer zones (which cover an area of slightly less than ½ mile).


To calculate the distance between each building on campus and the Van Pelt library, add the point shapefile (Van_Pelt_Library.shp). Right click on the University_footprint map layer and go to “Joins.” From the first drop‐down menu, choose “Join data from another layer based on spatial location.” Specify “Van_Pelt_Library.shp” as the file to join to. Give the new shapefile (which will be a duplicate of the footprints shapefile) a new name and location and click OK. Open the attribute table for the new footprints shapefile. Note the distance field (units are feet because of original projection) on the far right, representing the distance from each building to Van Pelt.


Matched pairs You can calculate the distance between two sets of points using the spatial join feature, but that will calculate the distance between the closest two points in the two different sets. Imagine that you have matched pairs—biological and foster parent households, patient and doctor office addresses—where the match does not necessarily include the closest two points. In order to calculate those distances, you’ll need to use a script for ArcGIS. Scripts are short programs written to extend or customize ArcGIS functionality. From the “Matched_pairs” folder, add the two shapefiles: Matched_pairs.shp and Matched_pairs2.shp. Notice that the attributes for the tables are the same. For each record (point), two sets of XY coordinates are listed, the XY coordinates for the point and its match. This is the format required for this particular script.


From the “Matched_pairs” folder, open the ArcScript folder and open the PointDistance.doc file. ArcGIS scripts are available to download for free from the ESRI website (and other sites, or you can create your own). Note the description of what the script does, what inputs it needs, and what outputs it creates. Scripts will also often come with detailed instructions for installing them. Return to ArcGIS. From the “View” menu, choose “toolbars” and go to “customize” at the very bottom.

Highlight the “commands” tab, click on the “Add from File” button at the bottom and browse to find the PointDistance.dll file. The .doc version of the file just included the instructions; .dll is the extension for a script recognized by ArcGIS. Click OK. You should see the Point Distance tool listed under “Commands.” Pick this tool up and drop it on one of your toolbars in ArcMap.

Click on your new button to open the Point Distance dialog box. From the first drop‐down menu, select “Matched_pairs: Shapefile.” Click in the next field to give the resulting output


file a name and location. The file that you create will be a line shapefile linking the matched pairs with an attribute of the distance between the two points. Select the first X and Y coordinates for the “From” fields (these are the X and Y locations of Matched_pairs.shp), then choose the second set of X and Y coordinates for the “To” fields (these are the X and Y locations of Matched_pairs2.shp). Click the “process” button, then OK and OK again. The distance attribute for the new shapefile will be in feet because those were the units of the XY coordinates (which were in state plane projection).

Calculating Networks and Network Distances Geocoding addresses is only one of many functions that can be performed on a network. Network Analyst opens up a number of very useful applications, including:

• Shortest Path Finding (Distance) • Shortest Path Finding (Cost/Time) • Network (Manhattan) Distance • Nearest Facility • Facility Service Area • Efficient Multi‐Destination Trip Planning

Imagine that medical clinics are scattered across the city and you need to arrange an inspection tour. A great deal of time will be lost in moving from one clinic to the next. There is an enormous potential for increased efficiency in “connecting the dots” with Network Analyst as opposed to doing it by hand. These functions are the same as the ones employed by Google Maps or MapQuest when you query them for directions. You can even generate a turn by turn report that can be read by anyone needing to navigate the network. While geocoding uses a network to find specific locations, Network Analyst uses specific locations to identify useful parts of the network.


The following exercise is designed to familiarize you with these functions and give you the tools you will need to further explore the capabilities of Network Analyst on your own. The first step is to create a network file. The input can be in many forms, but a shapefile is the most common and easiest to use. To create the network link, you must open ArcCatalog and ensure that the Network Analyst Extension is turned on. You can check this by clicking on Tools >> Extensions in ArcMap and activating the checkbox beside the label Network Analyst. When Network Analyst is activated, navigate in ArcCatalog to the PhilaStreets shapefile in the PHILA_base_files folder. Right click on the file name and select “New Network Dataset.” Type an appropriate name for the network and click next.

For this exercise, you can continue to click “next” until you reach the following window. When you click “next” here you will be told at least one cost attribute is required. If you click “yes” here, a cost attribute based on feature length will be generated automatically. Click “yes,” then continue clicking “next” until you reach the final dialog and click “finish.” A progress bar will appear for a few seconds while the network link is generated. If you are successful, the following items should have been added to your working folder.

Open ArcMap and load your new shapefile network dataset. At the following prompt, select “yes.” This will import the network, the original shapefile, and the junctions. As in


ArcCatalog, you must ensure that the Network Analyst Extension is active from the “Tools” menu. Load the Network Analyst Toolbar by right clicking a blank area near the top of the screen and selecting Network Analyst from the resulting drop down. First, let’s take advantage of Network Analyst’s ability to get from one point to another using only the network we have linked to it. It will obey the traffic laws as we have input them such as one way streets, turning restrictions, speed limits etc. The only restriction that we have loaded for this exercise is that of one way streets. Click the “Show/Hide Network Analyst Window” button located on the toolbar. This will add a second Table of Contents beside the standard one. Initially, it will show Stops, Routes and Barriers, all at zero.

Next, click the Network Analyst dropdown on the toolbar and choose the first option, “New Route.” The empty route item will be added to your Table of Contents with default symbology. The Network Location Tools should now be active. Click the “Create Network Location Tool.” While this tool is activated, you can add waypoints that will be connected using the route finder. Most of the time you will have a pre‐existing set of points that you will import directly into Network Analyst as opposed to selecting points by clicking on them individually. If you right click on the Stops object in the Network Analyst table of contents, you have the option to “Load Locations.” Any point shape file that you have loaded in your map will be available to import stops from, or you may navigate to any other files accessible to your machine.

For this next part of the exercise, we will add our points manually. Stop lists with a very large number of points will require more time to solve than ones with fewer points.


Click on the map at locations you wish the route to pass through. By default, the route will connect the points in the order which they were added. Once points have been added, the Solve Tool will be available.When you are satisfied that you have added all of the desired points, click the “Solve” button.

Network Analyst very quickly identifies the shortest possible route using the input restrictions to connect the points in the specified order. The order of stops can be changed by dragging them in the Network Analyst Table of Contents. If you change the order of stops, click the “Solve” button again, and the route will be updated. You can also change the point descriptions the same way you would change a shapefile label. Since longer routes can be fairly complicated, there is also a “Directions” tool that becomes available after you solve your route. Clicking this tool generates a turn by turn route description, similar to what you might get from Google or MapQuest. This list can be saved as a text file, or sent directly to your printer. Also notice, that the distance of your route is shown at the top of this window. You can see the total route distance with greater precision by finding the routes in the table of contents as was shown on the previous slide and opening its attribute table the same way you would with a standard shapefile. The rightmost field is the distance calculated in map units. In this case, it is feet.


For now, the route is forced to proceed from one location to another based on your choices. If you would like Network Analyst to overrule the order you have chosen and find the optimal shortest distance route, you can ask it to reorder your stops to find the optimal route. You will likely see a sharp drop in trip distance. The following connect the same set of points, one more efficiently than the other.


The procedure for determining service areas is much the same. You can create a New Service Area from the Network Analyst Drop down, load your points by right clicking on the Facilities Object in the Network Analyst Table of Contents, set your parameters for maximum serviceable distance/time and solve. This function takes considerably more time to run than does a simple route. This function is very useful aiding in the identification of service shortages and redundancies. The richer the network data you can provide, the more accurate and useful these functions will become.

Network Analyst uses several fields in the attribute table in its route finding and distance calculations.

The above clip from the PhilaStreets attribute table show us some of the key fields. FNODE_ and TNODE_ give each street section a directionality. They stand for From Node and To Node. Each intersection or node has a number. The FNODE_ and TNODE_ fields tell us which intersection is the “start” of the line, and which is the “end.” The start and end of each line segment was determined by the direction of digitizing. The From end was where the digitizer started the line, and the To end is where it was finished. In order to translate that directionality into one that makes sense for navigation, we turn to the ONEWAY field. It can contain the three attributes shown above. TF or To – From, FT or From – To and B or Both. This can be confusing to look at when a single road has been digitized in many parts, and all parts may not have been digitized in the same direction. In this case, a single road that is one way in the same direction throughout may alternate in TF and FT attributes along its length.


There are a large number of reserved field names that are used by network analyst to establish the attributes of your network. While only the most basic Network Analyst functions are shown here, you can get a much more thorough listing of capabilities and instructions in the ArcGIS Desktop Help under: Network Analyst, designing network datasets. If you need to make edits to the attributes of your data, you can visualize street direction and node location through the symbology of PhilaStreets_ND. You can see that many crossings of the Schuylkill Expressway do not have node points. This means that Network Analyst will not allow a route to assume intersections where there are none, such as at grade separated highways.


EXERCISE 10: ATTRIBUTE AND SPATIAL QUERIES GOALS for SESSION 9 At the end of this session, participants should be able to:

Create single or multi‐ring buffers;

Calculate Euclidean distance between sets of locations;

Calculate network distance using Network Analyst; IN‐CLASS EXERCISES San Francisco Head Start The layers in the “SAN_FRAN_Head_Start” folder are from an exercise in Mapping Global Cities by Ayse Pamuk. It includes the subway stations and routes, head start centers, and block group data. Consult the file “MappingGlobalCities_exercise” for the data dictionary for the block group data.

Construct buffers around the BART stations.

Use spatial and attribute queries to identify the head start facilities that are near the BART stations and have an appropriate number of children enrolled.

Use the block group data to determine an affordable place to live with good public transportation access to a Head Start facility.

TAKE‐HOME EXERCISES Philadelphia Billboards Open ArcMap and from the folder called “Phila_billboards,” add the billboards, schools, census_bg, and streets_class12 shapefiles. The billboards shapefile includes all of the billboards registered with the Philadelphia Department of Licenses & Inspections. Look at the attribute table to see who owns most of them. We first want to know if billboards are located in or near areas of high population density. Open the attribute table for the census_bg (block groups). Notice that total population is included as a variable, but not area. To calculate area, we need to create a new column. With the attribute table open, click on “options” and then “Add Field.” Call it “area” and format it as a double (if you choose a different type of variable, the calculation will not work).


Right click on your new “area” column and go to “calculate geometry.” Say “OK” to working outside an editing session. Choose “Area” from the “Property” dropdown menu and “Square miles” under “Units.” Click “okay.”

Symbolize the census block group shapefile based on population density. Double‐click on census_bg.shp and from the symbology tab, choose graduated color. Select “POP2000” as the “Value” and “Area” under “Normalization.” Alternatively, you could create a new column (double) to calculate population density. Click “OK” and look at the map. Are there billboards in any high population density areas?


To determine how many billboards Clear Channel owns, go to the “Select” menu and choose “Select by Attributes.” Choose “billboards” from the “Layer” dropdown menu. Scroll down the list of column names and double click on “Owner,” click on the “=” sign, then click “Get Unique Values” to list all the Owner names. Double‐click on ‘Clear Channel’ to finish your query, then click “OK.” Note that Clear Channel owns billboards all across the city—except the cluster of billboards closest to City Hall. Open the attribute table for the billboards shapefile. Note (at the bottom of the table) that Clear Channel owns 1567 of the 1949 registered billboards in Philadelphia.

Next, we will determine where the charter schools are located in Philadelphia. From the “Select” menu, choose “Select by Attributes.” Make sure the schools layer is selected from the first dropdown menu. Double‐click on “type,” then the “=” sign, and click on “Get Unique Values.” Double‐click on ‘Charter’ to complete the query. Note on the map that many of the charter schools are located close to Center City and from the attribute table that there are 31 charter schools. To un‐highlight the charter schools, go to the “Selection” menu and choose “Clear Selected Features.” Philadelphia’s zoning code prohibits billboards from being located within 660 feet of schools. From the “Select” menu, choose “Select by Location.” Put a check mark in the box next to “Billboards.” Choose “that are within a distance of,” then the “schools” layer, and type “660 feet” into the buffer option. This query identifies all of the billboards (377) within


660 feet of a school. You can also reverse the layers to select the schools (127) that are within 660 feet of the billboards.

You can also combine attribute and spatial queries to, for example, identify all of the Clear Channel billboards within 660 feet of charter schools. Start first with the attribute queries, repeating what you did in steps 4 and 5 (if you de‐selected them after). From the “Select” menu, choose “Select by Location.” From the “I want to” dropdown menu, choose “select from the currently selected features” so we are working only with the subset of billboards that are owned by Clear Channel. Put a check mark in the box next to billboards, choose “are within a distance of” and then choose “schools.” Note that there is a check mark next to “Use selected features” which will allow you to identify the billboards located 660 feet from only the charter schools. Type “660 feet” and then OK. Note that 39 billboards meet the query.


Most of the billboards appear to be located along major highways, such as I‐95, I‐76, and I‐676. The streets_class12.shp layer represents only the state and federal highways in Philadelphia. We can use a query to determine how many of the billboards are located within 300 feet of these highways, but if we create a buffer, we will be able to visualize the relationship more easily. Start ArcToolbox and expand “Analysis Tools,” “Overlay,” and then “Proximity.” Double‐click on “Buffer” to launch the buffer wizard.

From the “Input Features” dropdown, choose “Streets_class12.” Call the new (output) file “Streets_300ft.” Type in “300” and choose feet, then choose “All” for “dissolve type.”


We can determine how many billboards are within 300 feet using a location query. From the “Select” menu, choose “Select by Location.” Put a check mark next to “billboards,” choose “that are completely within” and then select the “streets_300ft” layer. Click “OK.” Open the attribute table for billboards. Note that 1107 out of 1949 billboards (57%) are located within 300 feet of highways.

Clear the selected billboards (“Select” menu, then “Clear Selected Features”). Let’s try the same query another way. From the “Select” menu, choose “Select by Location.” Put a check mark next to billboards, then choose “are within a distance of,” choose the “Streets_class12” layer, and type in “300 feet” as the buffer. Click “OK.” Open the attribute table for the billboards layer. This query selected 999 billboards. To figure out which billboards met the first query but not the second, clear the selected features and repeat the steps for #10. Then from the “Select” menu, choose “Select by Location” again. This time, choose “remove from the currently selected features” and use the same query criteria as #11 (billboards within a distance of 300 feet of streets). Open the attribute table for the billboards layer to see that 108 billboards are still selected (that met the first query but not the second). Look for the highlighted billboards on the map… but there are none. Take a second look at the attribute table and notice that the 108 billboards have a “U” or “T” for the “Status” column. This means they didn’t match (“unmatched” or “tied”) during the geocoding project. That’s why they didn’t meet the second query. It’d not clear why they met the first query.


We can calculate the Euclidean distance between schools and the closest billboards using the spatial join function. Right click on schools, go to “Joins and Relates” and then “Joins.” From the first dropdown menu, choose “Join based on spatial location.” Choose “billboards” and then the second radio button, which will generate a distance field. Give your new shapefile a name (“schools_dist_bb.shp”) and location and click “OK.”

Open the attribute table for your new shapefile and scroll the far right to see the new column “distance.” The units are feet because those are the units for the Projection system used with these data. To confirm this, look at the coordinates in the bottom right of your screen (note “feet”).


To identify the schools that are closest to billboards, right click on the “distance” field and “Sort Ascending.” This could help you identify the schools that are closest to billboards—places where you might want to intervene. Conversely, if you conducted the spatial join in the opposite direction, calculating the distance from billboards to the closest school, you could identify the billboards that are most clearly in violation of the zoning ordinance (in order to assist the Department of Licenses & Inspections with enforcement).


EXERCISE 11: GEOPROCESSING (CLIP, MERGE, DISSOLVE, APPEND) GOALS for SESSION 11 At the end of this session, participants should be able to:

Find geoprocessing tools in ArcToolbox;

Clip, merge, dissolve, and append data; EXERCISES Philadelphia MSA (Merge and Dissolve) From the PHILA_MSA folder, add all of the census tract shapefiles (these are the counties that make up the Philadelphia metropolitan statistical area). Make a map showing the median age for each county, so that you can look at the patterns across the MSA. Notice that when you create a graduated color map using natural breaks to classify the data, the range of values for each county is different. You would have to manually adjust the classifications so that the same color represents the same range of values for each county.

This is a situation where it would be helpful to merge all of the tract shapefiles together so that you could symbolize them all together. Launch ArcToolbox and use the “search” tab to find the “merge” tool. Double‐click to open the dialog box. Expand the help section. The picture on the right describes just what we want to do with our census tract files. From the first drop‐down menu, select each of the county census tract files. When you are done, count them to make sure you have them all (there should be 10). In the next field, give the new shapefile you will create a name and location. Click OK.


Try making your map again with the new shapefile. From the Symbology tab, click on “classify” and then “exclusion.” Set med_age = 0 so that areas with no population won’t be confused with areas with a young median age. Click OK. Create a choropleth map. Right click on the word “Symbol” above your color ramp and go to “properties for all symbols.” Make the outline width 0 so you can see the color patterns better.

The resulting map allows you to see the color patterns, but now you don’t know which county is which. We can dissolve the tract boundaries within the county shapefiles so that we only have the outline of the counties left. This works as long as there is a county attribute in each of the county census tract files (and there is). Launch ArcToolbox and double‐click on “Dissolve.” Choose your merged census tract shapefile from the first drop‐down menu. In the next field, give your new shapefile a name and location. Then put a check mark next to “COUNTY” in the next section because this is the field you will use as the


basis for the dissolve. Click OK. Make the fill of the resulting shapefile “No Color” so that you just see the county boundaries. If you want to label the counties, you can type them in manually using the text tool or you can create a new column in the attribute table for the county names. Notice that Philadelphia has lots of areas with the lowest age group while the inner‐ring suburban communities have higher median ages.

Albuquerque (Clip) Some of the files in the ALBUQU_base_files cover the larger county of Bernalillo, NM. Add the biketrails, majorstreets, and busroutes line shapefiles to ArcMap along with the police beats and council districts. Notice that the line shapefiles extend beyond the polygon shapefiles. In some instances, you will want to “clip” one file based on another so that they cover the same area. In this example, we can use the council.shp to “clip” the biketrails, majorstreets, and busroutes. Launch ArcToolbox and use the search tab to find the clip tool (the one marked “analysis tool”). Click on the “show help” button so that you can see a picture of what the clip tool does. Notice that the “input” layer is what will be cut (think cookie dough) and the “clip” layer is what is used to clip the input layer (think cookie cutter), resulting in a new shapefile. Choose “busroutes.shp” as the input layer and “council.shp” as the clip layer.


Give your new shapefile a name and location and click OK. Notice that the new shapefile stops at the edge of the council boundaries while the old (original) version extends beyond.

Philadelphia Schools (Append) Sometimes you may need to merge shapefiles that overlap rather than shapefiles that represent different pieces of a puzzle (as we did with the counties). For example, you might


have point shapefiles that are related and would be easier to symbolize as one shapefile. You can do this with the append tool in ArcToolbox. From the PHILA_institutions folder, add the charter, private, and public schools. The attributes for the three tables are not identical, but all of the have name and address fields. From ArcToolbox, find the union tool by using the search tab. Double‐click on “append” to open the diaolog box. Click on the “show help” button to see what is required for inputs. You must provide an existing “target” dataset for the appended map layers.

The easiest way to do this is to create a target dataset is using one of the input datasets. Right click on “phila_schools_private.shp” and go to data, then export, to create a copy of this shapefile. Rename it “target_schools.shp” and add it into ArcMap. Make the Editor toolbar visible by clicking on the Editor toolbar button. From the Editor menu, choose “start editing” and click OK. Open the attribute table for your new target shapefile, highlight all the records, click “delete” and then stop editing. Open the append tool, select public, private, and charter schools as “input datasets” and the new target shapefile as the target dataset. Choose “NO_TEST” from the Schema type, then click OK.

Your new shapefile should combine all the points into a single shapefile, each if which includes the school name. The address and other attributes were lost because they were not formatted consistently across the input shapefiles.


EXERCISE 12: CREATING DENSITY AND 3D MAPS GOALS for SESSION 12 At the end of this session, participants should be able to:

Create kernel density maps using various bandwidths and cell sizes

Open density map in ArcScene and convert to 3D

Create 3D maps using area (polygon) data in ArcScene

Create and export an animation video in ArcScene IN‐CLASS EXERCISES West Philadelphia Park

Add the five map layers in the WPHILA_Impervious folder to ArcScene. These are from the impervious surface layer for the City of Philadelphia.

Use constants in the Extrusions tab to extrude each layer to an appropriate height.

Use the “Navigate” and “Zoom in/out” tools to fly around;

Create and export an animation video. Philadelphia Intergroup Conflict

From the “PHILA_intergroup_conflict” folder, add “ig_conflict.shp;”

Create several different kernel density maps, changing the cell size and bandwidth;

Save one of the density maps

Add the density map to ArcScene and make 3D using the “Base heights” tab. TAKE‐HOME EXERCISES LA Alcohol Outlets One disadvantage of aggregating by zip codes or other administrative units is that the boundaries are not necessarily meaningful to people’s lives. Also, drawing the boundaries differently would yield different results. You can avoid these problems by creating a density map. Some people call these “hot spot” maps, but keep in mind that the “hot spots” are not necessarily indications of statistically significant clustering and may be an artifact of population density. Make sure the Spatial Analyst (raster) extension is turned on. From the “Tools” menu, go to “Extensions.” There should be a check mark next to “Spatial Analyst.” If it is not listed, you do not have access to this extension. Next, make sure the Spatial Analyst Toolbar is visible. From the “View” menu, go to “Toolbars” and make sure there is a check next to “Spatial


Analyst.” If there is, do nothing; if there isn’t, click on “Spatial Analyst.” The Spatial Analyst toolbar should now be visible. Click on the “Spatial Analyst” menu and go to “Density.”

Select alcohol outlets for “input data.” Leave the population field as “<None>.” Choose “Kernel” for “Density type,” 4,000 as the search radius, and 100 as the cell size (the units are feet because of the projection). Give the raster file a name (Alc_4000ft) and location (LA_Alcohol folder) and click OK. You should now see a raster density layer in your table of contents. Move it so it is above the block group layer but below the water and city outline layers. Make the lowest density category hollow so you only see more clearly where the alcohol outlets cluster.

How does this representation compare with the aggregate data?


To make a 3D density map, go to “ArcScene” from the start menu. Add the layer LA_zipcodes_alcohol. Double click on the layer to bring up the “Layer Properties” and click on the “Extrusion” tab. Click on the calculator icon to the right of “Extrusion value or expression” and type the formula “[Count_] / [SQMI].” Click “OK” and “OK” again. Click on the “Symbology” tab and create a graduate color map with the same variable (count of alcohol outlets per square mile). Double‐click on the word “Scene Layers” at the top of the table of contents. Next to “Vertical Exaggeration,” click on “Calculate from Extent,” then click “OK.” Add “LA_water.shp.”


Turn the zip code layer off and add the alcohol density layer. Double‐click and choose the “Symbology” tab from “Layer Properties.” Under “Show,” choose “Classified.” Create 32 classes so the color gradation is as smooth as possible. Next, click on the “Base Heights” tab. Click on the radio button next to “Obtain heights for layer from surface” and click OK.

Date post:	21-Aug-2018
Category:	Documents
Upload:	trinhthuan
View:	222 times
Download:	0 times

Workbook Exercises for ArcView 9 - Penn Engineeringese502/extra_materials/ArcView_9.3... · School...

Documents