Geog 578Exercise 2: Neighborhood ComplaintPage: 1/10
Geog 578: GIS Applications
Exercise 2: Neighborhood Complaint
Assigned on February 1st, 2006
Due on February 15th, 2006
Total Points: 30
1. Overview:
The Other Side of The Fence is a community group in Metro Toronto that has been complaining that access to public parks is biased towards affluent neighborhoods. They have only anecdotal evidence to support these claims. Provide concrete evidence that claims are either true or false.
2. Data:
parks.tab contains the park boundaries and park names.
ea_data.tab contains the EA controids and demographic data in 1991. Following is a list of the fields for each record in ea_data.tab:
Prfedea:EA ID.
Eapop:Population of the EA.
Lon:Longitude of the EA centroid.
Lat:Latitude of the EA centroid.
C91family:Number of families in the EA.
C9134child:Number of families with 3-4 children in the EA.
C915child:Number of families with 5 or more children in the EA.
C91totdwlg:Total number of dwellings in the EA.
C91owndwlg:Number of owned dwellings in the EA.
C91sngresp:Population with single language response.
C91multres:Population with multiple language responses.
C91mtengli:Population with English mother tongue.
C91engfre:Population with English and French mother tongue.
C91engnon:Population with English and non-official language.
E90hhaggin:Estimated household average income in 1990 (number being modified).
torcsd.tab contains Toronto CSD boundary (serves as a base map).
3. Output:
1)A thematic map showing the average distance of an EA to parks using 10 classes.
2)A thematic map showing the EAs with poor access (greater than the overall average distance) and the EAs with good access (less than or equal to the overall average distance).
3)A thematic map showing the income of EAs with 10 intervals.
4)A thematic map showing the EAs with above average income (rich EAs) and the EAs with below average income (poor EAs).
5)The number of poor access EAs that are below the mean average income (poor and far) and the number of good access EAs that are below the mean average income (poor and close).
6)The size of population in EAs with below average income and poor access to parks (poor and far), and the size of population in EAs with below average income but good access to parks (poor and close).
7)The estimated household average income in 1990 for EAs with poor access and for EAs with good access to parks.
8)Answers to the questions in the text and at the end of this assignment.
4. Steps:
4.1 Spatial distribution of wealth:
4.1.1 Calculate the overall average income:
1)Open ea_data.tab as a table (change the setting for Preferred view in the Open Table dialog window to Browser)
2Use Query->Calculate Statistics to calculate the statistics for the income column (E90hhaggin) and record overall average income value on your notebook.
4.1.2 Determine the poor and rich EAs:
1)Poor EAs:
a)Query->SQL Select to bring the SQL Select window and fill the blanks as shown in Fig. 1. The query depicted in Figure is translated to plain English as:
“Select only these rows from the EA_Data table whose value in the E90hhaggin column is less than 11,114.37, and transfer all columns of these rows into a table named poor_ea.”
As you can tell from the translation, the wild card (*) in the Select Column field means all columns. Once you finish filling the blanks and checking the accuracy of your entries, click OK to execute the query. You should see a table name poor_ea pop up.
b)The data transferred into poor_ea table is not saved yet. You MUST save this table permanently. Using File->Save Copy As… to save this table as poor_ea.tab.
Figure 1: SQL Query for Selecting the Poor EAs
2)Rich EAs:
Repeat steps a) and b) in 1) for selecting the rich EAs. Store the results into a table named rich_ea. Make sure that where Condition in the query is set to E90hhaggin>=11114.37. Think what is this 11114.37.
3)Close all tables (File->Close All).
4.2 Spatial distribution of accessibility:
Accessibility to a park from an EA can be approximated by the distance from the EA to the park. An EA which has a distance to the park greater than the average distance from all EAs to the park can be considered as a poor access EA to that park. If we say that an EA has a poor accessibility to all parks, we mean that the average distance from the EA to all parks (average distance to all parks for a EA) is greater than the overall average distance from all EAs to all parks (overall average distance to parks). Otherwise, the EA would have a good access to parks.
Since there are so many parks in the Toronto CSD, it might be easier for the purpose of this exercise that the parks are grouped into park concentration regions. We then treat each park concentration region (park region here on) as a single park. Distances are then calculated from EAs to these park regions instead of individual parks.
4.2.1 Create park concentration regions:
1)Open parks.tab, and torcsd.tab. Activate the Change View dialog window by clicking the Change View () and set Zoom (Window Width) to 14. Position the map in the center of the map window using the Grabber (). You may also want to resize the map window to make it larger so that you can see the parks better.
2)Arrange TORCSD and PARKS in the Layer Control window (Map->Layer Control…) so that layer PARKS is on top of TORCSD. Make cosmetic layer editable and click OK to close the Layer Control window.
3)Select the Polygon Tool () from the Draw palette. Create 5 park regions that show areas of park concentration through screen digitizing. You may want to experiment with the polygon digitizing tool first so that you know how to create and to delete a polygon.
4)Save the cosmetic layer (which contains the newly digitized park regions) into a new file (Map->Save Cosmetic Objects…). Let’s call this new table park_reg.tab. Once you are done with the saving the file, close all database tables and re-open park_reg.tab.
5)Add two columns to park_reg.tab. The first column is for the X-coordinate of the centroid of a park region (let’s call this field X_centroid) and the second column is for the Y-coordinate (call this Y_centroid). Both the X_centroid and Y_centroid fields have the following settings:
Width:10
Type:Decimal
Decimals:6
Here is how you create these columns:
a)Open a browser to show the park_reg table (Window->New Browser Window…).
b)Table->Maintenance->Table Structure… to bring up the Modify Table Structure window. After Clicking Add Field, you should see a new field named Field2 being added. Let’s now modify the settings for this field. Change the field name to X_centroid and set the type to Decimal (as specified above) in the Field Information section. You also need to set the width and number of decimal places accordingly. Once you are done with the settings, your Modify Table Structure window should look like the one in Figure 2.
Figure 2: Modify Table Structure Window
c)To add the column for the y coordinate field, click Add Field again and follow the procedure described in b) to change the settings for that field. Once both fields are set up, click OK to execute the modification (the browser containing park_reg will disappear, which is okay). Window->New Browser Window to bring park_reg browser up (since there is only one table being opened which is park_reg, therefore the park_reg browser will automatically be opened.)
6)Update the newly created columns using the CentroidX(obj) and CentroidY(obj), respectively.
a) Table->Update Column… to bring up the Update Column dialog window.
b)Let the Table to Update field contain park_reg, the Column to Update be X_centriod, and Get Value From Table be park_reg. Check off Browse Results, otherwise it will open multiple copies of the same table.
c)For the Value field, click Assist… to bring up the Expression dialog window. From the Functions list box, select CentroidX. Your Expression window should look like Figure 3. You can click the Verify button to check if your expression is correct. The syntax of the expression should be correct. Click OK to set this expression to the Value field.
Figure 3: The Expression Dialog Window
d)Now your Update Column window should look like the one in Figure 4. Click OK to perform the calculation.
Figure 4: the Update Column Dialog Window
You now should see the X_centroid column filled with longitude values. Save the changes permanently (File-Save Table…). Repeat Steps a) through d) to update the Y_centoid column. Make sure that you select the CentroidY function from the Function list for the Expression dialog window.
7)Assign IDs to the park regions.
a)Open a map window for park_reg (Window->New Map Window…) if it not already open. Set the zoom properly so that you can see all of the five park regions (How do you do that?).
b)Select the Info Tool from the Main palette. Click a park region, the Info Tool window pops up. Click the ID field and enter 1 (you give that park region an ID of 1). Click another park region and label that park region as 2. In the same way, you should label all of the five park regions from 1 to 5. Save the changes permanently.
c)Copy down the centroid coordinates for each of the five park regions onto a piece of paper since you need them in the next section. Now close all tables.
4.2.2 Calculate the distances from each EA to each of the park regions:
1)Open ea_data.tab into a browser (set the Preferred View to Browser when you are prompted by the Open Table dialog window).
2)Modify the structure of ea_data.tab to add five columns. Let’s use Park1_dis as the column name representing the distance to Park region 1. We name the rest of the columns as Park2_dis, Park3_dis, Park4_dis, and Park5_dis. Each of these columns should be defined using the following attributes (settings):
Width:10
Type:Decimal
Decimals:4
3)Update the newly created columns with the expression Distance(X_centroid, Y_centroid, CentroidX(obj), CentroidY(obj), “km”). This Distance function is listed in the Function list box after you click the Assist... button in the Update Column dialog window. X_centroid and Y_centroid are the X and Y coordinates for a park region centroid. You have to manually enter them. For example, for Column Park1_dis, you have to enter the X and Y coordinates of centroid of Park region 1. CentroidX(obj)andCentroidY(obj)are the two functions for calculatingthe centroid coordinates of a given object, which will be EA in this case. “km” means that the distance will be measured in kilometers. For every update, you may want to check the contents of the column to see if the results are correct. After you finish the updates, save the results permanently. Close all tables and re-open ea_data.tab into a Browser.
4)Add another new column (call it Average_dis) to ea_data.tab with the same settings as the distance columns defined in Step 2. Fill this column with the overall average distance to all park regions from an EA (add Park1_dis through Park5_dis and divide the sum by 5). Save the result.
5)Use the Calculate Statistics to obtain the overall average distance (the average of Average_dis) to all park regions and record this value on a piece of paper for later use.
4.2.3 Locate all farther and all closer EAs:
1)Select all EAs which have an average distance greater than the overall average distance and save the results to far_ea.tab. (Using the same SQL Select… method you used before for determining poor and rich EAs but with different a query condition).
2)Select all EAs which have an average distance smaller than or equal to the overall average distance and save the results to close_ea.tab.
3)Close all tables.
4.3 Finding out the evidence:
4.3.1 Evidence 1:
1)Determining the number of EAs which are poor and also farther away from the parks.
a)Open far_ea and poor_ea into browsers.
b)Set the SQL Select… window to the values as shown in Figure 5.
c)Save the result as pf_ea(poor and farther EAs).
d)Count the number of EAs in the pf_eatable. This number is the number of EAs which are poor and also farther away from the parks.
e)Close all tables.
QUESTION 1: Translate the query in Figure 5 into plain English. Avoid the use of GIS and other technical jargons all possible.
QUESTION 2:Why can we say that the number of EAs in the pf_ea table is the number of EAs which are poor and also farther away from the parks?
2)Determining the number of EAs which are poor but closer to the parks
a)open close_ea and poor_ea as browsers.
b)Repeat Steps b) through c) in 1) but with proper modifications (you need to figure out what would be the proper modifications). Let’s save the result from the SQL Select… on these two tables pc_ea (poor but closer EAs).
c)Count the number of EAs in the pc_ea table.
d)Close all tables.
3)Compare the results from Step 1) and Step 2) and write down you evaluation of this evidence based on the comparison.
Figure 5: The SQL Select Dialog Window for Poor and Far EAs
4.3.2 Evidence 2:
1)Open pf_ea into a browser.
2)Compute the total population in the EAs in table pf_ea (write down the number).
3)Open pc_eainto a browser.
4)Compute the total population in the EAs in table pc_ea.
5)Compare the two population numbers and evaluate this piece of evidence.
6)Close all tables.
4.3.2 Evidence 3:
1)Open far_ea into a browser.
2)Compute the average income for EAs in table far_ea.
3)Open close_ea into a browser.
4)compute the average income for EAs in table close_ea.
5)Compare the two average income and evaluate this piece of evidence.
6)Close all tables.
QUESTION 3: What is your judgment on the complaint based on the evidence you have found? (include all of you evidence in your answer)
4.4 Composing the required maps:
The composition of a map can be divided into two steps:
1)Create a thematic map using Map->Create Thematic Map…, which you have learned during the lab session on Introduction to MapInfo.
2)Create a layout which includes the thematic map and other map components (such as title, legend, north arrow, etc.). You have also learned how to create a layout and how to add components to it during the lab session on Introduction to MapInfo. It is time for you to practice these skills.
5. Using the minimum distance as a measure:
So far we have been using the average distance to all park regions as a measure. Now let’s create a new column (Min_dis) in ea_data.tab to contain the minimum distance to park regions and use this value as a measure. Start from 4) of 4.2.2 and replace Average_dis with Min_dis. Discuss the results.
To calculate the minimum distance among a set of distances, use the minimum function. The expression you need for the Expression dialog box when you update the min_dis column is:
minimum(Park1_dis, minimum(Park2_dis,minimum(Park3_dis,minimum(Park4_dis, Park5_dis))))
QUESTION 4:Translate the above equation for obtaining the minimum from a set of values into plain English.
6. Additional Questions:
QUESTION 5:How realistic is it to use only the income as a measure to distinguish affluent neighborhoods from poverty neighborhoods (discuss it)?
QUESTION 6:Discuss the definition of accessibility? What would be your suggestions (at least one)?
QUESTION 7:Discuss the aspects of this project which potentially contribute error into the final conclusion about the complaint.
QUESTION 8:Using the available data, develop a new metric for assessing the problem and attaining the study objectives. Does your new metric change your conclusions/evidentiary outcomes? Does this metric have a spatial bias? Elaborate.
End of Exercise 2. A Useful Case?