User Guide
IS 415 – Assignment 2
Chen Wenhao
Table of Contents
1. COMPILATION OF BASELINE DATA
2. PERFORMING TRADE AREA ANALYSIS
2.1 PROXIMITY ANALYSIS
2.1.2 CREATING DRIVE TIME CATCHMENT AREA
3
2.1.3 COUNT POTENTIAL PARTNERS AND COMPETITION USING DRIVE TIME CATCHMENT AREA
2.1.4 COUNT POTENTIAL PATRONS USING DRIVE TIME CATCHMENT AREA
9
2.1.4.1 CREATING GRID 9
2.1.4.1 ANALYTICAL GRID FOR POPULATION
10
2.2 HUFF GRAVITY MODEL
2.1.3 ADDING THE DISTANCE DATA
14
2.1.4 CALCULATING THE ATTRACTIVENESS X DISTANCE SCORES
18
2.1.5 CALCULATING THE PROBABILITY GRID
19
2.1.5 DISPLAYING THE PROBABILITY GRID 20
2.3 GEOGRAPHIC PROFILING
2.3.2 POPULATION DENSITY
21
2.3.2.1 DISPLAYING THE POPULATION DENSITY CHOROPLETH MAP 21
2.3.3 LOW/MIDDLE INCOME DENSITY
23
2.3.3.1 DISPLAYING THE LOWER/MEDIUM INCOME DENSITY CHOROPLETH MAP
23
2.3.3.1 DISPLAYING THE LOWER/MEDIUM INCOME DENSITY POINT MAP
24
2.3.3 OLD AGE/AGE 50 TO 59 DENSITY 27
3
3
3
7
14
21
1. Compilation of baseline data
Compilation relevant demographic, socio-economic, potential partners, road network
and appropriate location intelligence data.
For this assignment, the following drawings are imported:
AdminBndy1, DGPSubZone, CP_Clinic, Hospital Drawing, NamedPlc, Streets, TransHubs,
and ZLevels
These drawings were projected on the SVY21 (Transverse Mercator, WGS84)
projection.
Additionally, the Census2000 data and hospital data are updated.
1.1 Census 2015
The Census2015 is created by first calculating the annual growth rate between
Census2000, and the data from the 2010 Census. Subsequently, the Census2015 is
projected using the calculated growth rate. Note that the growth rate is calculated
separately with each categories, such as income level between 1000 to 1499, and
income level between 1500 to 1999, etc.
The Annual Compounded Growth rate is calculated using the following formula:
((Ending Value/Beginning Value)^(1/# of Years))-1
1.2 Hospital
The hospital data is updated with relevant information such as the price of wards,
number of beds, and other accessibility criteria such as the number of bus services,
proximity with MRT station and Bus Interchanges. Also, only hospitals deemed as closer
competitors to Jurong General Hospital were retained.
2. Performing Trade Area Analysis
2.1 Proximity Analysis
2.1.1 Aim: This analysis aims to create a catchment area, using drive-time, to determine
potential partners, competitors, and patronages of Jurong General Hospital, and its
competitors.
2.1.2 Creating drive time catchment area
Open up the Streets drawing. We will now create node points for the drawing.
•
Fill up the following entries on the transform toolbar and click “Apply”.
At this point, node points have been added
The next step is to add in the speed information for all the roads.
•
Right click on the Project Pane > Create > Query, and create a Query named
“Query”.
•
Double click on the Query you have created
•
Insert the following into the Query window:
•
Click on
to execute the command – please follow these step when
asked to perform a SQL query from this point on.
•
Insert the following SQL statements to complete adding in all the speed
information. Note that each SQL Statement has to been executed
individually.
UPDATE [Streets Table] SET [SPEED] = 130 WHERE [SPEED_CAT] = "6"
UPDATE [Streets Table] SET [SPEED] = 101 WHERE [SPEED_CAT] = "5"
UPDATE [Streets Table] SET [SPEED] = 91 WHERE [SPEED_CAT] = "4"
UPDATE [Streets Table] SET [SPEED] = 71 WHERE [SPEED_CAT] = "3"
UPDATE [Streets Table] SET [SPEED] = 51 WHERE [SPEED_CAT] = "2"
UPDATE [Streets Table] SET [SPEED] = 31 WHERE [SPEED_CAT] = "1"
At this point, we are ready to perform the Drive-Time Zone Analysis.
•
Right click on the Project Pane > Create > Map, and create a Map named
“Asgn2_map”. Select the “Hospital Drawing 1”, “Streets Drawing”, “Virtual
Earth Street Map Image” and click “OK”.
•
Zoom into the area around Jurong General Hospital, and select the node
nearest to the hospital.
•
From Drawing, under the menu bar, select Drive-Time Zones
•
Enter the following and click “OK”
•
A polygon should appear on the map. “Cut” the polygon then right click on
the Project Pane > Paste As > Drawing. Name the Drawing “Drive Time
Analysis”.
•
Repeat these steps to create the catchment areas for Gleneagles, Raffles,
Mount Elizabeth, Renci community, Tan Tock Seng, Changi General, Mount
Alvernia, National University, Singapore General, St Andrew Community,
Ang Mo Kio Community, Parkway, Khoo Teck Puat, and Alexandra
Hospitals. Paste the created catchment into the “Drive Time Analysis”
Drawing.
2.1.3 Count potential partners and competition using drive time catchment area
Now you are going to count the number of potential partners, competitors, and
patronages of Jurong General Hospital, and its competitors.
Create a new column, using the following SQL Statement, called
“COUNT_COMPETITORS” under “Hospital Drawing 1” – this is to enable the
counting of the hospitals that falls under each catchment area.
•
ALTER TABLE [Hospital Drawing 1] ADD [COUNT_COMPETITORS] INTEGER
Add Value “1” to all the rows under that column using the following SQL
Statement
•
UPDATE [Hospital Drawing 1] SET [COUNT_COMPETITORS] = “1”
Create a new column, called “COUNT_COMPETITORS” under “Drive Time
Analysis” Table.
•
ALTER TABLE [Drive Time Analysis] ADD [COUNT_COMPETITORS] INTEGER
Repeat these steps with “COUNT_SUPPORTERS” under “GP_Clinic Drawing”
•
ALTER TABLE [GP_Clinic Drawing] ADD [COUNT_ SUPPORTERS] INTEGER
UPDATE [GP_Clinic Drawing] SET [COUNT_ SUPPORTERS] = “1”
Next, define the transfer rule for the “COUNT_COMPETITORS” field in the “Hospital
Drawing 1” Table
•
Click on the “Hospital Drawing 1” Table
•
Right click on the “COUNT_COMPETITORS” field name
•
From the context menu, select Transfer Rules
•
Complete the Transfer Rules dialog with the following:
•
Click OK.
•
Apply the same Transfer Rules with the “Drive Time Analysis” Table.
•
From the Drawing menu, select Spatial Overlay.
•
Complete the Spatial Overlay dialog as follow
Subtract 1 from the “COUNT_COMPETITORS” column under “Drive Time Analysis”
Table. This is the remove the hospital itself from the number of competitors present in
the catchment area.
•
Select the “Drive Time Analysis” Table and apply the following function on
the transformation tool bar.
•
Perform the same steps for “COUNT_SUPPORTERS” using the number of
clinics that falls under each catchment area. You do not have to subtract 1
in this step.
2.1.4 Count potential patrons using drive time catchment area
2.1.4.1 Creating Grid
In order to more accurately count the number of potential patrons under each drive
time catchment, we need to create an analytical grid, and distribute the population data
across these grids.
Let us proceed with the creation of grids.
•
Create a new Drawing and put it onto the “Asgn2_map” created earlier.
•
From View, under the menu bar, select Grid.
•
Input the following and click Create.
•
Open the “Drawing” table and duplicate the ID and rename is ID 2.
•
Put the “AdminBndy1 Drawing” onto the “Asgn2_map”.
•
From Drawing, under the menu bar, select Topology Overlay and select the
following options.
•
Click OK.
A new drawing called “AdminBndy 1 Drawing 2” will be created.
•
Rename the drawing to “Grid500m Drawing”.
2.1.4.2 Analytical Grid for Population
Now we need to link the different grids to the population data. First, we link the
DGPSubZone Table to the Census2015 Table.
•
Open the DGPSubZone Table and click on Table, under the menu tool, and
select Relations.
•
Click on the “New Relation” icon.
•
Match Key fields in “Census2015” and link the DGPSZ_CODE of both tables.
•
Get the total population (2015_TOTALPOP) data into the DGPSubZone
Table.
Next, we will import the population data from the GDPSubZone Drawing into the grids.
•
Open “Grid500m Table “, right click on the header field and select Add >
Column. Name the column, “DGPSZ_CODE” and select Integer (32-bit). Click
OK.
•
Right click on the newly added “DGPSZ_CODE” column and select Transfer
Rules. Select the following options and click OK.
•
Open “DGPSubZone Table “, right click on the header field and select
Transfer Rules. Select the same options and click OK.
•
Insert the “Grid500m Drawing “ and “DGPSubZone Drawing” unto a map.
•
Select Drawing, under the menu tool, and select Spatial Overlay.
•
Select the following options and click OK.
The newly created “DGPSZ_CODE” under the “Grid500m Table” should be filled up at
this point.
To distribute the total population across the different grids, we need to count the
number of grids that falls under each subzones, then divide the total population of each
subzone by the number of grids, thereafter, inserting the results into each cell.
•
Insert a column, “COUNT_GRIDS”, of type integer (32-bit) into “Grid500m
Table”, and into “DGPSubZone Table”.
•
Fill the column in “Grid500m Table” with value = 1. You may do so by using
the transformation tool bar, with the following options selected.
•
Apply the following transfer rule on both columns of both tables.
•
Open a map containing “Grid500m Drawing “ and “DGPSubZone Drawing”.
•
Select Drawing, under the menu tool, and select Spatial Overlay.
•
Select the following options and click OK.
•
Delete the COUNT_GRIDS column from the “Grid500m Drawing “.
•
Select the “Grid500m Drawing “ and click on Table, under the menu tool,
and select Relations. Import the “COUNT_GRIDS”, and “2015_TOTALPOP”
columns from “DGPSubZone Drawing”.
•
Create a “POP” column, Integer 32-bit, under the “Grid500m Table”.
•
Divide the “2015_TOTALPOP” by “COUNT_GRIDS” and store the results in
“POP”. You may do this by applying the following transformation steps.
Now, you may finally count the number of potential patrons under each drive-time
catchment area.
•
Create a new column “2015_TOTALPOP”, Integer 32-bit, under the “Drive
Time Analysis Table”.
•
Apply the following Transfer Rule to both the columns “2015_TOTALPOP”
and “POP” under the “Drive Time Analysis Table” and “Grid500m Table”,
respectively.
•
Open a map containing “Grid500m Drawing “ and “DGPSubZone Drawing”.
•
Select Drawing, under the menu tool, and select Spatial Overlay.
•
Select the following options and click OK.
You should see the following results.
2.2 Huff Gravity Model
2.2.1 Aim: This analysis aims to evaluate the geographical positioning of Jurong General
Hospital by putting the distribution of competitors into a geographical context, and
evaluating each location's relative attractiveness.
The attractiveness factors used in this model include the number of beds, accessibility
(which is operationalized as number of bus interchanges, MRT stations, and bus
services nearby), and price (C, B2, B1, A2, A1 wards).
It is crucial, at this point, to note that since price is inversely related to attractiveness,
we use 1 divided by price to calculate attractiveness.
2.2.2 Adding the Distance Data
To calculate the distance of each grid to the different hospitals, we first need to create
points for every grid.
•
Open the “Grid500m Drawing”.
•
Apply the following function on the transformation tool bar.
•
Cut and paste the created Centroids as a new drawing, and rename it as
“Grid500m_Centroids”.
•
Create a column, under “Grid500m_Centroid”, for each hospital. You may
insert the following SQL Statements.
ALTER TABLE [Grid500m_Centroids Table] ADD [DIST_GLENEAGLES] DOUBLE
ALTER TABLE [Grid500m_Centroids Table] ADD [DIST _ALEXANDRA] DOUBLE
ALTER TABLE [Grid500m_Centroids Table] ADD [DIST _RAFFLES] DOUBLE
ALTER TABLE [Grid500m_Centroids Table] ADD [DIST _MOUNT_E] DOUBLE
ALTER TABLE [Grid500m_Centroids Table] ADD [DIST _RENCI] DOUBLE
ALTER TABLE [Grid500m_Centroids Table ADD [DIST _TTSH] DOUBLE
ALTER TABLE [Grid500m_Centroids Table] ADD [DIST _CGH] DOUBLE
ALTER TABLE [Grid500m_Centroids Table] ADD [DIST _MOUNT_AL] DOUBLE
ALTER TABLE [Grid500m_Centroids Table] ADD [DIST _NUH] DOUBLE
ALTER TABLE [Grid500m_Centroids Table] ADD [DIST _SGH] DOUBLE
ALTER TABLE [Grid500m_Centroids Table] ADD [DIST _ST_ANDREW] DOUBLE
ALTER TABLE [Grid500m_Centroids Table] ADD [DIST _AMKH] DOUBLE
ALTER TABLE [Grid500m_Centroids Table] ADD [DIST _PARKWAY] DOUBLE
ALTER TABLE [Grid500m_Centroids Table] ADD [DIST _KHOO_TECK_PUAT] DOUBLE
ALTER TABLE [Grid500m_Centroids Table] ADD [DIST _JURONG] DOUBLE
ALTER TABLE [Grid500m_Centroids Table] ADD [DIST _ALEXANDRA] DOUBLE
After the points have been created, we can proceed to calculate the distance based on
the DISTANCE function in Manifold SQL.
•
Enter the following SQL Statements to calculate, and insert the distance
from between each point to each hospital.
UPDATE [Grid500m_Centroids] SET [DIST_GLENEAGLES] = Distance((SELECT [Hospital
Drawing 1].[ID] FROM [Hospital Drawing 1] WHERE [Hospital Drawing 1].[ID] = 211820),
[Grid500m_Centroids].[ID], "km")
UPDATE [Grid500m_Centroids] SET [DIST_ALEXANDRA] = Distance((SELECT [Hospital
Drawing 1].[ID] FROM [Hospital Drawing 1] WHERE [Hospital Drawing 1].[ID] = 211821),
[Grid500m_Centroids].[ID], "km")
UPDATE [Grid500m_Centroids] SET [DIST_RAFFLES] = Distance((SELECT [Hospital
Drawing 1].[ID] FROM [Hospital Drawing 1] WHERE [Hospital Drawing 1].[ID] = 211822),
[Grid500m_Centroids].[ID], "km")
UPDATE [Grid500m_Centroids] SET [DIST_MOUNT_E] = Distance((SELECT [Hospital
Drawing 1].[ID] FROM [Hospital Drawing 1] WHERE [Hospital Drawing 1].[ID] = 211823),
[Grid500m_Centroids].[ID], "km")
UPDATE [Grid500m_Centroids] SET [DIST_RENCI] = Distance((SELECT [Hospital Drawing
1].[ID] FROM [Hospital Drawing 1] WHERE [Hospital Drawing 1].[ID] = 211824),
[Grid500m_Centroids].[ID], "km")
UPDATE [Grid500m_Centroids] SET [DIST_TTSH] = Distance((SELECT [Hospital Drawing
1].[ID] FROM [Hospital Drawing 1] WHERE [Hospital Drawing 1].[ID] = 211825),
[Grid500m_Centroids].[ID], "km")
UPDATE [Grid500m_Centroids] SET [DIST_CGH] = Distance((SELECT [Hospital Drawing
1].[ID] FROM [Hospital Drawing 1] WHERE [Hospital Drawing 1].[ID] = 211826),
[Grid500m_Centroids].[ID], "km")
UPDATE [Grid500m_Centroids] SET [DIST_MOUNT_AL] = Distance((SELECT [Hospital
Drawing 1].[ID] FROM [Hospital Drawing 1] WHERE [Hospital Drawing 1].[ID] = 211827),
[Grid500m_Centroids].[ID], "km")
UPDATE [Grid500m_Centroids] SET [DIST_NUH] = Distance((SELECT [Hospital Drawing
1].[ID] FROM [Hospital Drawing 1] WHERE [Hospital Drawing 1].[ID] = 211828),
[Grid500m_Centroids].[ID], "km")
UPDATE [Grid500m_Centroids] SET [DIST_SGH] = Distance((SELECT [Hospital Drawing
1].[ID] FROM [Hospital Drawing 1] WHERE [Hospital Drawing 1].[ID] = 211829),
[Grid500m_Centroids].[ID], "km")
UPDATE [Grid500m_Centroids] SET [DIST_ST_ANDREW] = Distance((SELECT [Hospital
Drawing 1].[ID] FROM [Hospital Drawing 1] WHERE [Hospital Drawing 1].[ID] = 211830),
[Grid500m_Centroids].[ID], "km")
UPDATE [Grid500m_Centroids] SET [DIST_AMKH] = Distance((SELECT [Hospital Drawing
1].[ID] FROM [Hospital Drawing 1] WHERE [Hospital Drawing 1].[ID] = 211831),
[Grid500m_Centroids].[ID], "km")
UPDATE [Grid500m_Centroids] SET [DIST_PARKWAY] = Distance((SELECT [Hospital
Drawing 1].[ID] FROM [Hospital Drawing 1] WHERE [Hospital Drawing 1].[ID] = 211832),
[Grid500m_Centroids].[ID], "km")
UPDATE [Grid500m_Centroids] SET [DIST_KHOO_TECK_PUAT] = Distance((SELECT
[Hospital Drawing 1].[ID] FROM [Hospital Drawing 1] WHERE [Hospital Drawing 1].[ID] =
211833), [Grid500m_Centroids].[ID], "km")
UPDATE [Grid500m_Centroids] SET [DIST_JURONG] = Distance((SELECT [Hospital
Drawing 1].[ID] FROM [Hospital Drawing 1] WHERE [Hospital Drawing 1].[ID] = 211834),
[Grid500m_Centroids].[ID], "km")
2.2.3 Adding the Attractiveness Data
Next, we proceed to calculating the different attractiveness factors for each hospitals.
•
Create a column, under “Hospital Drawing 1”, for each attractiveness factor.
You may insert the following SQL Statements.
ALTER TABLE [Hospital Drawing 1] ADD [Attractiveness_no_of_beds] DOUBLE
ALTER TABLE [Hospital Drawing 1] ADD [Attractiveness_no_of_interchanges] DOUBLE
ALTER TABLE [Hospital Drawing 1] ADD [Attractiveness_no_of_mrt] DOUBLE
ALTER TABLE [Hospital Drawing 1] ADD [Attractiveness_no_of_bus] DOUBLE
ALTER TABLE [Hospital Drawing 1] ADD [Attractiveness_price_b2] DOUBLE
ALTER TABLE [Hospital Drawing 1] ADD [Attractiveness_price_c] DOUBLE
ALTER TABLE [Hospital Drawing 1] ADD [Attractiveness_price_b1] DOUBLE
ALTER TABLE [Hospital Drawing 1] ADD [Attractiveness_a2] DOUBLE
ALTER TABLE [Hospital Drawing 1] ADD [Attractiveness_price_a1] DOUBLE
•
Enter the following SQL Statements to calculate, and insert the
attractiveness factor of each hospital. Attractiveness score is calculated
based on the score of a particular hotel, under a specific category (e.g.
Jurong’s attractiveness score for number of beds), divided by the sum of
that score of all the hotels under that specific category, multiply by 100.
UPDATE [Hospital Drawing 1] SET [Attractiveness_no_of_beds] = [num_of_bed] / (select
sum([num_of_bed]) from [Hospital Drawing 1])
UPDATE [Hospital Drawing 1] SET [Attractiveness_no_of_interchanges] = [interchang] /
(select sum([interchang]) from [Hospital Drawing 1])
UPDATE [Hospital Drawing 1] SET [Attractiveness_no_of_mrt] = [mrt] / (select
sum([mrt]) from [Hospital Drawing 1])
UPDATE [Hospital Drawing 1] SET [Attractiveness_no_of_bus] = [num_of_bus] / (select
sum([num_of_bus]) from [Hospital Drawing 1])
UPDATE [Hospital Drawing 1] SET [Attractiveness_price_b2] = 1 / ([price_b2] / (select
sum([price_b2]) from [Hospital Drawing 1]))
UPDATE [Hospital Drawing 1] SET [Attractiveness_price_c] = 1 / ([price_c] / (select
sum([price_c]) from [Hospital Drawing 1]))
UPDATE [Hospital Drawing 1] SET [Attractiveness_price_b1] = 1 / ([price_b1] / (select
sum([price_b1]) from [Hospital Drawing 1]))
UPDATE [Hospital Drawing 1] SET [Attractiveness_a2] = 1 / ([price_a2] / (select
sum([price_a2]) from [Hospital Drawing 1]))
UPDATE [Hospital Drawing 1] SET [Attractiveness_price_a1] = 1 / ([price_a1] / (select
sum([price_a1]) from [Hospital Drawing 1]))
•
Create a column, under “Hospital Drawing 1”, called “Total Attractiveness”
ALTER TABLE [Hospital Drawing 1] ADD [Total Attractiveness] DOUBLE
•
Sum up all the different attractiveness factors and store the results under
the column “Total Attractiveness”.
UPDATE [Hospital Drawing 1] SET [Total Attractiveness] = [Attractiveness_a2] +
[Attractiveness_no_of_beds] + [Attractiveness_no_of_bus] +
[Attractiveness_no_of_interchanges] + [Attractiveness_no_of_mrt] +
[Attractiveness_price_a1] + [Attractiveness_price_b1] + [Attractiveness_price_b2] +
[Attractiveness_price_c]
2.2.4 Calculating the Attractiveness x Distance Scores
Having tabulated the attractiveness and distance scores, we may now proceed to use
them. First, we need to divide the attractiveness score with the distance scores – to get
the numerator of the Huff Model Equation,
•
.
Create a column, under “Grid500m_Centroids”, for each attractivenessdistance pair. You may insert the following SQL Statements.
ALTER TABLE [Grid500m_Centroids Table] ADD [AxD_GLENEAGLES] DOUBLE
ALTER TABLE [Grid500m_Centroids Table] ADD [AxD_ALEXANDRA] DOUBLE
ALTER TABLE [Grid500m_Centroids Table] ADD [AxD_RAFFLES] DOUBLE
ALTER TABLE [Grid500m_Centroids Table] ADD [AxD_MOUNT_E] DOUBLE
ALTER TABLE [Grid500m_Centroids Table] ADD [AxD_RENCI] DOUBLE
ALTER TABLE [Grid500m_Centroids Table ADD [AxD_TTSH] DOUBLE
ALTER TABLE [Grid500m_Centroids Table] ADD [AxD_CGH] DOUBLE
ALTER TABLE [Grid500m_Centroids Table] ADD [AxD_MOUNT_AL] DOUBLE
ALTER TABLE [Grid500m_Centroids Table] ADD [AxD_NUH] DOUBLE
ALTER TABLE [Grid500m_Centroids Table] ADD [AxD_SGH] DOUBLE
ALTER TABLE [Grid500m_Centroids Table] ADD [AxD_ST_ANDREW] DOUBLE
ALTER TABLE [Grid500m_Centroids Table] ADD [AxD_AMKH] DOUBLE
ALTER TABLE [Grid500m_Centroids Table] ADD [AxD_PARKWAY] DOUBLE
ALTER TABLE [Grid500m_Centroids Table] ADD [AxD_KHOO_TECH_PUAT] DOUBLE
ALTER TABLE [Grid500m_Centroids Table] ADD [AxD_JURONG] DOUBLE
ALTER TABLE [Grid500m_Centroids Table] ADD [AxD_ALEXANDRA] DOUBLE
•
Divide attractiveness by distance and store the results into the newly
created columns. You may insert the following SQL Statements.
UPDATE [Grid500m_Centroids] SET [AxD_GLENEAGLES] = [AxD_GLENEAGLES] /
[DIST_GLENEAGLES]
UPDATE [Grid500m_Centroids] SET [AxD_RAFFLES] = [AxD_RAFFLES] / [DIST_RAFFLES]
UPDATE [Grid500m_Centroids] SET [AxD_MOUNT_E] = [AxD_MOUNT_E] /
[DIST_MOUNT_E]
UPDATE [Grid500m_Centroids] SET [AxD_RENCI] = [AxD_RENCI] / [DIST_RENCI]
UPDATE [Grid500m_Centroids] SET [AxD_TTSH] = [AxD_TTSH] / [DIST_TTSH]
UPDATE [Grid500m_Centroids] SET [AxD_CGH] = [AxD_CGH] / [DIST_CGH]
UPDATE [Grid500m_Centroids] SET [AxD_JURONG] = [AxD_JURONG] / [DIST_JURONG]
UPDATE [Grid500m_Centroids] SET [AxD_ALEXANDRA] = [AxD_ALEXANDRA] /
[DIST_ALEXANDRA]
UPDATE [Grid500m_Centroids] SET [AxD_MOUNT_AL] = [AxD_MOUNT_AL] /
[DIST_MOUNT_AL]
UPDATE [Grid500m_Centroids] SET [AxD_NUH] = [AxD_NUH] / [DIST_NUH]
UPDATE [Grid500m_Centroids] SET [AxD_SGH] = [AxD_SGH] / [DIST_SGH]
UPDATE [Grid500m_Centroids] SET [AxD_ST_ANDREW] = [AxD_ST_ANDREW] /
[DIST_ST_ANDREW]
UPDATE [Grid500m_Centroids] SET [AxD_AMKH] = [AxD_AMKH] / [DIST_AMKH]
UPDATE [Grid500m_Centroids] SET [AxD_PARKWAY] = [AxD_PARKWAY] /
[DIST_PARKWAY]
UPDATE [Grid500m_Centroids] SET [AxD_KHOO_TECH_PUAT] = [AxD_KHOO_TECH_PUAT]
/ [DIST_KHOO_TECK_PUAT]
Next, we create a new column “SUM_OF_ATTRACTIVENESS”, and fill it with the sum of
all the attractivess-distance scores. This is the denominator of the Huff Model equation,
.
•
Create a column, “SUM_OF_ATTRACTIVENESS”, under
“Grid500m_Centroids”. You may insert the following SQL Statements.
ALTER TABLE [Grid500m_Centroids Table] ADD [SUM_OF_ATTRACTIVENESS] DOUBLE
•
Fill it with the sum of all the attractivess-distance scores. You may insert
the following SQL Statements.
UPDATE [Grid500m_Centroids] SET [SUM_OF_ATTRACTIVENESS] =
([Grid500m_Centroids].[AxD_GLENEAGLES] + [AxD_RAFFLES] +
[Grid500m_Centroids].[AxD_MOUNT_E] + [Grid500m_Centroids].[AxD_RENCI] +
[Grid500m_Centroids].[AxD_TTSH] + [Grid500m_Centroids].[AxD_CGH] +
[Grid500m_Centroids].[AxD_MOUNT_AL] + [Grid500m_Centroids].[AxD_NUH] +
[Grid500m_Centroids].[AxD_SGH] + [Grid500m_Centroids].[AxD_ST_ANDREW] +
[Grid500m_Centroids].[AxD_AMKH] + [Grid500m_Centroids].[AxD_PARKWAY] +
[Grid500m_Centroids].[AxD_KHOO_TECH_PUAT] + [Grid500m_Centroids].[AxD_JURONG] +
[Grid500m_Centroids].[AxD_ALEXANDRA])
2.2.5 Calculating the Probability Grid
We can now proceed to tabulate the probability grid for Jurong General Hospital.
•
Create a column, “PROBABILITY_JURONG”, under “Grid500m” Table.
ALTER TABLE [Grid500m Table] ADD [PROBABILITY_JURONG] DOUBLE
•
Calculate the probability of each grid based on the formula
(attractiveness(Jurong)/distance) / (sum of all attractiveness/distance)
UPDATE [Grid500m Table] SET [PROBABILTY_JURONG] = [AxD_JURONG] /
[SUM_OF_ATTRACTIVENESS] * 100
2.2.6 Displaying the Probability Grid
•
Right click on “Grid500m Drawing”, under Create, select “Theme”
•
Name the theme “Huff Model Jurong Probability”, click OK
•
Open the newly created drawing, click on “Area Background” at the toolbar
located at the top, and select Theme.
•
Fill up the following options, and click OK.
You should see the following map.
2.3 Geographic Profiling
2.3.1 Aim: This analysis aims map the potential customers of Jurong General Hospital
based on demographic details.
2.3.2 Population Density
•
Create a column, “POPULATION_DENSITY”, Floating-point (Double), under
“Grid500m” Table
•
Show the “Area” column by selecting View, under the menu tool, then
selecting Columns. Check “Area” and click OK.
•
Calculate the population density by applying the following transformation
steps
2.3.3 Displaying the Population Density Choropleth Map
•
Right click on “Grid500m Drawing”, under Create, select “Theme”
•
Name the theme “Population Density”, click OK
•
Open the newly created drawing, click on “Area Background” at the toolbar
located at the top, and select Theme.
•
Fill up the following options, and click OK.
The following map should be generated.
2.3.4 Low/Middle Income Density
For this scenario, low income is defined as income level of 1500 and below. Medium
income is defined as having an income level of 1500 to 2999. This follows the top 20
and lower 20 percent of house holds income level in Singapore.
Only lower and medium income density will be mapped since these are the more
relevant target market of B2 and C class wards.
•
Create a new “LOW_INCOME_DENSITY” column under the “DGPSubZone
Table”, and input the calculated density using the following SQL Statements
ALTER TABLE [DGPSubZone Table] ADD [LOW_INCOME_DENSITY] DOUBLE
UPDATE [DGPSubZone Table] SET [LOW_INCOME_DENSITY] = ([2015_Below1000] +
[2015_ 10001499] ) / [2015IncomeTotal] * 100
•
Create a new “MEDIUM_INCOME_DENSITY” column under the
“DGPSubZone Table”, and input the calculated density using the following
SQL Statements
ALTER TABLE [DGPSubZone Table] ADD [MEDIUM_INCOME_DENSITY] DOUBLE
UPDATE [DGPSubZone Table] SET [MEDIUM_INCOME_DENSITY] = ([2015_15001999] +
[2015_ 20002499] + [2015_25002999] ) / [2015IncomeTotal] * 100
2.3.5 Displaying the Lower/Medium Income Density Choropleth Map
•
Create the follow column
•
Apply the following transformations
•
Repeat the steps for Medium Income density
•
Repeat the steps from Displaying the Population Density Choropleth Map
to get the following results
2.3.6 Displaying the Lower/Medium Income Density Point Map
Another way of displaying the lower/medium income density is by plotting point. The
Size of the point changes as density changes.
•
Open the “DGPSubZone Drawing”.
•
Apply the following function on the transformation tool bar.
•
Cut and paste the created Centroids as a new drawing, and rename it as
“DGPSubZone Centroid”.
•
Right click on “DGPSubZone Centroid”, under Create, select “Theme”.
•
Name the theme “LOW_INCOME_DENSITY”, click OK.
•
Open the newly created drawing, click on “Point Size” at the toolbar located
at the top, and select Theme.
•
Fill up the following options, and click OK.
•
Click on “Area Background” at the toolbar located at the top, and select
Theme.
•
Fill up the following options, and click OK.
•
Repeat the steps for the medium income density map
You should see the following maps upon completion.
2.3.7 Old Age/Age 50 to 59 Density
Perform steps similar to that of low/medium income.
•
Recap for Choropleth Map: Import AGE50_59DENSITY and
AGE60_OVERDENSITY from DGPSubZone into Grid500mSubZones
•
Create new columns called 500mAGE50_59DENSITY and
500mAGE60_OVERDENSITY in Grid500mSubZones
•
Copy the two columns from DGPSubZone into Grid500mSubZones and
divide it by the number of grids in such zone.
•
Create 2 new themes, Old Age Density and Age50_59Density and set the
appropriate themes using the newly create columns.
You should see the following maps.