It is almost essential for any sales and marketing project by starting with knowing who your customer is. Mapping I will use data from census 2000 to identify the most potential market segment for JURONG GENERAL HOSPITAL and apply geographic analysis techniques to identify areas where you can find more of your best customers.
 The percentage of the elderly group reside in each subzone in Singapore . The percentage is computed by first getting the total no. of people who are 65 years old and above and use the no. of people reside in each SubZone divided by the total elderly population to derive the percentage
 The Average Income in each SubZone Area. The average income is computed by taking the average of each income group multiply by the number of population in the group for each of the SubZone in Singapore. The profile will help to identify the location with the average income which is most likely to visit JURONG GENERAL HOSPITAL for its subsidized B class and C class wards.

Step 1: Import Drawings
Import DGPSubZone.shp, GP_Clinic.shp, Sg_SVY21.shp
Step 2: Change Projection of Imported Drawing - first assign projection of the imported drawings by accepting the default values and change projection by loading from
Component Sg_SVY21.shp

Step 3: Import table Consensus2000.csv and click on table relations to link it to
DGPSubZone Drawing by matching key fields DGPSZ_CODE which is common to both
This step will bring relevant demographic, socio-economic intelligence data in Census2000.csv to be compiled with location information in DGPSubZones.shp. As such, we will have the data which is necessary for us to analyze the demographics of the trade area of interest. I have selected columns related to age and income to be shared with DGPSubZones.shp.
Step 1: Import drawing Grid500m.shp and use the transformation tool to change all objects in the drawing to centroids

This step is crucial for us to perform trade area analysis using Huff Model as the centroid provides geo-spatial data on each point of location in Singapore which we want to analyze.
Step 2: Cut the layer as paste it as a drawing in the project pane
2.2.3
Proximity Model
As from what I understand in this project, we need to study the linkage between clinics and hospitals while the former may introduce patients to the latter. One factor to access the likelihood of referral is based on the proximity between the hospital and clinics. I would be evaluating the impact of proximity in two ways - Drive Time Zones analysis and Composite Catchment Area analysis.
For Drive Time Zones analysis
Step 1: Use import drawing to load the streets.shp file which is a network of streets in
Singapore. However, the streets map has no nodes to both ends of all the lines. To add points (nodes) to the ends of all lines, use the Node Points transform operator using the
Transform toolbar.

Step 2: Select the centre point for the analysis, in the case it is the JURONG General
Hospital. Put the Streets Drawing into Map and zoom in to select the nearest road end to the hospital
Step 3: Create two new columns in Streets Drawing’s table – SPEED and LENGTH. Copy the value of Length (i) to LENGTH, this helps to improve calculation each time the Drive
Time zone is computed. The speed value is derived from the Column Speed_Cat (Speed
Category) which classifies the general speed trend of a road based on posted or legal speed and is provided to enhance route calculation and the timing of route guidance.
Value of Speed Cat
1 - > 130 KPH > 80 MPH
2 - 101-130 KPH 65-80 MPH
3 - 91-100 KPH 55-64 MPH
4 - 71-90 KPH 41-54 MPH
5 - 51-70 KPH 31-40 MPH
6 - 31-50 KPH 21-30 MPH
7 - 11-30 KPH 6-20 MPH
8 - < 11 KPH < 6 MPH
Speed in MPH
84
72
60
48
36
24
12
6
Step 4: Use the business expansion tool Drive Time Zones from Drawings to obtain the drive time to JURONG GENERAL HOSPITAL within the given parameters. This step can be repeated using different Methods (Buffer, Zone, Hull) and different parameters for zones (I have used 10 mins, 20 mins and 30 mins zones to compare the difference). The
Speed is again derived from the Speed_Cat column in the given Street.shp file.

Step 5: The output will be the Drive Time Zone Map as shown below:
Step 6: Insert the layer into the Map

I will use composite catchment area to determine which clinics are in close proximity to the hospitals respectively since clinics are more likely to introduce patients to hospitals which are nearby.
For Composite Catchment Area
Step 1: Create common buffer o 1 km around hospitals. This will be the linear distance which covers 1 km 2 size of land surrounding the hospital
Step 2: Create Voronoi Areas Around Hospitals
Step 3: Use Drawing -> Topology Overlay to create the Composite Catchment Area to outline the catchment areas of each hospital. After that, paste it as a drawing and rename it to “No_of_Clinics nearby hospital (Composite Catchment Area)”

Step 4: Create a new column in the table of GP_Clinic Drawing and the table for the drawing created in step 3. The column shall be named “COUNT_CLINICS”. Set up the transfer rule for both columns
Step 5: Use Spatial Overlay from drawing to count the number of clinics within the catchment area of the hospitals

A Choropleth map can be plotted using the number of clinics near hospital as a them to estimate the number of referals they may receive from nearby clinics. The analysis report will be in the assignment 3 submission.
2.2.4
Gravity Models - Huff Model
Huff Model defines a trade area based on its attractiveness relative to other trade areas. These models provide an approximation of business trade area by putting the distribution of all locations (including competitors) into a geographical context and evaluating each location's relative attractiveness. The formula used for this model is as below:

In our case, the measure of attractiveness of hospitals will be the number of hospital beds. The attractiveness parameter is set to be 1 while the distance decay parameter is set to be 2 (both parameters are default values for the formula since the hospital is in theory and has not been built.
It will be fair to be conservative to estimate its attractiveness and travelling distance has similar impact on the patronage by patients as compared to other hospitals)
Step 1: Insert the column No_of_beds into the the Hospitals Drawing table. The information on the number of beds per hospital is obtained on the hospital’s website.
Step 2: Create the SQL query to compute the probability of patronage to JURONG
General Hospital by Patients from all over Singapore. The query shall also include geospatial information for the query results to be projected as a drawing on the map.

Step 3: Create a drawing using the SQL query statement and rename it to Probability Grid
Step 4: From the drawing, create a thematic map using the probability of patronage the variable
To substantiate the Analysis of Huff Model, two other demographic factors have been considered: the average incomes of population reside in each subzone in Singapore; the percentage of elderly group reside in each subzone in Singapore. These two analysis aims to complement the findings

from Huff Model by identifying the customer segment who are more likely to be extracted to the new hospital
For Average Income per Subzone:
Step 1: create a SQL query to compute the average personal income per subzone area and update the DGPSubZone table with the query results. However, the SQL statement would require us to determine the average income of a particular income group. I have used the average value of the upper and lower limits of the income group to be applied as the average income for every member in the group.
Step 2: Identify the average income level on the map by creating a theme using the average income level as the variable

For Percentage of the elderly group (population age 65 and above):
Step 1: create a SQL query to compute the total population of age 65 and above and update the DGPSubZone table with the query results
Step 2: Use manifold transformation tool to compute the percentage of elderly group live in each subzone area in Singapore by dividing them with the total elderly population in
Singapore.
Step 3: Identify the percentage of elder group in each area by creating a theme using the percentage of elderly group as the variable