User Manual for SQL Query Analysis on
Jurong General Hospital
Files included
The following components are included in the Manifold Map file.
DGPSubzone Drawing Original is the choropleth drawing with census data (regions with more
households with less than 3k income are in darker green)
Hospital Drawing shows all the location of all hospitals with services or target audiences similar to
Jurong General Hospital. This data is acquired from Navtaq. Specialized hospitals like Mental Health
Institution are removed.
Hospital Gravity is a choropleth map showing the probability that those staying in different grids will
visit Jurong General Hospital
HouseholdGrid is the analytic grid derived from DGPSubzone Drawing Original. This divides the
population in the subzones into equally sized grids (1km by 1km) for easier analysis. Grids with no 0
households with income < 3k are removed from this drawing. The is the analytic grid used for our
analysis.
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Google Maps Street Map Image is an image provided by Google Maps
Map overlays Google Maps Street Map image, HouseholdGrid, Hospital Gravity and Hospital
Drawing to provide an overview of all the hospitals and analytic grids
All the items starting with the word “Generate” are all Manifold Spatial SQL queries to generate
useful data for our analysis. Their use will be detailed below
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Census 2000 is a table of the year 2000 census data which was used to derived the number of
households with income of less than 3k within different subzones.
Usage
Visual Analysis of density of potential customers across Singapore
Double click on “Map”
The darker green area represents analytic grids with higher density of households with income less than
3k living in the area.
Proximity Analysis
Finding hospitals there is closest to each analytic grid
When the Manifold Map Project is open, double click on “Generate nearest hospital” under “Project” on
the right side to open the query window.
Then, click on the exclamation mark to execute the query. A table will then be generated.
Column Name
Id
Below_3K
POI_Name
What is it?
ID of Analytic Grid
Number of household
with income below 3k
within that grid
Name of hospital closest
to that grid.
Generate number of households with a particular hospital as their nearest hospital
Building on the earier SQL query, we want to find for each hospital, what is the number of households
with income less than 3k living within the analytic grids with that hospital as its nearest hospital.
In other words. Grid 288742 and grid
288743 has Ang
Column Name
What is it?
Mo Kio Hospital as their nearest
hospital. We
Hospital_name
Hospital name
want to sum the number of households
with income < 3k
living within these 2 grids. This is done
with the
assumption that all hospitals are perfect substitute of each other and households would visit the
hospital nearest to their grid.
To compute this result, double click on “Generate number of households near each hospitals” and click
on the exclamation mark to execute the query.
The result tables should look something like this:
households
Number of
household with
income below 3k
with this hospital
as their nearest
hospital
Distance Metric
To generate a pivot table with the distance between all the grids and hospitals, double click on
“generate distance metric table” within the project pane and click on the exclamation part on the top
left of the window to execute the query. The resulting table should look something like this:
Column Name
What is it?
The rows corresponds to each analytic grid while the columns corresponds to each hospital. To find out
the hospital name of each hospital id, double click on the “+” sign beside “Hospital Drawing” to expand,
and then double click on Hospital Table.
id
POI_Name
Hospital id
Hospital name
Attractiveness
Attractiveness
value of the
hospital
Street name of the
location of the
hospital
ST_Name
Gravity Model Analysis
The probability that households staying in each grid will visit Jurong general hospital is generated using
Huff’s Model.
Hospital attractiveness is derived from this source
http://www.mytopdozen.com/Best_Hospitals_of_Singapore.html which ranks hospitals in Singapore
according to their occurrence in online news, polls, photos and other relevant media. Hospitals with no
ranking data from the source are given an arbitrary attractiveness value of 100 to present that these less
popular hospitals share the same attractiveness value.
To generate a pivot table with the attractiveness value of all hospitals to all analytic grids, double click
on “Generate gravity model table” from the project pane to open the query and then click on the
exclamation mark on the top left of the window to execute the query. The resulting table looks like this:
This is similar to the distance metric table except that the values are now Pij instead of distance between
hospital and subzone.
To visually inspect the gravity model of the Jurong General hospital, right click on the column named
“4528” and press copy. Then, click on the “+” sign beside “Hospital Gravity” in the project pane to
expand it out.
Double click on Hospital Gravity Table, click on the the column labeled “HUFF” and paste.
As all these probability values are very small number and hard to analyze, we can artificially increase
these value by multiplying them by 1000 to make it easier to understand. This is done by typing the
following into the bottom of the window and pressing apply.
Resulting table looks like this:
To visually view the resulting choropleth gravity model together with the location of all the hospitals in
Singapore, double click on “Map”
At the bottom of the window, double click on “HouseHoldGrid” to hide the grids and double click on
“Hospital Gravity” to show the choropleth gravity model grids.
The color of the analytic grids represents the probability of households going to Jurong General Hospital.
The darker the green color, the higher the probability.