2. Agenda
◦ Introduction to Problem
◦ Data collection and data sources
◦ Data preparation and cleaning
◦ Process Steps
◦ Geospatial Analysis
◦ Geovisualisation
◦ Conclusion and Future Scope
4. Blood Donation Facts in Singapore
◦ Facts: Only 1.87% of total Singapore’s population donated blood in 2016.
◦ This compares with over 3% in countries like Australia and the United States
◦ Most blood was collected through Blood Mobiles.
◦ Anecdotally, Singaporeans treat blood donation as ‘outings’
Sources: Singapore HAS –Health Services Authority
WHO Global Database on Blood Safety (GDBS) for the year 2011
5. Data Sources
List of Blood banks (https://data.gov.sg/)
Hospitals (http://download.bbbike.org/osm/bbbike/Singapore/)
MRT stations and Bus stops (https://www.mytransport.sg/content/mytransport/home.html)
MRT routes (http://download.bbbike.org/osm/bbbike/Singapore/)
Planning Area Boundary (https://data.gov.sg/dataset/master-plan-2014-planning-area-boundary)
Streets (http://download.bbbike.org/osm/bbbike/Singapore/)
Location of points (IVLE)
DataSet Description Data Source
Datapoints of all class students IVLE (Apps used : Openpaths,Moves,FollowMe)
Singapore Planning Area subzones Boundary https://data.gov.sg/dataset/master-plan-2014-planning-area-
boundary
MRT stations and Bus stops https://www.mytransport.sg/content/mytransport/home.html
MRT routes http://download.bbbike.org/osm/bbbike/Singapore/
Singapore Streets http://download.bbbike.org/osm/bbbike/Singapore/
Hospitals in Singapore http://download.bbbike.org/osm/bbbike/Singapore/
List of Blood banks in Singapore https://data.gov.sg/
6. Data Preparation & Cleaning
◦ The data points retrieved from the class was merged and
several points found outside Singapore were removed as
part of Data Cleaning.
◦ Data in CSV format files was converted to SHP file formats
◦ The data for streets and MRT lines included routes that
are abandoned or under construction. The data was
selected accordingly using the feature selection tool in
ArcGIS Desktop.
◦ The MRT lines have been further coloured based on the
colour codes of MRT lines in Singapore.
◦ Made use of Definition query to retrieve relevant Sub-
Zones, Hospitals, Primary streets and expressways.
◦ Scale Range was fixed at 1:50,000 for Roads and 1:15,000
for bus stops.
7. The Input-Process-Output
Data Layers
From
Input data sources
Students’ location data
points,
Singapore subzones,MRT
routes,hospitals,bloodbanks
INPUT
PROCESS
Software Tool Used: ArcGIS Desktop
Exploratory Analysis
ArcToolbox:
Spatial Statistics Tool
GeoSpatial Analytics:
Average Nearest Neighbor Analysis
Optimised Hot Spot Analysis
OUTPUT
Geovisualisation
ArcToolbox:
Tracking Analyst Tool
Explanatory
8. Plotting of data points using ArcGIS
• The black points shown
here are the location data
of students.
• Insight : Visually,the data
points appear clustered in
the regions in and around
NUS-Kent Ridge.
• To prove this statistically,
Average Nearest neighbor
analysis is done.
Layer 1 : Data points for Geospatial Students in ISS
9. Nearest Neighbor Analysis Result
• To study the spatial distribution of the layer1
data points we performed a Nearest Neighbor
Analysis.
• Tool Used: Spatial Statistics Toolbox
• Interpretation: The Average Nearest Neighbor
Summary report shows that the z score is
significant as it is less than the critical z score
at 99% confidence.Thus the pattern of data
points is significantly clustered.
10. Plotting of points (cont…)
Layer 2 : Addition of MRT lines over the data points.
• Upon further analysis,
we found that most of
the data points lie along
the MRT lines.
• Insight: It appears that
many students
commute by MRT
because many data
points lie on the MRT
lines.
11. Optimized Hot Spot Analysis – Getis ord G
Insight : The result of Optimized Hot
Spot Analysis shows that there exists
a hotspot around NUS - Kent Ridge
area.
Tool Used: Spatial Statistics Toolbox
12. Hotspot Analysis comparison
• Compared to sample sizes of the data points the hotspot analysis shows different results.
• Insight: Due to the difference in significance of cluster sizes with respect to the sample size of the
datasets, hot spots analysis shows different results.
Consolidated Data: Our Team Pixel’s data:
13. Hospital and Blood banks
Layer 3 : Adding hospital and blood banks
• Geovisualisation
• The black cross represents the
4 blood banks in Singapore
• The red symbols are the major
hospitals and clinics in
Singapore.
14. Geofences around Hospitals’ Subzones
• The subzones in color are
the virtual geofences.
• Hospitals have a virtual
fence around the sub-zone
in which it is located.
• Tracking analyst tracks the
people entering the fence.
15. Geovisualization using Tracking Analyst tool
• Tracking analyst shows the
temporal changes in the
feature classes.
• The data points entering
the geofence can be seen
as green points.
16. Geovisualization using Tracking Analyst tool
• Data points entering the
geofence are highlighted.
• Geonotifications can be
sent to the datapoints
within the geofence during
emergency blood
demands using GeoEvent
server and GeoTrigger
service of ESRI.
17. Conclusion & Future Scope
◦ ArcGIS Desktop tool was explored for Spatial Analytics and Geovisualisation
◦ Blood Collection through blood mobiles can be maximized if the mobile drives
are sent to the places where clustering of people is periodic.
◦ The complete prototype development for emergency blood notification
service using ArcGIS needs to be further worked upon.
◦ This idea is in line with Singapore’s vision of Smart nation development. With
an increasing aging population, the blood demand management is an area of
study for Health Services Authority Singapore,.
18. References
No license for GeoEvent server ArcGIS Desktop.
GeoProcesor in the GeoEvent server can be used to implement the GeoFence
