Analytics And Intelligent Systems

NUS ISS AIS Practice Group

Balanced Scorecard : Public Service Division eGovernment Initiative — November 13, 2016

Balanced Scorecard : Public Service Division eGovernment Initiative

Selection of Agency

For our assignment on the Balanced Scorecard, we chose the Public Service Division (PSD) under the Singapore Government Prime Minister’s Office.

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Strategy Map

We looked at PSD’s approach to enable a future-ready Public Service that leverages on technology and data; eventually focusing on eGovernment.   After analysing available eGovernment information online, we attempted to create PSD’s Strategy Map and a identified some possible indicators for this initiative.

We found a lot of data and information across various Singapore Government websites.  Arising from the fact that numerous eGovernment projects are currently ongoing, we were able to create a rich dataset from which to draw our dashboard.  From our four indicators, we developed 12 indicators that would allow our stakeholders to view the progress of eGovernment initiatives from four perspectives:

  • eCitizen
  • eBusiness
  • eGov Capabilities
  • e-Collaboration across agencies

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eGovernment Dashboard

Our initial dashboard was too complex – utilising too many different types of charts, presenting too much information, and inadequately highlighting the important indicators.

To address the above areas for improvement, we reduced our indicators from 12 to seven, simplified the format and the charts used, as well as highlight the more important indicators.  The dashboard was created using Tableau Desktop.

Team Members

Dr Acebedo, Cleta Milagros Libre
Jun Yu, Thomas
Shen Shutao
Sulaiman Ahamed Moosa

Disclaimer:  This project is purely for academic purposes only, in partial fulfillment of the requirements of the NUS ISS MTech Enterprise in Business Analytics – Data Analytics Module. Our work was based on third party information and we do not claim any authority on the subject matter.  Authenticity of third party data was not validated.  The data and resulting dashboard should be treated to contain dummy data for an academic exercise.

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Earthquake Patterns and Impact: A Geospatial Perspective —

Earthquake Patterns and Impact: A Geospatial Perspective

A.  Selection of Global Phenomenon

Considering recent series of disasters with global impact, our team was immediately drawn to pursue something related to one of the natural disasters for our assignment on Geospatially Enabled Analysis and Visualisation of a Global Phenomenon.  After exploring the internet for data sources on disasters, and experimenting on various tools (Carto, SPSS, Tableau, and ArcGIS for Desktop), we eventually decided to look at earthquakes – to apply the concepts of geospatial analytics and geovisualisation to analyse earthquake patterns and its impact.

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B.  Geospatial Data Collection

We discovered and downloaded a lot of information and databases on earthquakes, but realised that many of them are not compatible and readily useful.

Using Excel and ArcGIS tools, we did geoprocessing – including cross-validation across sources, conversion of data types, data transformation (geo-referencing, geocoding) as well as creating layers and joining/relating databases.

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C.  Geospatial Analytics & Visualisation

Earthquake Patterns

To analyse patterns, we focused on the geographical location, distribution, and temporal changes of earthquakes.  Using geospatial analytics tools, our analysis enabled us to address the following queries:

  1.    Where do earthquakes usually occur?
  2.    Are they spatially clustered or dispersed?
  3.    Where are earthquakes strongest?
  4.    Are big earthquakes concentrated in specific locations?
  5.    Why do they occur at these locations?
  6.    How has earthquake pattern changed over time?
  7.    How has earthquake pattern changed over space?

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Earthquake Impact

Our study of the impact of earthquakes, using geographical association & interaction tools, allowed us to answer the following queries:

8.     Which countries are more susceptible to big earthquakes?
9.     What are the consequences of earthquakes?
10.   What other hazards occur with earthquakes?
11.    How do we prepare for earthquakes?

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D.  Conclusion

Our study made us realise that earthquakes are ongoing all the time.  Although many are too small to be felt; we need to stay vigilant and know what to do when a big one happens.

Web Map

All maps used in this report were generated by the group.  The map is available at ArcGIS online; it can be downloaded and opened using ArcGIS for Desktop.

Earthquake, an EBAC Geospatial Project 

http://www.arcgis.com/home/item.html?id=8eae0fe44d1e47dca468234da2c9fa54#overview

Team Members

Dr Acebedo, Cleta Milagros Libre
Jun Yu, Thomas
Shen Shutao
Sulaiman Ahamed Moosa

Disclaimer:  The maps are purely for academic purposes only, in partial fulfillment of the requirements of the NUS ISS MTech Enterprise in Business Analytics – Data Analytics Module. Our work was based on third party information and we do not claim any authority on the subject matter.  Authenticity of third party data was not validated.  The maps should be treated to contain dummy data for an academic exercise.

Finding Interconnected Road Segments From LTA Speed Band Data —

Finding Interconnected Road Segments From LTA Speed Band Data

In the recent Sense Making and Insights Discovery project, project teams were tasked to analyze data from publicly available datasets like the LTA data mall and come up with EPN (Event Processing Network) designs to reduce the occurrences of traffic jams.

One area that our team wanted to explore was to look at how we can creatively exploit the speed band data to find interconnected roads and segments which can then be used in other areas of analysis or computation. For example, in a routing algorithm or finding correlations between neighboring segments of roads.

The speed band data is updated in five minutes’ intervals. The response contains attributes such as the road name, road category, speed band information and start and end location in latitudes and longitudes. Every road is made up of one to many segments and long roads like expressways may be broken up into hundreds of segments. There are more than 50000 segments in the whole of Singapore. We imported one slice of data into PostgreSQL database with the PostGIS plugin enabled, creating a line string geometry field out of the start and end latitude and longitude from the data.

To easily visualize the data in our database, we can export a shapefile from any tables in PostgreSQL with a geo column using the bundled PostGIS Shapefile Import/Export Manager tool. We can then import it into ArcGIS or MapShaper (http://mapshaper.org/).

map1

In our first attempt, we realized that there are some gaps between roads. To help the intersection work better, we created a three-meter buffer around each road segment line string. The effect can be seen from the zoomed in figure below.

intersections

Intersections can now be easily found by running the ST_Intersect SQL command on the table. (http://postgis.net/docs/ST_Intersects.html)

Having found intersections, we proceeded to generate a graph database for both road and road segment levels. We used the python library networkX (https://networkx.github.io/) for creating the graphs and Graphviz (http://www.graphviz.org/) for visualization.

The road graph below is created using Graphviz using the sfdp (scalable force directed placement) command to layout the nodes. It contains all the roads in Singapore and contains about 4000 nodes. The red nodes are expressways and the green ones are major arterial roads.

graph

With the graph object, we can now perform some queries easily, for example if we wanted to find the roads that are connected to Heng Mui Keng Terrance (the road outside ISS):

neighbors = G.neighbors(‘HENG MUI KENG TERRACE’)

We can also get the segment level view.

graph2

After all the pre-processing, we are now ready to make use of our graph objects. In our EPN, we have proposed using the speed band information to help in routing vehicles in the event of a traffic jam or accident. If you look at the road segment graph, you can see that compared to the road graph, it is both directed and weighted. As we can easily find the distance for each road segment and we also have the speed band at any given time from the speed band data, we can use this information to derive the estimated time to travel across the segment at any given time. Using this as the weight, we can then find the shortest path (in terms of time to travel) between any two nodes in the graph using any shortest path algorithm like Dijkstra’s algorithm.

Another analysis that we can do is that we can find correlations between neighboring road segments in the graph to derive some insights. As mentioned earlier, there are more than 50000 road segments and it might be infeasible to perform correlation analysis on all the segments, especially since we are looking across time. For example, we might collect data across five days which results in 1440 data points for a single segment. It would probably make more sense (statistically and computationally) to zoom into clusters of segments of interest and look at correlations there, which can be done by getting n degrees of neighbors.

This is a short excerpt from our project and highlights some of the work done, thanks for reading.

By Team 5 (Chan Chia Hui, Randy Phoa and Zay Yar Lin)