Analytics And Intelligent Systems

NUS ISS AIS Practice Group




Source: Click here for the paper

Submitted by:

Team Incognito

Pankaj Mitra (A0163319E), Deepthi Suresh (A0163328E), Anand Rajan (A0163200B) and Neeraja Lalitha Muralidharan (A0163327H)

Spatial-Temporal Analytics with Students Data to recommend optimum regions to stay — May 7, 2017

Spatial-Temporal Analytics with Students Data to recommend optimum regions to stay

Objectives & Motivation:

Objective is to find a convenient place for students to stay based on data collected in Singapore

To any international student coming into Singapore needs robust information about convenient places to rent. To proceed with this we analyzed student’s movement data which was collected by students of NUS belonging to ISS school, exploratory spatial data analysis was done on that to find the pattern and insights. We considered that as a student basic amenities would be economical stay, cycling path, library, MRT closeness, parks, hawker center.

Data Sources:

Dataset description Data Source URL
Data points of all NUS-ISS  students IVLE (Apps used: Openpaths)
Dwelling data
Hawker Centres
Cycling Path
Park Connector Loop




OpenPath data with student’s personal location information was collected for the month of April. Data Cleaning was done to refine the data. As the data was stored on multiple mobile devices the date and time formats were inconsistent, thus, all the date and time field values were converted to a single standard format. To do more analysis, separate columns were created for date and time. The outliers were treated using R and the data points outside Singapore were ignored. Modified Name and MailID columns to obtain missing details using EXCEL.

Exploratory Analysis of Students Data (with derived fields):

Mean Centre:


From the above exploration, it is evident that most student stay at the university, travel to the residence and they use MRT most of the time. As students would prefer to stay near university, place near to MRT station and have the potential to use Cycling path, our aim is to find and suggest better zones for student life. Thus, we need to add these layers to the student data and carry out analysis.


To get more insights into a suitable dwelling, we tried to separately analyze various datasets such as HDB dwelling population data, Hawker center location, MRT station, and cycling paths.

Geographically Weighted Regression:

To find insights from student data using HDB and population data layer, geographically weighted regression is performed using variables as shown below with an assumption that data points having the timestamp of late night represent student’s home. Spatial join was done on hawker center, dwelling and open path student data layers to obtain final GWR model.

Explanatory Variables:

The count_  variable is the number of student data points per polygon, Count_1 is the number of Hawker centers per polygon, showsSHAPE_Area of the dwelling layer and HDB the total count of HDB per person.

GWR Model

The GWR results show that model can perform with moderate accuracy having adjusted R2 of 0.31.

Spatial Autocorrelation (Moran’s I) tool on the regression residuals was run to ensure that the model residuals are spatially random. Statistically significant clustering of high and/or low residuals (model under- and overprediction) indicates that our GWR model is not accurate enough to predict.

GWR Fail

The below map indicates regions with localized R square to find HDB population using students data. The Dark Red regions are the places where the model predicts with higher accuracy and the blue regions are the place where its prediction is poor.


As the model residuals are not random based on the spatial autocorrelation ( Morons I ), it cannot be used for prediction purposes. This model was just build to study the insights of students data with another layer of data.

Thus, to find convenient places for students, the places were ranked using student data points and factors like MRT, cycling path and hawker centers availability.


Ranking zones based on Hawker Centres:

To get a density of hawker centers in each we calculated a new field which was used for ranking:

Ranking of the classified field by using reclassify tool to rank the regions based on the hawker center density which shows places ranked according to economic zones for food.


Ranking zones Based on proximity to cycling paths:

The areas are ranked as per its proximity of cycling paths and data is converted to raster data and the ranked.


Ranking zones based on MRT by reclassify:

The easy access to public transport is considered one of the major consideration while choosing a place to stay and have used proximity to MRT as a factor to rank areas. MRT location data is converted to raster data to rank areas based on its proximity to MRT stations


 Final Ranking:

The final rank for each zone in Singapore was calculated based on the average of other three ranks (MRT, Hawker center, and Cycling path). Areas which are ranked good can be most favorable for staying. This final ranking can help to choose a place for staying based on individual priority.


 Final Rank




Using the final ranking, we can recommend to a new student coming to study at NUS a convenient place to stay, considering MRT, Cycling and Food places in the ranking.

As expected the better ranking zones are crowded near NUS itself and there are other places also being suggested by the ranking.

As a future scope of this story, we can add a configurable element which can replace Hawker center layer with many another layer like Libraries, Parks, HDB rental prices, Bus stop layer to form an ideal tool for the upcoming student to use it.

For detailed analysis, please read the entire report: – Spatial-Temporal Analytics with Students Data

Explored and submitted by: 





LI MEIYAO (A0163379U)




To find most accessible study areas for students in NUS

Problem Space:


Analysis Strategy:

  • What? – Availability of study areas for students
  • Where? – Inside NUS campus
  • Why? – The various reasons for the preferred locations
  • How? – Finding the clusters around various study locations

Data Exploration and Feature Addition:

  • Data source is OpenPaths
  • The class data was cleansed and the records pertaining to geographical coordinates of Singapore/NUS was chosen
  • The initial analysis of the sampled data was performed using various tools such as R , carto, ArcGIS to study the geographic spread
  •  A new dataset was formulated for representing the various study centres in NUS
  • Transformation was performed to achieve the variables in the necessary format for the geo-visualization
  • Reverse geocoding was performed on the dataset using the ‘ggmap’ package in R and the corresponding locations were obtained

Preliminary Analysis:


  • Carto was used to analyse the spread of the data points during the class hours and after the class hours
  • The results of the analysis portrayed that after the class hours the population spread is more at University town owing to availability of more study areas and facilities



Base Map Creation and Polygon Generation:


Addition of Layers:


Step 3: Loaded class data (master class namely) and converted coordinates for visibility

Density Analysis:



Step 6: The high-density area was in and around National University of Singapore. We can conclude that the data points are either working in NUS or students of NUS.

Assumption and Addition of new Layer:


Hot Spot Analysis:

Step 8: Hot Spot Analysis was performed, Arc Tool Box->Spatial Statistics Tools->Mapping Clusters->Optimized Hotspot Analysis


Model Diagram – Proximity Analysis:


Step 9: Proximity Analysis was performed to find most accessible study areas for students in NUS, Arc Tool Box->Analysis Tools->Proximity->Near

Proximity Analysis Results:


Inferences/Solution Outline:

  • Comparing the inference obtained from CARTO and Model built in ARCGIS, We can find that the students only focus on University Town
  • From the model it is evident that there were other study areas that could be preferred as the data points were close to these study areas
  • The students can explore other areas like FOE,SOC etc for holding discussion sessions
  • We can propose a new study area at an optimal location based on the geographic distribution of student data in case the number of students enrolled increases over a period of time


  • It’s a student’s location data
  • Sample size is limited to ISS students only
  • Non availability of accurate shuttle bus timings data
  • Non availability of students enrolled in each and every faculty

Team Name: Incognito

Team Members: Pankaj Mitra, Deepthi Suresh, Anand Rajan, Neeraja Lalitha Muralidharan, Nandini Paila Reddy

Delay Estimation in Pedestrian crossing — April 29, 2017

Delay Estimation in Pedestrian crossing

Team Name: Incognito

Team Members: Deepthi Suresh, Neeraja Lalitha Muralidharan, Pankaj Mitra, Sindhu Rumesh Kumar


This study uses multiple linear regression:

1.To provide theoretical support for traffic management and control

2.To increase efficiency at intersections and improve security

One Page Journal Summary “Estimates of pedestrian crossing delay based on multiple linear regression and application” authored by Li Dan and Xiaofa Shi.

Journal_Pedestrian Crossing

Click here for Journal.

Analysis on Workplace Injuries — March 17, 2017

Analysis on Workplace Injuries


The objective of this dashboard is to demonstrate different levels and types of injuries caused in the workplaces of Singapore.


Analysis on Workplace Injuries – Analytics And Intelligent Systems


  1. In which industry majority of injuries occur?

Majority of injuries occur in the Construction and Manufacturing industry consistently over the years but in the year 2016, the injuries pertaining to “Accommodation and Food Services” has also risen.

  1. With the current scenario in workplaces, which type of injury occurs more frequently?

The number of minor injuries (94.9%) greatly exceeds the number of major (4.5%) and fatal (0.5%).

  1. Is there a trend seen in the number of injuries caused in the industries through the years 2014-2016?

The fatal and major injuries are consistent through the years, while the number of minor injuries dropped in 2015 and increased drastically in 2016.

  1. What are the common types of minor injuries across different industries?

Injuries caused by slips, trips and falls are the maximum across industries.  Injuries caused by cuts or stabs by objects is more at the Accommodation and Food services industries, while workmen at Construction and Manufacturing industries are more affected by moving objects.

  1. Why has minor injury increased drastically from 2015 to 2016 while fatal and major injuries are consistent?

A good measure has been taken to avoid the fatal injuries yet minor injuries have not decreased. This could be because of the conversion of fatal injuries to minor injuries.


Final Analysis

We can clearly see that fatal and major injuries are very low and steps have also been taken to reduce this further, there is still a rise in the minor injuries. This may be because the fatal injuries have been converted to minor injuries.

Struck by moving/falling objects is one of the common cause of minor injury in Construction as well as Manufacturing industry, so to suggest a common solution for this, we could make use of Wireless sensors (heat and motion) on the objects which make an alert sound when it reaches within 50 m of any human being. This way the people working at the site will be aware of any moving objects in their vicinity and move out of harm.

The injuries may be minor, but if the injuries occur simultaneously to multiple people, it may affect the overall productivity of the company.


Tableau Public link:

Workplace Injuries in Singapore(2011-2016)

Submitted by:

Pankaj Mitra (A0163319E)

Deepthi Suresh (A0163328E)

Neeraja Lalitha Muralidharan (A0163327H)

Kriti Srivastasa (A0163206N)

Sindhu Rumesh Kumar (A0163342M)


Singapore Personal Crime Statistics — March 19, 2018

Singapore Personal Crime Statistics

GQM Framework Storytelling


  • The Police department of Singapore
  • Law ministry
  • Crime Statistics wing


  • Find the insights for Crime data using visualization capabilities which otherwise is difficult to look directly from the data
  • Identify and define key performance indicators and suggest action items

Key Questions

  • How is Singapore placed against other major cities
    • Overall Singapore is ranked 1st in terms of Personal security and 2nd for overall security in the world from Safety Index 2017
  • What is the current crime statistics of Singapore
    • Overall crime has controlled in past 6 years with conviction rate down as much as 46%
  • What is the demography of the people in penal admissions
    • It is found that locals involvement is found to be higher than the foreigner coming to Singapore for work or other purposes
  • What are the major offenses in Singapore
    • Drug and property related crimes are highest
  • Money Lending cases hotspots
    • Woodlands tops the chart in Moneylending and Moneylending Harassment cases

KPI Metrics

  • Crime rate
  • Offense type distribution
  • Over the year change in crime
  • Over the year change in Remand
  • Penal admission by age
  • Penal admission by gender
  • Penal admission by education
  • Total Moneylending cases

Key Findings

  • Woodlands is the hotspot for Money Lending cases
  • Drug offenses and Property Crime tops the list
  • Secondary Level offenders are much higher than primary or no Education
  • Past 10 years conviction has decreased where remand has increased



The dashboard can be accessed at the public tableau

Data Sources:

Team Neo:

Anusuya Manickavasagam (A0163300Y)

Kesavan Sridharan (A0163207M)

Muni Ranjan (A0163382E)

Pradeep Kumar (A0163453H)

Supply Chain Analytics — Optimization for Small Retail Firm — December 19, 2017

Supply Chain Analytics — Optimization for Small Retail Firm


Supply chain optimization is critical to ensure the optimal operation of a manufacturing and distribution supply chain, it basically refers to the minimizing of operating cost whilst meeting the demand of downstream customers. Nowadays, data analytics is widely used in supply chain management and optimization to make better business decisions. It is the science of examining raw data to help draw conclusions about information. The supply chain is a great place to apply analytic theories to look for a competitive advantage, because of its complexity and also because of the prominent role supply chain plays in a company’s cost structure and profitability.

In this research, the data set is simulated according to real business situation, it represents the historical data of a small fictitious retailer, including files of items, orders, purchase orders, suppliers, BOM, etc. Below is the dashboard of the data set.


The optimization is conducted based on the pull strategy and agile theory. Agile is defined as a strategy that is more responsive in a volatile market place, where this strategy is totally demand driven. As consumers buying patterns are changing on a very rapid pace, so does the whole supply chain management changes. The fundamental drivers of agile supply chain are Speed, Cost and Efficiency. A pull strategy is when customer demand drives the entire production process. Long term customer demand forecast is accomplished. The advantage of using pull strategy is that it has higher service levels, lower carrying costs, decreased inventory levels, and fewer markdowns. We then further conduct inventory optimization including ABC analysis, stock reward function calculation, and route optimization on optimal route design.

Demand Forecasting

Demand forecasting is important for the other parts of supply chain management. As the retailer, we need to do the operational short term demand forecasting. For this project, we mainly made the quantity prediction of each item. As our data records covering the time   period from 2013 January to 2016 September, we carried out the demand forecasting of 2016 October, November and December. The data structure is shown as below.


Data includes the order records of each product items in three different warehouses and on different date, each warehouse has the same 106 items. As it shows, for each item it has very little order quantity, so the data was rearranged as monthly orders. The three locations of the warehouses are Chicago, New York, Los Angeles. To simplify the introduction of our forecasting model, New York is chosen as an example to do the later forecasting and later inventory optimization. A quantitative model is built to predict future demand of each item. Following figure is the graph of one item in one location during the round 3 years’ records.


As it can be seen, the trends of one product is not so obvious. Then some traditional machine learning methods were applied, however the performance is not satisfied, compared with time series model, we tried doing the prediction, but the accuracy is not as good as expected.

The reason might caused by the demand of each item in each warehouse is small. So the model could be sensitive for the tiny variety. And the seasonality is not so obvious. So we move to try another method to do the forecasting. Long short term memory is one excellent deep learning method for time series forecasting. The partial structure of LSTM is shown as below.


The LSTM is a special kind of RNN, recurrent neural network, it is capable of learning long-term dependencies, learn from the previous data input, to our case, each neuron will learn from the previous neuron, with current month demand and the last month demand, as our model we set the time step as 6, which means we will get one prediction depends on the previous 6 months’ actual demand. After trained our model with our 45 months data, we finally can get our Oct’ s demand, and combined with the predicted Oct demand, do the prediction for the Nov, then combine and forecast Dec demand. It realized the information persistence. And not so strict to find the seasonality, so the result could show better performance.

As we can see, here is the result of the 45 months training processing results, it fits well of our original demand, and we also get the predicted demand for the last three months of 2016.


Then we repeated the same work for each item in each warehouse.

ABC Analysis

Used for: Inventory management and optimization

Based on: Pareto principle, 80/20 rule

Logic: vital few and trivial many

Objective: Maximize service AND Minimize inventory cost

Data preparation, Analysis and Results:

Inventory management is a challenging problem area in supply chain management. Companies need to have inventories in warehouses in order to fulfill customer demand, meanwhile these inventories have holding costs and this is frozen fund that can be lost. To achieve inventory management and optimization, ABC analysis is conducted to categories inventory items into ABC class based on importance rating.


Data set on sales and inventory with over 100 types of products in 2015 is deployed for analysis. To carry out such analysis, we first derive the items like unit cost and the usage of each product over 1 year. Then calculate the turnover in 2015, rank those products based on net value, and got the cumulative cost and items. After we got the pre-processed data table in above figure, we finally use the ABC rules listed below to plot the ABC classification results as a line chart.



Revealing from the figure 3, there are in total over 20% of products are classify into A type, which make up over 70 % of the net value. Then follows B type having over 30% of products which accounts for almost 20% of the revenue. Finally, products in C class can only earn less than 10% net value but make up 40% of the total products which indicates as a great amount. Overall it proves the rule of thumb: vital few but trivial many.

Inventory management Implication and Strategies


  • A-items should have tight inventory control, more secured storage areas and better sales forecasts; re-orders should be frequent, with weekly or even daily reorder; avoiding stock-outs on A-items is a priority.
  • B-items benefit from an intermediate status between A and C; an important aspect of class B is the monitoring of potential evolution toward class A or, in the contrary, toward the class C.
  • Reordering C-items is made less frequently; a typically inventory policy for C-items consist of having only 1 unit on hand, this approach leads to stock-out situation after each purchase which can be an acceptable situation, as the C-items present both low demand and higher risk of excessive inventory costs. Make to order production strategy is also applicable there.

Warehouse Insights and Optimization


ABC analysis can also be used in design of warehouse when carry out storage and retrieval process. Since majority of the picking activity is performed in a rather small area, the warehouse layout should be optimized to reduce time spent looking for product in the back of the warehouse. Demonstrated from above figure,  the fastest moving products in the inventory should be located closest to the shipping, staging, and receiving area in the lower right of the diagram below. The area marked with A, B, C indicates the ABC items.

Application of ABC analysis in warehouse


Previously, the shelves are set up in a U-shaped layout, providing a logical path for picking. However, this layout is not efficient. For each preparation of partial products, operators have to walk the whole U. If the stored items are big volumes or if the U cell is widespread, the time spent in walking and transportation becomes significant.

Since the time passed to move or transport material or parts is considered as a non-value operation, or a waste in the lean thinking way. ABC analysis is deployed here focusing on the “stock turns” index or “frequency of picking”. The A class items being so often picked, it is only common sense to place them close to entry-exit (green zone) in order to reduce reaching distance and picking time. Statistically in fewer demand, B class items are placed behind A class (orange zone) and C class items (the least picked) stored even further (red zone). So in most cases, the journey in the U cell will be restricted to the front (green zone). In few cases only, statistically less frequent, the operator will have to walk the whole U cell.

On top of time and distance reduction, we can imagine the light of our green zone should be on constantly, but orange and red zones could be equipped with sensors or switches to light them up only when somebody is in. In the same way, for the comfort of workers, heating or air conditioning isn’t necessary in zones where they seldom go. These are opportunities to reduce energy expenses.

Implication and Recommendation


Besides the implication and strategies, for further scope, developing a customized inventory management agent system is also applicable by consolidate the technology we proposed so far as demonstrated above.

  • Purpose — To determine the amount of inventory to keep in stock, which is how much to order and when to order.
  • Functions — Automation of inventory management; Timely react to demand deviation from the forecast demand; Making corrections based on stock reward function & replenishment policies
  • Working Logic — ERP data is first as employed as the input for ABC analysis. Then the LSTM algorithm are used and the related forecast error will be calculate, more deep learning algorithms like RNN or time series model like Arima can also be involved to finally choose the best. The stock rewarding algorithm (SRA) can be used to get the recommended stock level using forecast results, at the same time some replenishment policies and rules from domain expert of this company can also be added to supplement SRA to achieve optimal stock level. At last, real demand will be compared with the forecast one, and the performance of SRA will be evaluated to modify the parameters in the previous section to achieve better performance.

Stock Reward Function


Since our customer is a retail company, it should pay a lot attention to stock related management. Except for demand prediction, optimize stock is also an important point. Here our team try an emerging method, called Stock Reward Function.

Stock reward function is defined as bellow:


In which:

  • is the gross margin for selling 1 unit
  • Sis the stock-out penalty (negative) for not serving 1 unit
  • is the carrying cost penalty (negative) for not selling 1 unit in stock
  • is the number of units held in stock
  • ytis the demand for the period t
  • αis a discount factor
  • Ris identical to R but with S=0

In our implementation, we suppose that R∗ is equal to R, and S=0, and we will get Stock reward function as bellow:



Since we wanted to try different way with other teams, we not only tried Lingo to implement this function, but also used Python to implement it.

We used a Python package called DEAP. Which is a Genetic Algorithm package. Since we have fixed some parameters like bellow:

  • C = – 0.3
  • α = 0.98

Our chromosome and gene design become quite easy, which is consist of K. And as for demand yt, we choose 3-month demand.

Our target for this GA is maximizing reward for every product. And we set GA initial population as 1000, 40 generations and mutation to get different input combination.

Finally, we will get the optima stock for every product, which is showed bellow:


Order Routing


Retail Order Routing is the efficient allocation and fulfillment of customers orders. It means knowing where all the inventory is located, understanding the costs of shipping from each location and having the flexibility to change priorities on the fly to better manage the business. Order Routing helps manage, optimize and improve the profitability of the retail business. As the business grows, routing the orders becomes a complex problem that grows exponentially. Running a real-time business without a solid retail order routing system – is just not sustainable. Efficient and timely routing helps in increasing the customer satisfaction.

The problem discussed here is about:

  • A small retailer company hosts warehouses in three major cities in the United States ,New York, Chicago and Los Angeles.
  • A survey collected from the various customers indicated that they are quite happy with the quality but they are not satisfied with the on time delivery.
  • In order to analyse this issue, the customer order data was collected from the various warehouse locations to suggest any improvement in the existing routing procedures.
  • The team chose the warehouse located in New York and analysed the various outbound routes to the customer locations situated in the city.
  • The main parameters that were considered while optimization of routes were distance, delivery time window, and service time.

Routing use My Route Online

My route online is an online software which aids in optimization of multiple routes using the google map API. We can  Import as many as 350 addresses from Excel, or manually add multiple locations in various different formats.


Steps of using My Route Online

1)Enter the address manually in the prompt that appears or import the address from excel files and click on next button.


2)Set the required parameters like the departure time,number of routes, vehicle size etc and click on Plan My Route button at the bottom corner.


3)Visualization of the route


  • Print out the best routes



Manual changes in the route can be performed using the Manual Changes button and the best route map can be obtained by clicking the Print Map button.

Route with Delivery Time Constraints

For Deliveries adhering to time windows i.e the customer wants the delivery to be done with in a particular time window during the course of the day,the below instructions needs to be followed

1)Add two additional fields to your spreadsheet(excel spreadsheet). Label them “Delivery From” and “Delivery To”. The labels must be exactly as written here, without the quotes. These labels will be recognized as “Window From” and “Window To” in the software.


2)In the “Delivery From” field, put in the earliest delivery time for the stop and in “Delivery To”, put the latest. Make sure that the times are 24 hour time (military time). For example, 10:00 AM is 10:00, and 2:00 PM is 14:00.

3)Import your spreadsheet into MyRouteOnline.

4) Set the parameters you need and click Plan My Route.


Team Member

A0163200B – Anand Rajan

A0163364E – Gao Yuxi

A0163265E – Li Hangxing

A0163312U – Yao Meng

A0163313R – Shi Haiyuan

Divorce Trend in Singapore — August 31, 2017

Divorce Trend in Singapore


Singapore is a unique city-state with cultural diversity that has gone through tremendous changes in the past 40 years, growing from a sleepy fishing village to one of the most vibrant commercial hubs in the world. As the economy and infrastructure were transformed from pre-industrial to industrial status to position the nation state as a competitive global market player, its divorce rate are also on the rise which is quite alarming with the ranking of 4th among Asian countries. The risk factors that trigger marriage dissolution can be plenty to gradually make it a grave social concern, which involving children, employment, household income, marriage duration, education, the changing social status of Singapore Woman and diverse racial and ethnic characterization etc (Straughan, 2009).

GQM Analysis:


Based on the data availability and literature review of impact factors on divorce(QUAH, 2016), this dashboard applies 5 crucial factors to conduct dashboard visualization and analysis, which involving household income, ethnic group, age group of the couple, marriage duration and employment. Here we identified the business goals and set up KPI.

  • Conclude the divorce trends in Singapore over 35 years’ data and make reasonable predictions
  • Identify the crucial factors which triggering marriage dissolution and analyze their impacts on divorce rate.


  • How long do the marriages last before the divorce?
  • what is the age distribution of divorce number?
  • How does unemployment influences divorces?
  • How does income influences divorces?
  • What is the difference in divorce based on the race?


  • Duration of marriage before divorces per year
  • Divorce number of different age groups
  • Annual long-term unemployment rate
  • Monthly household income per year
  • Yearly racial divorce number



Access here: Dashboard – Divorce Trend in Singapore


Overall Divorce Trends


According to the annual report released by the department of Statistics in Singapore, A total of 7,614 marriages ended in a divorce or an annulment in 2016, which indicating as the highest annual figure over 36 years ever since. Both civil and Muslim divorce have contributed to such peak, with up to 1.5% rise from the 7522 marital dissolutions in the previous year.

Comparing to other Asian countries which also suffering from increasing Divorces, Singapore ranks the 4th with both nearly 7 annual divorces per 1000 married males or females respectively.

Hence such high figures on record together with the SmartArt (shown on the left) to perform exploratory analysis on divorces over years is indicating the next peak of this socioeconomic phenomenon in the coming future.


Q1: What is the age-specific distribution of divorce rate within Singapore? Should relevant campaigns or social trend be created to address this situation?


Overall, the number of divorce people has been increasing yearly for all age groups in Singapore. Specifically, people aged in 25-49 consist of the majority of divorce population. It means that middle and middle-late-aged people are more likely to divorce than people in any other age group. It can be reasonable because:

  • For young people, since the average age of marriage keeps rising, there are fewer and fewer young people are getting married. Less get married, less divorce.
  • For middle and late-middle-aged people, levels of stress were highest among this age group. Children’s education, taking care of parents, career challenge and so forth make people feel anxiety and easily cause couples quarrel and then get into divorce.
  • As for the elder people who generally have married for over 20 years, it seems after all these years, couples have adjusted and broken with each other well. Their marriages become more stable and also would be probably less and less willing to divorce as time goes by.

Q2: How long do the marriages last before the divorce?


As we can see, below 5 years marriage duration has the most number of divorces, followed by 5 – 9 years duration. All other duration have significantly lower number of divorces. And a cross-over in year 2010 where 5-9 years duration has more divorces than below 5 years duration, ever since then. Besides, sudden peaks in year 1999 and 2003 across all duration. There are some possible reasons:

  • If a couple can ‘tolerate’ each other for 10 years, they are less likely to divorce thereafter.
  • The ‘7-year itch’ phenomena is getting more significant in modern times.
  • Some events happened around or before 1999 and 2003 could have caused the sudden peaks. The events could have been socio-economic in nature and was widespread and significant enough to affect marriages across all duration.

Q3: How does unemployment influences divorces?


Here we can see there are sudden peaks in long-term unemployment rate from 1997 to 1999 and from 2001 to 2003. This correlates to the sudden peaks in divorces around 1999 and 2003 shown in the previous 2 charts. It can be reasonable because:

  • The 1997 Asian Financial Crisis had an effect on the economy of Singapore. This in-turn caused long-term unemployment and could have strained the marriages. However, the Singapore well-managed currency recovered the economy quickly and was not affected for long.
  • The 2001 terrorist attack on World Trade Center in US affected the global economy and had a bigger effect on Singapore economy than 1997. The effect lasted longer and the unemployment rate took longer to recover. The divorces has a sharper increase than 1997.

Q4: How can median monthly household reflect the total number of divorce?


As we can see from the graph, from 2000 and 2003, the household income is decreasing, meanwhile, the number of divorce people is increasing in a dramatic way. While, according to the analysis of that years, we found that the average level of price was still increasing. The conflict between income and expenditures increased the family burden which affect the harmony of family relationship. After 2005, with the increasing of income, the increase speed of  total divorce number was slow down.

Until 2011, when household income broke 7000 dollar per month, the total divorce number tend  to be stable. After 2011, although  the household was still increased, the total divorce number kept stable. According to this situation, we could draw the conclusion that once the household income per month higher than S$7000, the income may not influence divorce or not.

Q5: What is the difference in divorce based on the race?


Generally speaking, the divorce trends are quite similar between Indian and Chinese. Before 1998,  the divorce rate of Chinese is slightly higher than that of Indian. After 1998, the divorce rate of Chinese kept on climbing, while the divorce rate of Indians began to fluctuate between 2.0 and 2.5. From year 2010, the divorce rate for both races seem to fluctuate at a relatively constant rate.

There was a peak around 1998 for Indian and Chinese divorces. The divorce rates increased to form some peaks. There was an Asian Financial Crisis in 1997 that caused many Singaporeans to lose their jobs, the household income decreases, and financial conflicts in the family worsen. Hence in the few years after the Asian financial crisis, the divorce rate increased, then decreased after the crisis has eased.

From the graph, the divorce rate of other races, that consists mainly the Malays, is significantly lower than Indians and Chinese. This could be due to their overall different economic status, social structure and outlook in life in general which are also different from that of the Chinese and Indians.

Further, it is observed that the divorce trend of the other races (mainly Malays) is similar to the divorce trend of Chinese, with a delayed effect of about 5 to 6 years. Hence, we could make a prediction that after 2015, the divorce rate of Other Race will begin to fluctuate at a relatively constant rate, like the trend for Chinese after year 2010.


The general divorce rate in 2016 was unchanged from 2015. However, there was a “prominent shift” in the age profile of divorces towards the older age groups in the last decade. From above discussion we find these main factors which affect divorce:

  • Distribution of Ethnic Groups – There is a tendency for different ethnic groups to have particular reasons for divorce. Each ethnic group has experienced different patterns of divorce, degrees of proneness to divorce, and causes for divorce. Although ethnic groups are, to some extent, differentiated by their different socio-economic status in the society, they experience the same processes of social and economic changes and live in the same society. This points to the fact that cultural elements play a crucial role in determining the pattern of divorce and the divorce rate in a society.
  • Age Distribution of Divorced Couples – From before studying, we find that the trend suggests that the dangerous age to be involved in divorce was overall increased, it moved from 25-40 to 30-45 years old over the past three decades.
  • The Time for Divorce – The mean duration of marriage for the divorced couples in 2016 was 10 years. The distribution of the duration indicated that more divorce took place around the fifth to ninth year of marriage, which accounted for 29.9 per cent of the total divorces. Generally speaking, after five to nine years of marriage, the husband has usually established himself, and the family is better off. At this stage, the wife does not keep up with her husband’s status. This may cause the breakdown of a number of marriages.
  • Household Income & Unemployment Rate – Money issues could be another main reason of divorce. Finance stress can cause marital problems, and marital problems can result in divorce. The correlation between income and divorce isn’t quite that clear cut, however. It indicates that financial problems are directly linked to marital problems, other factors may help contribute to whether those problems ultimately bring about divorce.

Other Crucial Impact Factors on Divorce & Suggestions

The dashboard was limited to only certain kinds of information available, as the actual results reveal more variables related to divorce proneness. The following are some other variables which would affect the marriages :

  • Number of Children of the Divorced Couples – Divorce proneness is correlated with childlessness. Childless couples apparently separated more frequently and earlier than couples with children. Childlessness may be a cause of divorce, but it may just as well be a consequence of marital instability or disharmony that leads to divorce.
  • Divorce and Occupation – In Singapore, there did find some correlations between divorce proneness and occupational groups. There was a positive correlation between occupational status and proneness to divorces. Those who were at higher occupational status were more prone to divorce.

Divorce is a form of marital instability and a type of family dissolution. It is a complicated process which involves multi-dimensional factors, both intra-familial personal factors and extra-familial socio-cultural factors. There are some suggestions on rising divorce rate:

  • Having marriage counselling programmes for couples in a difficult marriage situation could be a good idea, since married couples attending the programmes may be able to work the differences out and eventually decide against a divorce. These marriage-related programmes does enhance the overall quality of marriages and also improves relationship building skills amongst married couples.
  • Taking into account the current divorce trends, perhaps more comprehensive marriage preparation programmes and seminars for couples preparing for their wedding can be conducted by MSF, community-centered organizations and relevant religious groups to help couples better understand topics such as communication, conflict management, commitment and problem-solving to build stronger and more lasting marriage unions.

Data Sources & References

Data Sources


Team Members(Team 18)

Li Hangxing        A0163265E

Li Ruiqi                A0163293A

Lim Chong Wui  A0163460L

Shi Haiyuan        A0163313R

Yao Meng             A0163312U

Feasibility Analysis of Developing Singapore into Smart City — August 17, 2017

Feasibility Analysis of Developing Singapore into Smart City


Advances in digital technology have opened up new possibilities to enhance the way we live, work, play, and interact. And “Smart City” is not a measure of how advanced or complex the technology being adopted is, but how well a society uses technology to solve its problems and address existential challenges. Singapore strives to become a Smart City to support better living, stronger communities, and create more opportunities, for all.

Smartness penetrates in everyday life of each citizen, it is significantly impacting the  transformation of the conventional way of living. Building a smart city includes providing:

  • efficient, safe, reliable, and enhanced transportation
  • integrated and seamless healthcare
  • sustainable and livable residence environment
  • close and intimate public sector services

The analysis is conduct based on  the foundation construction of the above aims, involving nation wide Internet coverage, citizen acceptability, manpower and financial funding input, etc., to discuss whether it is the right time for transformation in Singapore.

Target Audience

Minister Lawrence Wong, Ministry of National Development

GQM Methodology

Goal — Analyze current status of infrastructure construction to suggest building Singapore into the first Smart City around the globe.


  • Is the network coverage broad enough?
  • What is the R&D (research & development) expenditure currently?
  • Do citizens get used to digital life?
  • What is the current status of domestic enterprises (acceptability)?
  • Is the IT manpower sufficient enough to support smart city development?


  • Wi-Fi hotspot density
  • web presence percentage of domestic enterprises
  • mobile penetration
  • online-shoppers distribution
  • resource requirement from IT industry


Date Modeling



Dashboard Design


Insight Discovery

Successful transformation of living habits for citizen

Citizens are trying to adapt to the “smartness” of living environment. Data of “Online shopper by age” reveals active online users distribution among different age range.

  • Age between 25 and 34 is the major consumer group in online shopping, 75% of them prefer shop online.
  • Age group from 15 to 24 has a sharp increase from year 2007 to 2014, 55% of them use online shopping for daily needs.

Fully network coverage enables smart solutions launching

Singapore has high density of Wi-Fi hotspot coverage (excluding industrial areas of westernmost and Changi airport), which stimulates the facilitation and implementation of smart solution towards almost every aspect. Citizens will be able to keep themselves connected to the entire smart ecosystem via Wi-fi or 4G network. For instance smart living solutions aims at providing seamlessly and remotely managing connected homes from anywhere, at anytime, on any screen in real time.

The intensive coverage of network nation wide is solid foundation of launching smart solutions, enables society to be fully connected.

Stably increased financial investment towards R&D provides strong support in innovation and development.

Financial investment to R&D is continuous increasing, it has an obvious rising trend from 1990 to 2014 in both private and public sectors:
Private — comprises all business enterprises in the private sector.
Public — comprises all entities in the Government Sector, Higher Education Sector and Public Research Institutes

Government tends to sustain the R&D in innovation driving, smart solution research, technology development, etc., to guarantee a stable research environment for designing, building and implementing smart city.


Team members

Cui Leyang    (A0163218J)

Duan Han     (A0163238E)

Gao Yuxi       (A0163364E)

Li Yue            (A0163373E)

Que Qiwen   (A0163391E) 


Drug Abuse in Singapore — August 14, 2017

Drug Abuse in Singapore


  • Drug abuse is defined as the persistent misuse of an illicit substance even when the results of such behaviours bring forth negative consequences, such as legal ramifications, illness or family problems.
  • Drug abuse constitutes a major socio-medical problem throughout the world. With the advent of  digital age and the spread of the Internet and digital technologies online and mobile, the challenge of keeping Singapore drug-free is increasing. It has become easier to acquire drugs, and be misinformed on drugs.
  • The illicit drug manufacturing generally follows the natural path of supply and demand, and as the number of users continues to increase, so do Asian-based production setups.
  • Minister for Home Affairs K Shanmugam Mr Shanmugam called for the anti-drug fight to be made a “national priority”.


Minister of Law and Home affairs


In this assignment we aim to:

  • Provide an overview of ‘drug abuse’ situation in Singapore.
  • Investigate the key factors that lead to the increased number of drug abusers in Singapore.
  • Help the Minister of Home Affairs focus their strategies on key areas, by providing an unbiased review of the effectiveness of various campaigns against drug abuse.


What has been the trend of drug abuse in Singapore over the recent past?

What is the most commonly used drug in Singapore?

What do the trends suggest and what are the factors that contributing to the drug abuse trend in Singapore?

How does Singapore’s drug control situation compare to other countries in the region? (KPI: 5-year growth rate trend with other countries in %)

Are the current measures taken by the government in the ‘fight against drug’ improving the situation? What is currently the most effective strategy?


What has been the trend of drug abuse in Singapore over the recent past?

  • YoY % change in number of drug abusers in different categories (age, education, new, repeat users)
  • 5-year average growth rate in %

What is the most commonly used drug in Singapore?

  • % change in the use of drug, year on year (for each different drug type)
  • 5-year average growth rate in usage, in %

How does Singapore’s drug control situation compare to other countries in the region?

  • 5-year growth rate trend of each country, in %

Are the current measures taken by the government in the ‘fight against drug’ improving the situation? What is currently the most effective strategy?

  • Performance Ratio of campaign = no. of rehab/(total no. of new + repeat cases)
    Higher ratio indicates better performance
    (refer to Anti-Drug Campaign Performance Chart, first 5 years versus recent 5 years)


  • Based on our goal we collected relevant datasets from and defined KPIs that will help us understand the drug situation in Singapore as well as measure the current campaign’s effectiveness.
  • The team performed  data sourcing from with the searched keyword drugs, after filtering and sorting. The team finally decided to select following datasets to build the dashboard

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Data from:


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  • The data collected from the various sources was preprocessed and for  the sake of timeliness only the data for the past ten years has been considered.
  • All the numeric fields which were appearing as text fields were converted to float type for enabling visualization in power BI.
  • The data normalisation criteria 1NF was already satisfied since there were no multi-valued attributes or any repeating groups. Additionally since there were no composite keys in the data tables the 2NF criterion is also satisfied. Finally, since there is no non-key attribute dependent on any other non-key attribute, so the 3NF is also satisfied.



What has been the trend of drug abuse in Singapore over the recent past?

From  the dashboard we can observe that number of drug abusers have been rising over the past years at a worrying rate, with highest figure in the decade recorded in 2013. Age group 21-30 has shown the highest increase in drug abuse, increasing by 400% from 2006-2016.

Profile of drug abusers is changing – New wave of younger, better-educated addicts. Drug abuse can also come from well-educated people, as seen in the slight increase in abusers from the tertiary group.

The consequences of this trend among youths should not be taken lightly – if we are not careful, they can become our next generation of abusers.

What is the most commonly used drug in Singapore?

From the dashboard, We can observe that the abuse of ice has increased on a year on year growth rate of 12.3%(2011 to 2016), from 2006 to 2016.

What do the trends suggest and what are the factors that contributing to the drug abuse trend in Singapore?

Age and education level of the population groups are major contributors towards the number of drug abusers in Singapore, we can see a slight increase in drug abusers among highly educated group (tertiary and above). A new wave of better educated population in drug abuse cases indicate that low education level alone may not be sufficient to explain the increase in drug abuse.

This highlight new trends in our fight against drug abuse – such as the changing attitude and perception of our youths towards drugs.
The broader issue at large might be misinformation about drugs online, leading young people to think the drugs are not harmful.

How does Singapore’s drug control situation compare to other countries in the region? (KPI: 5-year growth rate trend with other countries in %)

The 2016 World Drug Report by the United Nations Office on Drugs & Crime (UNODC) reported that methamphetamine (ice) seizures in East and Southeast Asia almost quadrupled between 2009 and 2014. South-east Asia continues to be a major market and producer of illicit drugs and Singapore is  a major transport hub, and vulnerable to drug syndicates in the region. Comparing with other countries in the region, we can see that the 5-year growth rate Singapore remains relatively green on the heat map indicating a low 5-year growth rate. Singapore currently has a 5-year average growth rate of 9.2% while in SE Asia countries like Laos and Myanmar the 5-year growth rate is 17.8% and 11.2% respectively.

Are the current measures taken by the government in the ‘fight against drug’ improving the situation? What is currently the most effective strategy?

We can see that for the first 5-year from 2006 to 2012, the ratio is low, since the sum of new and repeat cases are large relative to the number of rehabs. From 2012 to 2016, the performance ratio is raised, since the number of rehab increased while the sum of new and repeat cases dipped. Hence indicating that the current rehab measure is to some extent successful in controlling the drug situation over the years.

However, the total no. of drug users is still increasing, from 1.7k to 4.6k over a ten-year period from 2006 to 2016.

All these suggests that although rehabs and arrests are important part in Singapore’s comprehensive anti-drug regime, they are merely part of the overall approach which will not work on its own.

Other lines of defense like cyber tracking and enhanced online tracking facilities would be needed to cope with new challenges such as the increased supply of drugs in the region and online.


Anti-Drug Abuse Campaign this year with a renewed focus on involving youths in the fight against drugs, as well as enhance its social media efforts.

Singapore will have to increase its partnerships with overseas counterparts and tackle the new “online supply menace”. There is also a strong requirement for volunteers, more individuals to raise up their hands and shoulder the responsibility in the fight against drugs.

Preventive education, which includes school talks and lesson plans.

NG WEIPING (A0056380H)

A Survey on Singaporean Traffic and Roadwork Data — August 7, 2017

A Survey on Singaporean Traffic and Roadwork Data


When you’re driving around in the Business Hub of the world, you would obviously expect things to be a bit on the congested side. As of the Tom Tom world traffic index in 2016, it has been observed that singapore has witnessed a rise in congestion levels due to traffic. This comes as no good news, as the existing traffic condition in the country adds about 34 minutes of extra travel time on an average. Combine that with the pre-existing traffic situation in Singapore and you’re staring at a big logistical problem.

However, many efforts are being taken on the travel optimization front in the city. The introduction of the Electronic Road Pricing or ERP systems that reduced the time wasted on manual parking fee collections. Further efforts to reduce traffic congestion and improve quality of transport across the country need sufficient backing by meaningful data and statistics. This is the inspiration behind surveying the country’s traffic and incident data.

Data Acquisition and Pre-Processing:

Data for the survey was pulled from as well as the Land Transport Authority Data Mall. We extracted the following fields from the available APIs for the use of our survey models.

Label Description
Incident Type Road incident type. Example: Roadwork, Accident, Vehicle Breakdown.
Geographical Location Geographical data consisting of Latitude and Longitude.
Road Name Name of the road where the incident has occurred
Time Time details about the incident, consisting of start and end time of the same.
Temperature Average Temperature recorded in singapore for the corresponding time stamp.
Pressure Pressure recorded in Singapore for the corresponding timestamp.
Humidity Average Humidity recorded in Singapore for the corresponding timestamp.

Data Sources: ; ;

GQM Analysis:


The broad goals of this analysis is to devise methods that will aid in a reduction of congestion due to traffic while simultaneously reducing the effect of roadway incidents on the traffic.


To provide solutions that help us achieve our goals, we have to answer some basic questions first. Here are the main questions whose answers we feel are imperative in order to achieve solutions leading to our goals. We have grouped them into the 4 main types of questions we need.

  • Where?

Where is traffic concentrated across the city?

To understand how we must tackle the problem of overcrowding the roads, we must first understand what the most affected areas are.

  • When?

When is the Traffic at its peak?

To develop a sustainable planning model, we will also require the times at which traffic it at its peak. For example, if one were to schedule a roadwork activity, they would have to make sure it doesn’t clash with peak traffic timings so as to minimize its effect on the congestion.

When do majority of incidents occur?

By understanding the distribution of incidents across times, we would be able to estimate and predict the likelihood of its occurrence and correspondingly take action.

  • How?

How is the distribution of major roadwork incidents across the country?

To allocate resources for different tasks, we must know the different kinds of incidents occurring across the country, along with their individual frequencies of the same.

  • What?

What are some factors that affect traffic conditions?

Do factors such as weather influence the traffic incidents in Singapore? If so, is it possible to predict the likelihood of certain incidents based on weather? And if there weather affects the traffic conditions by a lot, we could focus planning activities on mitigating the effects of the same.


  1. Geospatial Traffic Data- Incident Flag, Latitude, Longitude.
  2. Time Data- Start Time, End Time, Total Time.
  3. Weather Data- Temperature, Humidity, Pressure.
  4. Incident Frequency Rate.

Data Dashboard:

     Click on the picture to view the Public Tableau Dashboard


Insights and Conclusions:

  • We observe that most incidents take place during the day and that there are very few recorded cases of traffic related incidents during the night.
  • Roadwork incidents seem to take the longest amount of time, and hence must be planned carefully so as to not disrupt normal traffic.
  • The effect of Temperature, Humidity and Pressure upon Singapore traffic is minimal, and that traffic is weather-independent.
  • A majority of the traffic related incidents seem to occur around the Central Business District in Singapore, thus indicating where most resources must be pumped.
  • A more in-depth analysis can be done over a longer period of time and can yield better insights


Team of 
Naitik Shukla (A0163426H)
Navneet Goswami (A0163221W)
Shobhit Jaipurkar (A0163331R)
Vignesh Srinivasan (A0163246H)
Baby Bonus Dashboard – Building a Nation. Creating a Family. —

Baby Bonus Dashboard – Building a Nation. Creating a Family.



The information provided in this blog post is meant for student project/student research purposes. Content accuracy is based on and limited by the team’s understanding. Raw data accuracy is valid as of 6th Aug 2017.

The blog post may include links to external internet sites. These external information sources are outside the control of the team. The user of the external links is responsible for making his or her own decisions about the accuracy, reliability and correctness of information found.

In no event shall the team be liable for any indirect, special, incidental, or consequential damages arising from any use or reliance of information presented in this student project.

Student Team (Part-Time KE MTech): 

Tan Yoke Kum (E0146982), Koh Tian An (E0146432), Ng Shan Jun (E0146756), Isaac Varun Kumar (E0146794), Ho Wei Jing (E0146951).

Introduction – What is the Baby Bonus Scheme?

The Baby Bonus Scheme was first introduced by then Prime Minister Goh Chok Tong during the National Day Rally held on 20 August 2000. Also known as the Child Development Co-savings Scheme, it is an initiative targeted at boosting fertility rates by encouraging married couples to have more babies. [1]

The aim of Child Development Co-Savings (Baby Bonus) Scheme is to help lighten the financial costs of raising children. [2]

Project Contextualization – What are the Critical Elements of the Baby Bonus Scheme?

Creating a dashboard requires knowledge of the theme/domain – and the questions that that needs to be answered for that domain.

The team asked ourselves:

  • What is the Baby Bonus Scheme actually helping with?
  • What is the Baby Bonus Scheme’s objectives?
  • Why will someone need to monitor the Baby Bonus Scheme?
  • What will the user of the dashboard needs to look out for?

The team researched on the subject and based on our limited comprehension of the subject, formed the following understanding:

Singapore’s Baby Bonus Scheme

Supports couples in their decision to have more children. [2]

  • The Child Development Co-Savings (Baby Bonus) Scheme helps to lighten the financial costs of raising children.
  • The scheme is part of the Marriage and Parenthood Package and includes a cash gift and contributions to the Child Development Account. [2]

The success of the Baby Bonus scheme is debatable [3], but revisions over the years have produced an enhanced financial incentive scheme that is more beneficial to prospective parents considering the current income growth and cost of living.

Using this understanding, the team then formulate the organisational goals which will become the philosophy of the dashboard, with five key questions that help identify the data needed, the core layout and the chart types of the dashboard design.

Organisational Goals

Ensure the effectiveness and relevancy of the Baby Bonus Scheme in reducing the financial burden placed on parents having to raise a child.

Provide an overview the areas of interests that will:

  • Address the questions raised
  • Allows the viewer to make an executive assessment on the impact of the Baby Bonus Scheme
  • Decide on the level of improvement required for the Scheme

The Five Questions that Drives the Data Collection and Dashboard Design

  1. What is Singapore’s fertility trend?
  2. What is Singapore’s birth order trend?
  3. Which ethnic group responds positively to the Baby Bonus Scheme?
  4. What is the progression of living costs (of a child) over the years since the introduction of the Baby Bonus scheme?
  5. How effective is the Baby Bonus Scheme in lessening the financial burden of raising a child?

Data Collection – What is the Data We Need? Where Do We Get It?

The team has handpicked several market costs indicators to assist in calculating “sample” living costs for raising a child.

The compiled costing data is not meant for a one size fits all scenario, and is meant for a general overview of the trend in costs of raising a child over the years.

Some of the monetary data are not readily available, or found in a single source among the government’s open source data sets. As such the team has conducted investigative data gathering, extracting information from newspaper articles, internet archives (Wayback Machine) and other internet sites e.g., forums and websites.

Cornerstone data such as the Baby Bonus Scheme cash payouts, Singapore’s birth rates and fertility trends can be found on government websites, though sometimes forums, newspapers and others need to be sourced (e.g., for past Baby Bonus Scheme cash payouts)

Sampling are made as representation of certain data sets (not to be taken as indicators for the whole) e.g.,:

  • Maid Costs are represented by the measures for Filipino Maids (assumptions are made that costs are constant over the years till the next price adjustment.)
  • Hospital Maternity Costs is represented by pricing from Thomson Medical (using the single room cost)
Data Cluster Contains Source
Birth Rate
  • Year of Recorded Birth Rates
  • Total Annual Births
  • Total Malay Births
  • Total Chinese Births
  • Total Indian Births
  • Total Other Ethnic Births
  • Year of Recorded Median Household Income
  • Median Household Income Per Member
Baby Bonus
  • Year of Recorded Baby Bonus
  • Baby Bonus CDA
Fertility Rates
  • Year of Recorded Fertility Rates
  • Total Fertility Rate
  • Net Reproduction Rate
  • Year of Recorded Housing Resale Price
  • Month of Recorded Housing Resale Price
  • Resale Price (Mean)
  • Resale Price (Median)
  • No. of Flats
Birth Count By Order Year of Recorded Birth Count

  • First Child
  • Second Child
  • Third Child
  • Fourth Child
  • Fifth Child
  • Sixth and Above
Monetary Matters
  • Year of Recorded Costs
  • Maid Cost By Month
  • Infant Milk Powder Cost (by 900 Gram Tin)
  • Tins of Milk Per Month
  • Infant Care Services By Month
  • Child Care Services By Month
  • Thompson Medical Delivery (One Time Fee)
  • Childcare Subsidies By Month
  • Infant Care Subsidies By Month
  • Baby Bonus Cash Per Year
  • Total Costs Per Year [Using Cost of Maid (x12) + Cost of Milk Powder (x12) + Cost of Infant Care/Cost of Child Care (x12)]

Wayback Machine. KK Women’s And Children’s Hospital Web Archives. Retrieved on 6th Aug 2017 from

Data Cleaning and Our Dashboard KPI Metric

Certain data such as the KPI – Metric for Baby Bonus are calculated once the data are gathered and the team began to prepare and clean them.

Cost of Living/Expenditures of raising a child are calculated based on certain indicators of the “monetary matters” raw data.

Data that presents their values in months are represented into annual data by using their original monthly data x 12.

Calculating change among certain data are based on:


KPI Metric is shown as below:

KPI Ensure the effectiveness and relevancy of the Baby Bonus Scheme in reducing the financial burden placed on parents having to raise a child.
Metric Main elements used:

  • Total Cost of Living – Per Annum (Based on sample indicators tied to raising a child for the first six years)
  • Cost of a maid
  • Cost of milk powder
  • Cost of infant care/child care
  • Baby Bonus Cash Payment – Per Annum (Based on total payout divided by six years)

Data Modelling


Baby Bonus Dashboard Design



To understand if the dashboard will be able to fulfill the team’s identified organisational goals and key questions, the team took a walk in the shoes of the identified user of the dashboard.

Reading the Dashboard – Chart 1 Fertility Rate


The birth rate has seen a steady downwards trend over the decade. The decline has been arrested in recent years, although it is still dipping down slightly.

Current birth rate is less than 1.4 per household. This is well below the 2.1 target rate.

The dashboard user is perceived as being the “Manager” for the Baby Bonus Scheme, as such, the fertility rate is the leading cause of why the Baby Bonus Scheme is needed.

Key Note: The Baby Bonus Scheme is but one of the supportive measures to assist in arresting the fertility rate decline and support a KPI target of 2.1 for total fertility rate. Its principle direction is to lessen financial burden of raising a child, and as such, it does not directly improve the fertility rate significantly. It is however a key influencer in maintaining the status quo (as will be shown below).

Reading the Dashboard – Chart 2 Birth Rate By Order


Due to the way the Baby Bonus cash pay outs are staggered and differentiated based on the order of the child, the birth rate by order is an important trend for the “Manager” of the Baby Bonus to monitor.

It is observed that the scheme did encourage first time parents especially after the revision in 2004 to include the first baby. Parents with a child are also more likely to have a second child, with the numbers peaking or maintaining throughout the period of Baby Bonus Scheme adjustment intervals.

Key Observation: In truth the trend shows that the Baby Bonus Scheme helps in maintaining the number of prospective parents having first and second child.

The downward trends and flatlines are the third, fourth, fifth and sixth child. This presents a pattern that parents are more cautious of having more than two children in this day and age, even if Baby Bonus cash pay outs are enhanced.

It can be implied that the Baby Bonus Scheme has no impact on citizens’ decisions in having a third child and more.

Key Note: From dashboard observation, the “Manager” will be able to identify his key target audience as the parents having first and second child. To improve the conception of third child onwards, the government might have to consider supportive measures or monetary incentives in other areas.

Reading the Dashboard – Chart 3 Birth By Ethnicity


Race plays a part in fertility rate. Since fertility rate is measured by the number of child per woman (of child bearing age range), the birth rate is important.

As shown, while the Chinese are the ones setting trends of Singapore’s birth rates by annum due to their large population size, the Malays “seem” to be the group responding well to the Baby Bonus Scheme in recent years. They are the only ethnic group to have seen an upward climb in births in recent years.

Key Note: The “Manager” of the Dashboard will be able to pick up on the trend and explore in deeper detail of “WHY?” Malays “seem” more receptive to the scheme, and to explore factors of influence for other ethnic groups.

Reading the Dashboard – Chart 4 Expenditure Incurred


Key contributors identified are:

  • Maid Cost
  • Infant Milk
  • Infant Care
  • Child Care
  • Delivery (Thomson Medical)

In order to reduce financial burden for prospective parents, the expenses spent on raising a child is a key decision factor in whether the existing Baby Bonus Scheme should be revamped.

As most market item costs (pertaining specifically to infants and children) are not publicly/officially available on government data sources, a significant amount of work was put to identify “key contributors” to represent the trend of expenditure needed to raise a child over the years.

As can be seen, the total costs have been raising steadily over the years, establishing the need for the Baby Bonus Scheme to act as a counterbalance to sustain the parents and their children’s quality of life.

Reading the Dashboard – Chart 5 Expenditure vs Baby Bonus


Although this is in essence a general overview of total expenditure of the previous graph, the objective of this graph is to present a comparison of the rate of expenditure growth against the Baby Bonus Scheme’s pay outs (using data from the 1st/2nd child pay out as they are of same tier and make up more than 80% of newborns annually.)

The “Manager” of the Baby Bonus dashboard will be able to look at this quick overview to identify if the expenditure of raising a child is increasing faster than the Baby Bonus cash incentives. This preempts him to begin looking deeper into the raising costs, and what other measures he can take to prepare for eventual Baby Bonus Scheme revamps.

As supported by the trend of the data over the years, the Baby Bonus cash incentives have been raising in a proportionate measure close to the trend of the expenditure of raising a child.

Reading the Dashboard – Making a Decision at the Conclusion

With the readings, the “Manager” will then be able to forecast/estimation and begin formulating next course of actions/future plans.

Using what the data has shown thus far as a guide, the “Manager” could decide that based on the data trends, the estimated expenditure of raising a child will continue to raise, which indicates that the Baby Bonus Scheme will need to raise with it.

To ensure data integrity and accuracy of trend prediction, a wider net will need to be cast by the authorities to identify and store market costing data for other necessary essentials (items/services) for raising a child (e.g., baby clothes, diapers, vaccination costs).

Extra Notes – Other Factors Affecting KPI

A KPI such as the fertility rate is also indirectly influenced by other factors. Some of these factors that could affect the KPI and fall outside the scope of the Baby Bonus Scheme (for this project) are identified, so as to illustrate the team’s decision of why data relating to these elements were ultimately not utilized in the dashboard.

Household Income was originally identified, and then discarded as a key influencer of the Baby Bonus Scheme (in this context of the project) as the team’s available data on income is based off per household.

  • If a couple live with their parents or siblings, it doesn’t mean the combined household is supporting the child expenditure.
  • Each households have other expenses tied to it. E.g., daily essentials, elderly care, education.

Racial Culture

  • As shown from the dip in birth rate in year 2010 (Year of the Tiger) culture played a part as it is a belief among Asian Chinese that children born in the year of the Tiger are more difficult to manage, resulting in lower births in Tiger years. This is more prominent in Singapore and Taiwanese Chinese. [4]

Environmental and Health Factors

  • Hazardous levels of haze
  • Zika virus

Generation Cycles

  • A baby boom during previous generation could lead to another baby boom/fertility peak 20-30 years down the timeline as the children of prior baby boomer generation reach child bearing age.

After Action Review

During the course of the project, the team experienced stumbling blocks as it was discovered that publicly available data do not cover certain key indicators as needed for the analysis. While the unavailable data could be discovered on the internet, certain data pertaining to costs of items or services over the years are lost as the websites updated.

The team have to explore creative ways to access, interpret and make assumptions of data where necessary via internet archives (which does not managed to store everything, especially pages that are dynamic), online newspapers articles, libraries, blogs and forums.

The team realised the value of starting a systematic and properly modelled storage of data over the years throughout the process. It is difficult for analytic work if the sources are all over the place, and authenticity of the analysis can be called into doubt if data is not from validated sources.

This project has been an interesting and fun experience, the team’s data findings provided useful insights that altered our preconceived perceptions of the subject.


  1. Baby Bonus. (2000, August 28). The Straits Times, p. 8. Retrieved from NewspaperSG.
  2. Baby Bonus Scheme. Ministry of Social and Family Development. Retrieved from
  3. Baby Bonus Has Had Little Impact So Far. The Straits Times. pA1 & A6. 15 August 2009. Retrieved on 2 Aug 2017 from
  4. Here’s how the Chinese Zodiac affects national birth rates. Business Insider. Retrieved from

Creative Commons

  1. Pixabay. “Baby Girl Sleeping”. TawnyNina. Retrieved on 7th Aug 2017 from
Singapore Car Ownership and Public Transportation Analysis — August 6, 2017

Singapore Car Ownership and Public Transportation Analysis


With its small land mass and high population density, Singapore has always been wary about the issue of congestion. Pollution and environmental sustainability are also hotly debated topics that are relevant when discussing the issue of private car ownership and public transportation ridership.

In an attempt to control a growing vehicle population, the Certificate of Entitlement (COE) system was introduced in 1990. The government determines the quota of COEs to release and then allows the market to price it. Vehicle owners have to bid for a COE that would enable them to own and use the vehicle for 10 years. Bidding is conducted twice a month, through an open online auction system.

COEs have made Singapore one of the costliest countries to buy a car, thus discouraging many people from choosing to own a car. On the other hand, continuous efforts have been made to make public transport affordable and convenient. Investments have been made at maintaining and improving the infrastructure, the number of trains and buses have been increased, as well as the routes covered.

Goal and Target Audience

The target audience for this dashboard is the Ministry of Transport and Land Transport Authority. The goal is to analyse the impact of various government initiatives on controlling congestion through measures such as restricting car ownership and improving public transportation such that it is a viable and often, preferred alternative.


  1. How have population size and GDP per capita changed over the last 5 years?
  2. Have people been buying more cars on average?
  3. Are people choosing to take public transport more frequently?
  4. Is there a correlation between GDP and car ownership?
  5. How effective has the COE mechanism been in controlling car ownership?

Key Metrics

  • Ratio of people to cars
  • Average daily public transport ridership




  1. How have population size and GDP per capita changed over the last 5 years?


The annual population of Singapore has increased 7.69% from 2011 to 2016.


The annual GDP per capita has increased 8.96% from 2011 to 2016.

2. Have people been buying more cars on average?


After a surge from 2011 to 2012 where the ratio of people to cars decreased from 11.63 to 8.60 (meaning that people bought more cars), there has been a steady increase in the ratio, suggesting that people have indeed been buying more cars on average in the last 4 years.

3. Are people choosing to take public transport more frequently?


Usage of buses and MRTs has increased by about 30% and 50% respectively over the observed 5-year period, faster than population growth of 7.69%. This increased popularity of public transport indicates that this is a preferred mode of travelling as opposed to personal cars. This suggests that the addition of more buses and trains with more lines and routes serviced has had the desired effect of encouraging people to use public transportation over private cars.

4. Is there a correlation between GDP and car ownership?

As cars are seen as luxury goods, one would expect a positive correlation between GDP and car ownership in Singapore.

From the above macroeconomic trends of a larger, wealthier population, one would expect the ratio of people to cars to decrease. However, since the significant decline from 2011 to 2012, the ratio has been increasing. This suggests that despite people in Singapore becoming wealthier, they are choosing not to buy cars.

There could be push and pull factors to account for this. On one hand, the prices of cars (heavily influenced by COE prices) could have increased more than this increase in wealth as measured by GDP per capita. On the flip side, public transportation could have been improved to the point where it is now the preferred mode of transport, appealing more to people in Singapore than owning a private car.

5. How effective has the COE mechanism been in controlling car ownership?


This graph shows the quota of COEs released remained rather constant from 2011 to 2014. Category A refers to cars below 1600c while Category B refers to cars above 1600cc. Both categories follow a similar trend however there was a widening gap with more lower cc cars being allowed since 2015 as compared to higher cc cars.


Expectedly, the price of a Category B COE is usually higher than that for Category A. However, prices for both Category A and Category B peaked in early 2013 and have been on a similarly gradual decline since. This is in tandem with the increase in the quota number as the price and quota are negatively correlated.


The efforts in curtailing car ownership, and by extension, congestion and pollution have been somewhat effective, with the ratio of people to cars going up despite an increasing GDP per capita. This suggests that the COE mechanism used to adjust the cost of car ownership has been effective.

Coupled with the increase in average daily public transport ridership, it appears that people in Singapore are finding public transport a more popular and viable means to travel, perhaps due to its convenience and low cost.



Team Members (Team 8)

Nway Nway Aung (A0163233N)

Soong Li Ching (A0163442L)

Luo Yuan (A0163417H)

Lynette Seow Hui Xin (A0163452J)




Performance Management Dashboard for the Early Childhood Development Agency —

Performance Management Dashboard for the Early Childhood Development Agency

Analysis of Demand for Regional Child Care Centers

The emergence of new Built-To-Order Housing and Development Board (HDB) flats often drives demographic changes within each town – new towns are characterized by a strong presence of young adults while older towns tend to face issues surrounding ageing population. As such, there is a need for interested government bodies, such as the Early Childhood Development Agency (ECDA), to understand the population structure as well as the quality and sufficiency of child care facilities within each town in order to better allocate manpower and logistic resources to meet the evolving needs and promote pro-family services that may in turn alleviate the nation’s falling birth rate.

Performance Management Dashboard for the Early Childhood Development Agency

The Early Childhood Development Agency (ECDA) is the regulatory and developmental authority for early childhood services in Singapore. Its mission is “to ensure every child has access to affordable and quality early childhood development services and programmes.” Putting ourselves in the shoes of ECDA’s planning division director, we envisage this role to be responsible for charting various organisational strategies to achieve the goals of accessibility, affordability and quality so as to fulfill the agency’s mission. To assist the director in assessing whether the organisation is on track to achieve the goals set out for the fiscal year, we developed a performance management dashboard highlighting several useful key performance indices (KPIs).

Business Goal:

To create for the planning division director of ECDA a dynamic dashboard for tracking KPIs and answering the following business questions:

Accessibility and Sufficiency of Childcare Facilities: Enrolment Rate

1. Is the average child care centre enrolment rate in each town optimal? Are there areas where new centres are required?

Measurable KPI: Difference between the desired level of enrolment rate and the observed enrollment rate in the last four quarters.

Both under-enrolment and over-capacity are suboptimal. Optimal enrolment is required to ensure that centres are able to reap economies of scale and cover operating costs, without compromising the accessibility and sufficiency of childcare facilities.

Findings: Towns such as Bukit Timah, Bukit Batok, Choa Chu Kang, Tampines, Kallang, Sengkang and Punggol are currently in the Red Zone. While some of the districts such as Bukit Batok, Bukit Timah and Kallang have under-enrolment issues, the rest of the towns are at risk of having under-capacity issues (e.g newer towns such as Punggol, Sengkang, Choa Chu Kang, as well as Tampines).

Among the under-capacity towns, the newer estates have more future demand for child care centres compared to the older towns. This is suggested by the difference between the current number of dwelling units and the projected ultimate. For instance, Punggol is estimated to expect a doubling of housing units in the next few years. In such areas where an influx of population and a potential hike in absolute number of children born are expected, ECDA should aggressively increase the number of child care centres in order to meet the growing demand for such services in years to come.

On the other hand, older towns such as Bukit Timah has almost reached it’s maximum housing and population capacity, with less than 8% of dwelling units to be expected in the future. Furthermore, it also has under-enrolment issues. Given this backdrop, the growth of child care centres in these estates needs to be moderated. While it is not possible to force childcare centres to close, it is perhaps ideal to let market forces take over.






2. Will the current plan of building child care facilities in areas of risk succeed in reducing the number of towns whose enrolment rate falls outside the optimal zone?

Measurable KPI: The number of towns that has an enrolment rate falling outside the optimal zone.

To address this question, we project the absolute enrolment figures into 2017 Q3 and Q4, and considering the additional capacity that would be added due to new centres being built in these two quarters, we derive the projected enrolment rate. By comparing the projected enrolment rate and the target overall FY17 level, we will be able to assess if the planning division is on track to meet the KPI. For this KPI, the target is not strictly 0. Since it takes a long-term view to see improvements in optimal enrolment rates, a more attainable and time-bound target would be to aim for at least an incremental improvement.

Findings: While the appropriate intervention strategies in the younger town could move places such as Sengkang and Punggol out of the red zone, there will be towns such as Marine Parade joining the list. Yet, for the case of Marine Parade, as the remaining housing unit capacity of 8% suggests that there may not be much room for new HDB estates to be built, growth in these estate needs to be moderated.


Affordability: Fees and Charges

3. Will an average family in Singapore be able to afford child care services?

Measurable KPI: The overall median monthly fees for full-day child care services

It is important to assess the growth of child care industry median fees. Assuming an annual inflation rate of 3%, a hike in child care prices beyond this level can potentially reduce child care affordability. Ensuring a reasonable growth of average child care fees in each town is a critical part of pro-family policies.

Findings: The KPI target is set to be 3% above FY16’s median fees. In this respect, performance for this KPI is expected to be very well within the FY17 target. This could be due to the growth of partnership programmes, as discussed in the following question.

4. How will the growth of partnership programmes such as the Anchor Operator (AOP) and Partner Operator (POP) schemes help anchor the growth of industry median fees?

Measurable KPI: Proportion of child care centre capacity contributed by centres under the AOP and POP schemes as a ratio to the proportion of Singaporeans living in HDB.

This KPI is significant as one of the main goals of the AOP and POP schemes is to help anchor the growth of industry median fees. These operators, which are government-appointed and receive government grants, are child care centre service providers which have to cap fees, at $720 and $800 a month respectively for full-day childcare, among other criteria. The growth of operators under these schemes will help anchor the increase in childcare industry median fees. The KPI is measured as a ratio to the proportion of Singaporeans living in HDB in order to factor in regional characteristics (for e.g. Bukit Timah has a higher percentage of private home owners, hence the preference may be for private childcare services instead of childcare operators under the AOP/POP schemes.)

Findings: Comparing the change in median fees and the change in AOP & POP Ratio Over Time, we see an interesting but not unexpected correlation. As the ratio dipped in 2016 Q4, we may observe a corresponding increase in median fees. As the ratio increased gradually over the next few quarters, we see a corresponding trend of decrease in median fees. This suggests that the growth of AOP and POP schemes does have impact on median fees that can be explored further.


However, an anomaly is observed at Bukit Merah:


Quality: Partnership Programmes

5. How can ECDA achieve a balance between anchoring the growth of industry median fees and not compromising the quality of childcare services?

Measurable KPI: Proportion of child care centre capacity contributed by centres under the AOP and POP schemes as a ratio to the proportion of Singaporeans living in HDB.

The same AOP/POP scheme ratio KPI can be used as an indicator of the quality of overall delivery of childcare services in Singapore. Both AOP and POP schemes, besides having stringent fee caps, also ensures good quality developmental programmes by subsidizing the professional development of centre leaders, teachers and caregivers. The greater number of operators achieving eligibility for these schemes will not only help anchor the growth of fees, but also ensure good quality childcare services.

Findings: There is an overall gradual growth in AOP/POP scheme ratio. Most of the towns have relatively high ratios, with the exception of Bukit Merah, Clementi and Geylang. The low ratio shows that these are the estates which require specific attention. More can be done to introduce AOP/POP scheme child care centres to these towns.

While most estates experience a rise in AOP/POP ratio in the past year, Tampines registered a falling ratio instead. This may indicate a sudden influx of non-AOP/POP operators. There is no guarantee that these operators will meet the necessary quality standards. That being said, the AOP/POP ratio is still within the acceptable range. Hence, ECDA’s attention can be diverted to other areas of need.



Summary of KPIs

Category KPI FY17 Target Derivation
Accessibility and Sufficiency Overall Enrolment Rate Green Zone: 75 +/- 2.5%
Risk Zone: 75 +/- 5%
Red Zone: < 70% or > 80%An enrolment rate of around 75% is assumed to be most optimal as most of the child care center resources are utilized at this rate, but there remains reasonable bandwidth for unanticipated surge in needs.
Number of Towns Outside Zone Green Zone: < 6
Risk Zone: 6 (No improvement)
Red Zone: > 6Based on 2016 Q4 KPI snapshot value of 6.
Affordability Median Monthly Full-Day CC Fees Green Zone: < $917 (< 3% increment)
Risk Zone: $917 – $935 ( < 5% increment)
Red Zone: > $935 (> 5% increment)Inflation rate in Singapore averaged 2.64% in the past decades. Hence, an increment of approximately less than 3% will be ideal.
Affordability and Quality Proportion of AOP/POP CCCs (By Capacity) Green Zone: > 0.62
Risk Zone: 0.6 – 0.62
Red Zone: < 0.6

Dashboard Design and Assumptions

31.pngLink to Dashboard

As the dashboard is designed for the planning director of ECDA in mind, here are the important considerations:

  1. The most relevant KPIs are placed at the top left hand corner for readability, along with each KPI’s target.
  2. Blue and orange are used as informative colours
  3. Other charts illuminate the KPI trends by introducing the time component along with a projection of the fiscal year end results.
  4. The map serves as a filter to drill down to the town level. Besides, it also shows the median fees colour-coding each town polygon for greater appreciation of the regional characteristics. The map also displays the location of new child care centres.
  5. Axis breaks have not been used due to large differences in values across towns.

List of Assumptions and Disclaimers

  • Due to data availability, the current quarter is assumed to be 2017Q2.
  • Due to lack of ECDA official enrollment, capacity and centre information for 2015 and 2016, 2016Q3 statistics are simulated.
  • Assuming new child care centres, its license type (AOP/POP/NONE), its capacity as well as the potential number of children born to a particular town are jointly predicted by HDB and ECDA. Mock up enrollment statistics (predicted) are used to represent this piece of information for 2017Q3 and 2017Q4.
  • Median fees for 2017Q3 and 2017Q4 are projected using an inflation rate of 5%.
  • All choice of KPI are stipulated using existing data to reflect a reasonable and realistic simulation.

Data Sources:

  • Enrollment, capacity and centre information

  • HDB Housing Information

Annual reports from 2010 to 2016 are scrapped for data

  • Population Data

Utilized Data:

Singapore Resident by Planning Area/Subzone and Type of Dwelling, June 2000-2016
Singapore Resident by Planning Area/Subzone, Age Group and Sex, June 2000-2016

Team Members:

Liao Jiexun Jacob (A0163410W)
Luo Jiayu Joy (A0065569N)
Xu Hao Chang (A0163416J)
Zhu Wei (A0163199U)

Singapore ICT development KPI —

Singapore ICT development KPI


On 24th November 2014, Singapore smart nation initiatives was officially initiated by Singapore Prime Minister Lee Hsien Loong. As part of the Smart Nation initiative, Singapore government target to improve the Information and Communication Technology(ICT) development and capability of Singapore.

According to IDI, the ICT development of a nation can be determined by different indicators to capture the respective ICT readiness, ICT intensity and ICT capability. Based on the 2016 IDI ranking, Singapore currently are positioned at 20th of IDI ranking and hence there are a lot of efforts need to be done by Singapore government to improve the ranking of Singapore.

Goal & Target Audiences:

The target audience of this dashboard is the top management of GovTech. The dashboard can provide them an overview of the Singapore ICT development benchmark against the top countries in terms of their ICT capability set-up

Besides, the dashboard also can provide at-a-glance views of Key Performance Metrics(KPI) in terms of ICT infrastructure capability, ICT development spending, ICT manpower capability and 4G mobile adoption of Singapore as a measure of effectiveness of Smart Nation initiatives and as a reference for GovTech top management on their nationwide ICT roadmap decision making.


  1. What is the current average internet speed of Singapore? How Singapore position against the top developed nation and what is the target average internet speed for Singapore?
  2. What is the current internet bandwidth per user of Singapore? How Singapore position against the top developed nation and what is the target internet bandwidth per user for Singapore?
  3. What is the distribution of the recent Singapore government ICT spending in different ICT focus area as compared to previous year?
  4. What is the 4G mobile adoption trend of Singapore mobile users and what is the impact of switching the users between different technologies (3G to 4G, 2G to 3G)?
  5. What is the Singapore ICT manpower development trend and what is the impact of launching of Smart Nation on the ICT job creation of Singapore?

KPI Metrics:

Singapore ICT development benchmark

  1. Ranking of United Nation ICT Development Index(IDI)
  2. World ranking of average Internet connection speed(Mbps)
  3. Target average Internet connection speed(Mbps)
  4. World ranking of Internet bandwidth per User(Bit/s)
  5. Target Internet bandwidth per User(Bit/s)

ICT readiness

  1. Average Connection Speed of Singapore(Mbps)
  2. Percentage of Individuals using the Internet
  3. Percentage of households with Internet access

ICT intensity

  1. Percentage of mobile broadband subscriptions
  2. Singapore mobile Subscription trend by technology type

ICT capability

  1. Singapore government ICT spending breakdown
  2. Singapore ICT manpower availability trend

Data Collection:

Most of the technology capability data such as average internet speed and percentage of Internet access on Singapore GovTech open data platform are not up to date until recent year. As some of these indexes are changing frequent on quarterly basis the latest ICT infrastructure related data used to build the dashboard has been collected from online resources as listed below:

Dashboard Design:

Below is the overview of the dashboard.

CA1_Singapore ICT development KPI

The color of the dashboard has been kept under 10 types to avoid visual overwhelming. The position of Singapore for two of the world ranking chart has been highlighted to emphasis to relative position of Singapore against different nation. The 5 important KPI figures has been highlighted for clearer visualization as compared to charts.

Different charts have been used in the dashboard due to different consideration as explained below:

Gauge meter has been used to represent the current internet speed and bandwidth per user of Singapore against the target figures. The metric with below or above the 50 percentiles of target metrics are represented with a red and green meter respectively.

A line chart has been used to better visualize the trend of mobile subscription trend by technology type across the period of time. A 100% stacked bar chart has been used for the visualization of the breakdown of ICT spending and placing side by side with previous year distribution for clear comparison.

Finally, a stacked column chart has been used to show the trend of Singapore ICT manpower availability across the time period as there are two groups of manpower (employed and vacant) which sum up to be the total ICT manpower availability of Singapore.


What is the current average internet speed of Singapore? How Singapore position against the top developed nation and what is the target average internet speed for Singapore?Q1

The current average internet speed of Singapore is 20.3 Mbps and positioned at 7th of the world. South Korea has an astonishing average internet speed of 28.6Mbps and hence it has been set as the target internet speed for Singapore to look forward.  Also, out of nation with the top 10 fastest internet speed, 4 is from Asia indicated the fast development of ICT capability of Asian countries in terms of ICT readiness.

What is the current internet bandwidth per user of Singapore? How Singapore position against the top developed nation and what is the target internet bandwidth per user for Singapore?


The current internet bandwidth per user of Singapore is 0.74 Mbit/s per user and Singaporean has successfully positioned themselves at the 3rd place of the world in terms of bandwidth per user. However, as indicated in the horizontal bar chart, there is still a huge gap between Singapore and the top 2 nation. Hong Kong and Luxembourg is more than 5 times and 9 times than Singapore respectively in terms of bandwidth per user and hence it is reasonable to set the target bandwidth per user to be the 4.16Mbit/s per user and currently Singapore is at the low percentile of the target and hence more efforts need to be done to increase this metric.

What is the distribution of the recent Singapore government ICT spending in different ICT focus area as compared to previous year?


We can observed that Singapore government has put more focus on ICT Security (22%), Digital & Data (22%) and Smart Nation initiatives(9%) as compared to 2016(2%, 4% and 1% respectively) where back then the focus is heavily levied on the ICT infrastructure(66%) and Web & Services(16%). This is in line with the Singapore government vision to further improve the ICT security of Singapore following a few incidents regarding the security breach of public sector ICT infrastructure. Also, it can be concluded that Singapore government have recognized the importance of data analytics & science in high level decision making be it in business or public sector and hence decided to invest more on Digital & Data development of nation. As shown in the 100% stacked bar chart, the distribution of the ICT spending is more evenly distributed across a few important ICT development pillars as compared to 2016 and it is clearly more sustainable for the long term ICT roadmap development of Singapore.

What is the 4G mobile adoption trend of Singapore mobile users and what is the impact of switching the users between different technologies (3G to 4G, 2G to 3G)?


From the line chart, it can be shown that the 2G users has been steadily flat and start to decrease from 2010 after the introduction of 3G network into Singapore on May 2005. The number of 3G user increase steadily from 2005 to 2013 and along the period many 2G user have switching from 2G to 3G mobile plan. The latest 4G network was introduced on March 2013 and there is a mass switching of 3G to 4G subscription by telco and can be visualized by a sharp drop of 3G users on that month. There is a sharp drop in 2G subscriptions and sharp rise of 3G subscriptions on March 2014 which represent a mass switching of 2G to 3G by telco. Currently most of the mobile users are using 4G technology and this represent an encouraging 4G adoption rate among Singapore citizens.

What is the Singapore ICT manpower development trend and what is the impact of launching of Smart Nation on the ICT job creation of Singapore?


The number of ICT manpower availability has increased steadily from 2003 to 2015, there more ICT vacancy available in recent years indicate there is a requirement for Singapore government to train more ICT professional to be able to meet the manpower demand by the industry. However, the increment of employed ICT experts is slow down from 2008 to 2014 although there is more ICT vacancy created. This represent a manpower gap among the ICT industry.

Since the launch of Smart Nation initiatives of November 2014, there is a lot of info-communications jobs created as shown in the stacked bar chart of 2015. In is advisable for the top management of GovTech to launch more short professional ICT courses for the unemployed middle age PMET to assist them to switch to this industry as there is a supply.

Team Member:

Huang Fuxing(E0147025)

Low Kang Jiang(E0146478)

Tan Hui Xing(E0146437)

Tey Peng Mok(E0146914)

Zhang Huaipeng(E0147003)