In line with HPB’s action plan to promote healthy lifestyle and active ageing to build a nation of healthy people and mitigate the ageing population issues, our team has made use of geospatial temporal intelligence to provide useful information and analysis to support this initiative and make it easier for us to take ownership of our own health.
We do complete view of the health related amenities on common visited places followed by the analysis such as daily behavior of people, steps analysis, ellipses, directional distribution, hotspots to get more insights.
Average working hours in Singapore:
- Based on the statistics provided by the Ministry of Manpower*, the average working hours in Singapore for a full time is about 48 hours in a week (highlighted in red), which has exceeded the normal working hours of 40 hours in a week.
- Although the average working hour has an improving trend over the years, people in Singapore still spend a substantial amount of time at work.
- This leads to a challenge for working individuals to maintain a healthy lifestyle and work life balance.
Awareness of Health related Amenities:
- To make it easier for working individuals in Singapore to maintain a healthy lifestyle amidst their busy work schedule, there is a need to raise awareness on the list of health related amenities based on their travel patterns.
- In particular, in their course of travel between their workplace and home, it would be useful for them to be more aware of the complete list of health promoting amenities which are near and could be accessed easily to help them to overcome the time constraints and obstacles to achieve a health lifestyle.
Data Collection & Preparation
To kick start our initiative, we collected location data using both Openpath and moves phone application for our team. We then merged our dataset with available datasets from other teams. As the majority of the other team’s dataset is Openpath data, we decided to focus on Openpath data. Based on the merged dataset using Openpath, we performed the following actions:
- For records with missing names, we tried to identify the names using the type of phone and populate these records with dummy names.
- For records with email ids appearing under names, we populated the names as dummy names.
- There were records where the location points are outside of Singapore (e.g. Hong Kong, Batam, etc). As the scope of our analysis is in Singapore, we decided to remove such location records.
- As the timestamp was in UTC format, we performed a data transformation to translate the time to Singapore time (UTC+8) to facilitate better analysis.
- We created a date and Day of week field (Eg Monday, Tuesday etc) from the transformed timestamp.
- We created a list of GIS shape files e.g. gyms, healthier eateries, parks, sports fields to support our healthy life style analysis
- We also needed data from moves to support our analysis to optimize and improve the number of walking steps achieved. As the scope of our steps analysis is on working professionals, we decided to leverage on the data collected from moves (e.g. number of steps) and attempt to derive the no of steps with the location coordinates and time. As the required data was in separate tables in moves (e.g. summary, activities, places, storyline tables) and there was no single table with all required data attributes, we use SQL to extract the data from different tables and handle records with null location coordinates.
ETL (Extract, Transform, and Load):
During the data preparation stage using data from moves apps, we extracted the activities, places and storylines from the full summary. As the data is being split across multiple tabs and is not a 1 to 1 join, the ETL process is used to merge and load back into the ArcGIS platform.
Location by Day of Week
The locations are segregated by the day of the week (e.g. Monday, Tuesday, etc.) to analyze travel patterns according the the day of the week.
Mon-Thursday : Similar patterns can be observed.
Friday : Dispersal of points can be concluded.
Saturday-Sunday: Concentration of points at NUS are visible.
Mean Centre and Directional Distribution
- For each day of the week, the mean center and directional distribution analysis are applied on the location data to investigate how the locations are dispersed over Singapore.
- We can see that there is a spread in the northeast/southwest direction during the weekdays and greater concentration over the southwest region during weekends.
- The potential application based on the directional trend is that it allows us to know which area to concentrate our healthy life style efforts based on days of week, weekdays and weekends respectively. Eg we could organize exercise events at east coast park as a big team during weekday.
Easy Access to Health Promoting Facilities
Across Singapore, there are many amenities which could encourage people living in Singapore to live healthy. The amenities considered here are:
- Healthy eateries
- Sport Facilities
Buffering Travelling Pattern
- A 500 meters buffer area around the travelling pattern of the class is generated as a starting point to facilitate further analysis of travelling pattern intersecting with available amenities/facilities in the subsequent slide.
- This buffer symbolizes the deviation of an individual from his/her usual traveling pattern.
- 500m is chosen because it is a comfortable distance for walking to the next desired location.
Intersection With Amenities/Facilities
- The 500m buffer which was created is used to intersect the available facilities.
- Those facilities which are out of the buffer zone will not be considered and removed from the map.
- The remaining facilities are the ones which people can be easily accessed based on their travel patterns.
Individual Travel Patterns-Person A
- The previous analysis made used of location data derived from many people. This analysis could also be applied to a single individual to explore the facilities which he/she can potentially use.
- For example, the travelling patterns of person A is plotted on the map and the same workflow is applied. The results will show the health promoting amenities/facilities which person A can access with ease.
Individual Travel Pattern – Person B (by day of week)
For another example, this is the typical travelling pattern for person B on Thursday. For this individual on Thursday, the available easy accessible facilities are shown on the map which he can go to. This visualization can be extended to include any days of week.
- Usually step challenges consider only the total number of steps within a single day, without any location based analysis of the steps. Our analysis attempt to analyse steps of an individual comparing to other individuals:
- Based on the visualization of the number of steps (based on the size of the circle) for our team after work(ie after 6:30 pm) over a period of two weeks, it shows that our team mate TC and Yubo has a greater number of walking steps as compared with the rest of our team.
- Upon further interview with them, we realized that Yubo tends to walk 15 minutes from Buona vista MRT to his home although there is a bus service to his home. As for TC, he has a tendency to walk from the customer’s office to his office after work. For the rest of our team, the number of steps was relatively small due to the tendency to travel by public transport /car.
- Hence, in order to improve our walking steps to achieve a healthy number of steps amidst our busy schedule, it is recommended that we learn from the above model team members and refine our travelling patterns such that there is more walking activity or not driving to work during certain days of the week.
- It is important to note that walking has numerous benefits. It strengthens our heart, lowers disease risk, helps to lose weight, prevents dementia, tones our legs, bums, tums, gives us more energy, and makes us happy. Hence, with a slight tweak in our travelling patterns and lifestyle, it would lead to a big change in our health condition.
Step Analysis – Gertis Ord Gi (Hotspot)
- Applying the Gertis Ord Gi analysis on the step feature class for all the location point. We pinpoint the hotspots and coldspots in terms of number of steps recorded.
- Cold spot would mean that an individuals is recording lesser steps at a particular location. Hot spot would mean that an individual is taking greater steps at a particular location.
For example, we can see that there are some cold spot around the Marina Bay Financial Centre while there are hotspots around the Tanjong Pagar area.
- These two sets of hotspot and coldspots are derived from different individuals.
- Relating this to the hotspot analysis earlier, we can see that the individual situating at Marina Bay Financial Centre is rather stationary compared to the other individual which is situated around the Tanjong Pagar area.
- In this situation, reminder can be given to encourage the individual to move more rather than to stay stationary for long periods of time. As per advice from health experts, long periods of sitting day-in and day-out can seriously impact our health and shorten our lives.
Limitation and Possible improvements
- Lack of dataset features/attributes
- Age, Gender, etc. are not available in the current phase, but it will be helpful to include these attributes for analysis in future studies.
- Limited data
- The sample can be considered small due to the time constraints. Increase of sample size would be good for future studies.
- Data are taken from Openpath and Moves as the only limited sources.
- Ratings for health facilities (eg Accessibility, price, health grade, etc. ) could be added as attributes for data fields for better analysis in future studies.
Submitted by team:
- Lee Tai Ngiap
- Lwi Tiong Chai
- Gello Mark Vito
- Zheng Kaiyuan
- Huang Yubo