Analytics And Intelligent Systems

NUS ISS AIS Practice Group

Promoting Healthy Lifestyle in Singapore — May 21, 2017

Promoting Healthy Lifestyle in Singapore

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:

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  • 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.

*http://stats.mom.gov.sg/Pages/Hours-Worked-Summary-Table.aspx

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

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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:

Data Cleansing

  • 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.

Data Transformation

  • 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.

Feature Creation

  • 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):

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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.

04050607

Mon-Thursday : Similar patterns can be observed.

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Friday : Dispersal of points can be concluded.

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Saturday-Sunday: Concentration of points at NUS are visible.

Mean Centre and Directional Distribution

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  • 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:

  1. Parks
  2. Gym
  3. Healthy eateries
  4. Sport Facilities

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Buffering Travelling Pattern

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  • 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

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  • 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

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  • 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)

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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.

Step Analysis

  • 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:22
  • 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.

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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.

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  • 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
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Spatio-Temporal Analysis Of Real Estate Market Using Geographic Information System — May 8, 2017

Spatio-Temporal Analysis Of Real Estate Market Using Geographic Information System

Objectives:

           The objective is to analyse the spatial distribution of  real estate price and its variability over time. 

           Real Estate is a special type of commodity . The location of real estate plays an important role in determining the price of dwelling. The determination of prices in a given area is closely related to the spatial distribution of properties. Some of the considered important factors are MRT LINE , Amenities like Hawker centers etc 

Data Sources:

          Transaction for private dwellings was fetched from Urban Development Authority of Singapore and also the MRT Line.

Address Geocoding:

           The location of a real estate is presented in the form of street name which should be converted to Lat Long points. We have used Python Geocode api for converting the street name to Latitude and Longitude .

Exploratory analysis:

        We have analysed total number of transaction taken place in each year for the dwelling with respect to a particular range of Unit Price.

styled-line(2)

Estimation of Distribution in Unit Real Estate Price:

 Spatial interpolation method Inverse distance weighting is used to generate models of selected dwellings. 

         The first issue appeared during data loading . Transactions of various dwelling located in same building is linked to the same address point and had a same position.Thus the average of values are taken to convert to a single value. Maps of the growth of dwelling values throughout the 2014-2017 characterized by a significant price increase.

Interpolation_2014_2015Interpolation_2016_2017

These maps shows that there is an increase in transactions around MRT lines and also significant price increase between 2016 and 2017 within unit price range of 2300 – 2600

Summary:

In particular, the use of a single color scale on the “interpolation map” allowed for the sharp increase in the unit prices of dwellings in late 2016 and 2017 to be captured, as also evidenced by graphs . Simultaneously, areas where the increase was found to be the highest and places that do not respond to trends in the market were indicated.

Team Name: HackEarth

Team Members: Navneeth Goswami , Vignesh Srinivasan , Praveen Kumar

— May 6, 2017

Title: ISS Students’ Lifestyle & Rental Recommendation

Team Member: Huang Wei, Li Huangxing, Li Quiqi, Lim Chong Wui, Liu Huan

1.0 Objective:

  • ISS students behaviour analysis
  • Rental recommendation and comparison

2.0 Data Preparation

  • combine Moves & Openpath data
  • remove duplicated spatial-temporal data
  • remove outliers
  • group data into time periods of the day
  • identify and extract student home geolocations
  • find geolocations of places of interest using Google Map APIs

3.0  ISS Students Behaviour Analysis

This video shows the movement of ISS students during 3 weeks and can be viewed from the following link: https://youtu.be/ZF0_LR_Di0Q

Particularly, moving points will not disappear but accumulate gradually in map.
  •  Blue points represents the movement of each person
  • Red triangle shows the home of ISS students.
  • Green square indicates as ISS

As shown in the video, the moving points are too rambling and disorganized to easily identify the behavior of each student. That’s why we involve static maps like supermarket that student would frequently go, together with ISS students’ movement data to conduct behavior analysis.

3.1 How we conduct behavior analysis?1

3.2 A typical Day of ISS Students

24.0 Rental Recommendation

4.1 Why Use Hexagon?34.2 How we rank these hexagons?4

4.3 Density maps for 5 factors and overall recommendation:

density

4.4 Top 10 recommendations:

breakdown

Red areas:
suitable for students who prefer short travel time to school
– short distance to school
– do not have much sports facilities and amenities
Yellow areas:
suitable for students who want to balance travelling time and living convenience
– moderate distance to school
– rental is comparable
– more facilities compared to Red areas
Blue areas:
suitable for students who have budget concerns
– rental price is less expensive
– plenty of facilities and public transports

4.5 Student Home vs Recommendation

The No.1 hexagon (the nearest one to ISS) has highest density of students’ homes, followed by No.3 hexagon and No.7 hexagon.

Many students’ choices do not match the other top 10 hexagons. It may due to students’ lack of information of their amenities, convenient transportation, and good infrastructure.

stuvsRental

6.0 Summary

Student behavior analysis:

We studied only a typical day of ISS students, and assumed 4 kinds of places students normally go (amenities, parks, sports and healthcare).

Most students go to healthcare places or do sports between 3pm and 6pm, and go to amenities and parks between 6pm and 10pm

Rental recommendation model:

We assumed most ISS students live in either condo or HDB, and data collected are all from foreign students who are renting houses.

Our top 10 recommendations provide an overall evaluation, and add value to students who are going to make new choices

You can access our online map: http://arcg.is/2poQoTS

Spatio-Temporal Analytics – Shopping Mall Location — May 5, 2017

Spatio-Temporal Analytics – Shopping Mall Location

Geo-Spatial Analytics and Visualization of Shopping Mall Location Strategy

Objective

To find an appropriate location of building a shopping mall to meet the NUS students’ shopping demand. 

1. Data source and Data processing  

Data source

Raw data of all class students (Apps: Moves, OpenPath )

Parks, shopping malls, supermarkets locations in Singapore https://data.gov.sg/  

MRT stations, bus stops, main streets in Singapore https://www.mytransport.sg/

Data processing

After receiving the consolidated data of the whole class, firstly, we did data cleaning, such as combining the columns of variables and standardizing some of the variables, i.e. date and time. And then we divided the dataset by time, day and night, school days and no-school days. Then, we assumed that people always stay at home from midnight to 7:00 am next morning, so we can easily conclude these locations in this period of time, which is everyone’s home.

Also, we input the data set into ArcMap, and we found that on school days our activities radius is basically restricted within a small area, just school, and home. While on no-school days, our footprints are all over the country. We supposed that these places are some leisure and entertainment locations, such like parks, shopping malls, supermarkets and so on. Therefore, we collected these places’ location and then to find some relationship between them. In this process, we converted the postal codes and addresses into latitude and longitude.

2. Description of ISS students’ tracks in the daily life

To begin with, we used ArcGIS online to pinpoint ISS students’ tracks and homes and then used ArcMap to make a short video, which describes ISS students’ footprints as the time goes by on a specific day.

To dig more information, we divided all the points into two parts, school days and non-school days, because we assumed students’ behaviors on school days and non-school days are totally different. In addition, we also took the duration into consideration. When it comes to all the points of school days, students would like to stay at home or at school for a long time.

Z1.png

However, in terms of non-school days, quite a lot students moved to shopping malls or business central areas.

z2.png

By comparison, we drew a conclusion that ISS students have a high demand of shopping or enjoying their life in business central areas. Hence, we were thinking about a question: from the viewpoint of ISS students, is it possible for us to find a new place to construct shopping mall to make ISS students’ life much easier?

3. Requirements for Target Location Analysis

Required Data to Locate A Shopping Mall

In order to support our hypothesis, we have searched many data, then we found that 4 kinds of data maps are useful and available for our analysis. Respectively they are Street MAP, Home Address MAP(HA), Train Station Distribution MAP (TS), Shopping Mall Distribution MAP(SM), Bus Stop Distribution MAP, Shopping Mall Distribution MAP.      BS Bus Stop     

HA

Home Address

SM

 Shopping MallSMA

   Super Market

TS

Train Station(MRT)

Crucial Factors and Requirements for Location Analysis

Recently, in big cities, the most concerned issue is how to find a business location for a shopping mall, where is population-intensive and convenient for transportation and parking. Therefore, in order to pick the most perfect shopping center location for developers, we need to take many factors into consideration achieving the greatest economic benefits.

The first factor is convenience, in this part we need to analyze MRT and Bus Stop Distribution map, after reading many essays, implementing the MRT buffer of 100m and home buffer of 500m, because 500m distance is acceptable for those residents around on foot and the distance of 100m is convenient for those go to shopping malls by public transportation.

The second factor is competition, we must control the number of shopping malls in one region, with limited purchasing power, more shopping malls tend to share few profits.

The third factor is Agglomeration economy, under the influence of agglomeration effect, the new-start shopping malls will grow up quickly with some mature supermarkets around.

Buffer_Shopping Mall

Buffer of Shopping Mall (500M)
Buffer_Train Station

          Buffer of Train Station (100M)

Buffer_Home Address

Buffer of Home Address (500M)

4. ArcMap Analysis Process

1st Iteration Analysis

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Intersection of Buffer_HA+BusStop

Intersection Analysis – Buffer of Home Address and Bus Stop location for convenient purpose.

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                Erase Shopping Mall from 1st Intersection

Erasion Analysis – Removing existing buffer of Shopping Mall from remaining area of intersection analysis.
微信截图_20170505174714.png

1st Iteration Result

As we can see in this above map, the located area was chosen is still very large and dispersed, which is not suitable for us to found a shopping mall.

With the purpose of finding a more specific location for our shopping mall, we add train station data (MRT) into a new intersection layer and do 2nd Iteration Analysis as follows.

2nd Iteration Analysis

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 Intersection of Buffer(HA+TS)+BusStop

Intersection Analysis – Buffer of Home Address, Buffer of Train Station and Bus Stop for a specific location.
5

  Erase Shopping Mall from 2nd Intersection

Erasion Analysis – Remove existing Buffer of Shopping Mall from the previous outcome of intersection analysis.

微信截图_20170505174857.png

2nd Iteration Result

ArcMap Analysis – Final Location

After previous 2 Iteration Analysis, we pinpoint our shopping mall in three areas near our home address.

We know that Clementi has a shopping mall already, so there is no need to construct a new one in the near future. We draw a conclusion that it’s possible to build a new shopping center near Buona Vista.微信截图_20170505175333.png

5. Challenge & Limitation

The sample size is limited. Our data source comes from the app “Moves”, and only 26 students data available.It means that 26 students’ home addresses will be analyzed as the residential area.In order to solve this problem, we buffered the home addresses with 500 meters, which was treated as the residential zone.

The geospatial data understanding and preparation should be gained in a short time. As the topic is about the shopping mall location, how to analyze geospatial data with specific software is very important to get a proper and practical shopping mall location.We focused on the literature reviewing and software learning (eg.Arcgis, Carto) at an earlier stage, including some data format conversion and data searching.

The consideration of influencing factors is not integrated.As we known, the factors which will have an influence on the shopping mall location are very complex in the practical application, such as policies and regulations of government, the population density, landform, etc.In this data analysis, we take several main factors into consideration, not all.

DATA PIXIE:

Ma Min (A0163305N)

Yu  Yue (A0163377X)

Liu Cong (A0163299R)

Hao Suya (A0163339B)

Zhang Dongxue (A0163353J)

Geo-spatial Analytics and Geovisualisation of Students’ Household Surroundings —

Geo-spatial Analytics and Geovisualisation of Students’ Household Surroundings

 

Objective:
To give indicators about choosing a good place of residence for students in NUS ISS by visualizing household surroundings.

Background&Tools:
Most of students in NUS ISS are foreigners, so the first thing after they came to Singapore was to find an ideal place to live in. However, they won’t know whether it is a perfect match for them until they have moved in for a while, and some houses have weaknesses like higher price, long distance or bad environment.
It is very difficult to find a better place for yourself, because no one can have a trial for changing the accommodation, so the students need some indicators to help them to define a good place of residence.Here we use ArcGIS to do all visualization and analysis.

Data Sources:
The basic data we use is from the app “Moves”. It contains 1,845 records from 26 students, and we have putted marked all records on the map——each point represents one record and the size of it represents the length of duration the person stayed. Then we add some other data like bus stop location, highway network, pharmacy and playfield to do the analysis.

Data Processing:
Step 1: Visualize students movement track data from MOVES by using ANN statistical methods.
Figure 1 Visualization of data from MOVES

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Note: Each point represents one record and the size of it represents the length of duration the person spent on that place.
Insight: This figure shows where ISS students spent majority of their time and that the most popular area is around the NUS campus.

Step 2: Apply clustering and outlier detection.
Figure 2 Cluster and outlier

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Note: “Duration” is the input.The points of high cluster, like the purple ones, indicate the places we often go and stay for long, like campus and home. And the points of high outlier are places we stayed for long but only visit once or twice, like hotel or house of friends and relatives.
Insight: For example, the bright green point, located in somewhere in the campus but not ISS, may be another place like Utown or central library where the student spent a whole day there for self-study or group discussion, but it is not the place he or she usually goes, so defined as an outlier. The points of low cluster are places that we often go but only stayed for a while, such as food courts and bus stops near our houses, and the points of low outlier are places that we also spent a little time there and only visit once or twice, like a shopping mall far away from home.
Purpose: To identify and define points represent homes.(as Figure 3 shows)

Figure 3 Points represent homes

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Step 3: Add highway and main road data to explore the influence of noise to students’ homes by using buffer.
Figure 4 Distribution of Highways ,Main road and Common Road

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Note: Different kinds of road will have different average decibels of influence to the surroundings.
Benchmark of highways radiation radius: 70 meters and 100 meters.
Benchmark of main road radiation radius: 30 meters and 50 meters.
Benchmark of common road radiation radius: 20 meters and 10 meters.
Figure 5 Buffer of Highways,Main Road and Common Road

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Purpose: To find homes which are seriously influenced by noise.

Step 4: Add bus stop, pharmacy and playfield location data to explore the convenience of daily life.
Figure 6 Bus Stop of Singapore

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Insight and process: We put the location of all the bus stops in Singapore on the map.They are very crowded, implying that the foundation facilities are well-constructed in Singapore.We use the ‘Select by location’ tool and set the search distance as 500m, which means that if one’s home is less than 500m from the nearest bus stop, it will be selected.The result is shown in green ticks as shown in Figure 7.
Figure 7 Selected Homes by bus stop station

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Then, we put the location of all playfields in Singapore. With a distance of 1000m, we select some of the homes and the result is shown in blue ticks.
Next we put the location of all the registered pharmacies (shown as red crosses in Figure 8)in Singapore on the map, too. Some of the homes are selected with a distance no more than 1000m, which are shown in purple ticks.

Figure 8 Selected homes by playfields and pharmacies

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Step 5: Use the result we conclude before we finally marked those homes with the least noise and best convenience in the map.
Figure 9 Final result of good homes

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Conclusion:
We have applied clustering, outlier detection, buffer, ANN methods and selection functions to do this geospatial analysis about household surroundings of students in NUS ISS.

We just used the students data for sampling, actually we can add more data about life and from different kinds of people to improve this analysis and make some recommendations.

 

 

Submitted by Team HAHAHA EBAC04 Full-time
GAO RUOFEI     A0163436E
HE SHANGUI    A0163348B
LIU YIZHE         A0163191J
ZOU YUMENG  A0163394Y

Geo-Spatial analytics and Geovisualisation: Location Based Analytics to help meet emergency blood demands in Singapore — May 1, 2017

Geo-Spatial analytics and Geovisualisation: Location Based Analytics to help meet emergency blood demands in Singapore

Objective: To geovisualise the targets for public notification in times of emergency blood demands.

  1. As per Singapore’s HAS(Health Services Authority),only 1.87% of total Singapore’s population donated blood in 2016. This needs to be improved for future readiness.Emergency blood demands in hospitals increase in times of epidemics like SARS,swine flu etc.Though blood banks are reached out by the hospitals for meeting demands ,but voluntary donors’ blood collection can help in such situations.

In times of emergency blood demands in a hospital,if a public notification geotargeted to the people around a hospital zone is sent,it is more likely that the people  in hospital’s proximity will be able to reach the hospital quicker than the ones far away.

This objective of this blog is to geovisualise the targets for such public notification.

2.  Also,as per the HAS’s report, the maximum blood collection happens from Bloodmobiles.It will be more efficient to send bloodmobiles for donation drives to the places, where more  people are aggregated i.e the population hot spots.

pic1Data Sources:

Dataset description Data Source URL
Datapoints of all class students (to simulate the geotargeting) IVLE (Apps used : Openpaths,Moves,FollowMe)
Singapore Planning Area subzones Boundary https://data.gov.sg/dataset/master-plan-2014-planning-area-boundary
Singapore Streets,Hospitals,Bloodbanks in Singapore,MRT routes,lines,bus stops https://data.gov.sg/

Processing:

pic2.jpg

Plotting students’s movement data points, Singapore subzones,MRT lines routes.

pic7.jpg

Optimised Hot – Spot analysis for the students’ datapoints.

Purpose-To geovisualise the population hot spots.

pic3.jpg

Geovisualisation:   Building virtual geofences around hospitals is shown in below picture.Datapoints entering within these geofences will be targeted for public notifications.

pic4.jpg

pic5.jpg

pic6.jpg

Conclusion/Study extension : The process to target the population in a geofence for emergency public notifications was geovisualised.GeoEvent server can be used to visualise Geofences.

The population hot spots was geovisualised using the student’s location samples. The periodic population hot spots can be targeted to improve effectiveness of blood donation drives.

 

The published maps can be viewed online:https://pixels.maps.arcgis.com/home/item.html?id=49d9cdb1e89f421086e1dc28d80223c0#overview

Submitted By:

Name       Student ID
Arti Tyagi  (A0163343L)
Kriti Srivastav (A0163206N)
Pooja Gupta (A0163281J )
Sowndarya  (A0163229E )
Terence Ng  (A0035506M )

NUS- ISS Course – M.Tech.  Full Time

(National University of Singapore – Institute of Systems Science)

Dashboard Visualisation of Average Monthly Household Energy Consumption Per Year in Singapore — April 28, 2017

Dashboard Visualisation of Average Monthly Household Energy Consumption Per Year in Singapore

The Visual Dashboard allows the user to see the level of energy consumption across Singapore and for a specific region. 

March 9 2017

Pranav Agarwal, Rohit Pattnaik, Snehal Singupalli, Ashok Kuruvilla Eapen and Abhilasha KumarDashboard Image

Pieces in Dashboard:

  1. MAP: The map allows the user to see the level of energy consumption across the country and for a specific region. After a region is selected the Graphs numbered 4,5 and 6 reflect information only for the selected region.
  2. Statistics: The Statistics for a year represent the range of energy consumptions (Minimum, Average and Maximum) for Singapore.
  3. Top N Consuming Regions: The Top N Consuming Regions shows the rank of highest energy consuming regions for the selected year (N = number selected by user)
  4. Electricity Consumption by Dwelling Type: Electricity Consumption by Dwelling Type line graph represents the amount of energy consumed by each dwelling type for the year selected by the user and the region selected in the map.
  5. Year on Year Change in Consumption (in %): Year on Year Change in Consumption bar graph represents the rate of increase or decrease of energy consumption for the selected region in the map. The colour depicts the rate of increase or decrease which is in line with colours highlighted in the map.
  6. Year on Year Change in Consumption by Dwelling Type (in %): Year on Year Change in Consumption by Dwelling Type line graph represents the rate of change of energy consumption for the region selected in the map split according to the dwelling type (1/2 Rooms, 3 Rooms, 4 Rooms and 5 Rooms). The colours of the line represent each dwelling type.

Interactive Features:

A) Select Energy- Type: The user can choose any of the below energy type from the drop-down menu.

1) Electricity  2) Gas  3) Electricity per capita population  4) Gas per capita population

B) Select Year: The user can select any year between 2005-2015 from the drop-down menu.

C) Select a Number: The user can enter a number, N, between 1-10 to identify the top N energy consuming regions.

The Dashboard would enable the user to answer the following Questions:

  • Do bigger Dwelling Types consume more energy?
  • Do high electricity consumers consume more gas too?
  • Does a yearly change in consumption suggest the advent of more energy efficient devices?
  • What has been the trend in energy consumption over the past few years?
  • Which regions are among the highest consumers of Energy?
  • What is the pattern in consumption across years, across dwelling types?
  • What is the impact of population on the level of energy consumption?

Link to view the Dashboard on Tableau Public:  View Dashboard

Geo visualization of Age Dependency Ratio — November 19, 2016

Geo visualization of Age Dependency Ratio

Rajiv Krishna Prasad A0148542A

Sanjeev Kumar Chandran A0148398L

Mahendra Kumar KS A0148495N

Kotlanka Krishna Chaitanya A0150146R

_________________________________________________________________

Age dependency ratio (% of working-age population) data from 1960 till 2015 was used for the analysis, and was projected for 2016 and 2017.

Age dependency ratio is the ratio of dependents (people younger than 15 or older than 64) to the working-age population (ages between 15 and 64). Data is shown as the proportion of dependents per 100 working-age population.

The data obtained from the data source did not contain the latitude and longitude points for the countries. To incorporate these points, we obtained latitude and longitude data for all the countries from “http://www.csgnetwork.com/llinfotable.html”

We have converted countries into the signed degrees’ format (DDD.dddd), which are then used as markers on the map.
e.g.: Singapore into geographic coordinates latitude 1.290270 and longitude 103.851959

Findings/Insights:
1. Majority of the countries with least change in age dependency are from the European Union while those for increasing age dependency are from Africa
2. There was no general trend observed for countries with decreasing age dependency
3. There are more number of countries with decreasing age dependency (148) than countries with increasing age dependency (40)

Age dependency ratio for 1960:

adr-1960

Age dependency ratio for 2015:

adr-2015

Projected age dependency ratio for 2017:

adr-2017

In addition, the link to access the sheet published online is given below:

https://public.tableau.com/profile/publish/DA-Assignment/Geovisualization#!/publish-confirm

Geovisualisation on Human Development Index & Immigration in ArcGIS — November 14, 2016

Geovisualisation on Human Development Index & Immigration in ArcGIS

ArcGIS Visualization on International Immigration and changes in Human Development Index in OECD countries:

About Human Development Index

The Human Development Index (HDI) is a composite statistic of life expectancy, education, and per capita income indicators, which are used to rank countries into four tiers of human development.

Abstract

  • This Geo Visualisation is done to see Human Development Index growth over the years for the several countries from 1980-2010.
  • It is splitted into 5 groups with each group comprising 5 year HDI data.

HDI of 1980-85 :

1980-85

  • We can see that North American Countries and Australia have higher HDI.
  • African Countries and India has lower HDI.

We can see how the HDI is changing over the next 20 years visually below :

HDI changes over the years(1985-2010)

1985-90 :

2

1990-95 :

3

1995-2000 :

4

2000-05 :

5

2005-10 :

6

Summary of changes in words over the 20 years :

Based on the above visualizations,we can conclude that :

  • There is a marginal increase in HDI for India compared to the previous years.
  • Most of the South American countries and Russia has achieved the higher HID over the period of 20 years.
  • Barring few countries, all other African countries still possess a lower HDI. North American nations, European nations and Australia consistently has a higher HDI over the period of 20 years.

 

International Immigration:

ii

 

Effects of Immigration on Human Development Index in OECD countries

  • Thus, from the analysis of the visualizations obtained, it can be visualized that the immigration might be one of the factors contributing to an increase in Human Development Index in most of the OECD countries.
  • We can also infer that majority of immigrants target countries with a high human development index than their countries of origin.

Team Details :

  • Aravind Prabhu(A0148607Y)
  • Bala Gowtham(A0148536X)
  • Herald Nithesh(A0148613E)
  • Vignesh Mohan(A0148543B)

 

Earthquake Patterns and Impact: A Geospatial Perspective — November 13, 2016

Earthquake Patterns and Impact: A Geospatial Perspective

A.  Selection of Global Phenomenon

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

slide3

B.  Geospatial Data Collection

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

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

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C.  Geospatial Analytics & Visualisation

Earthquake Patterns

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

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

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Earthquake Impact

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

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

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D.  Conclusion

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

Web Map

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

Earthquake, an EBAC Geospatial Project 

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

Team Members

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

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