RiskMappers Proposal

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PROPOSAL

POSTER

APPLICATION

RESEARCH PAPER

PROJECT MOTIVATION

Risk in Singapore Since 2014, Singapore has been striving to achieve the vision of being a smart nation. The most integral step in this direction is the analysis and assessment of risk, for it is only by doing so that we can build a healthy, safe and prepared smart nation. After all the steps taken by the Singaporean government and society to be more risk-free, it is important to understand our vulnerability - in terms of dominant contributing factors, location and key institutions that could potentially be affected by these risks. To address this, we identify the risks posed to a specific target group of the population, school students, so as to narrow our focus and understand these risks better. Additionally, we will analyse the public service infrastructure in place in terms of 24 hour response institutions. We demonstrate the potential of R shiny app using interactive map to explore and analyse both sides of vulnerability and preparedness with respect to risk. Risk and hazard are terms commonly used to describe aspects of the potential for harm caused by environmental factors and other agents. In Singapore’s endeavour to be a healthy, safe and prepared smart nation, the most important age group of the population is of children - the future generations of our country. Children and teenagers aged 6 thru 16 are in the most susceptible and impressionable years of their life and therefore, it is important for us to better understand the vicinity of institutions that younger generations attend, the key ones being primary and secondary schools. This analysis will also help parents in choosing schools while being aware of the potential risks and vulnerabilities their children might be exposed to. For the sake of scope, we have restricted ourselves to three types of risks - health (tobacco and unhealthy eateries), entertainment (arcades) and terrorist (petrol stations, as a representative of the same). We then analyse the total number of risk factors to assign a risk score to each school that is later mapped out. With the launch of SGSecure in 2016, it is evident that Singapore’s government has been taking emergency preparedness seriously. Therefore, the second part of our analysis, the response analysis is with regards to the key public service institutions that are expected to operate 24/7 and include emergency rooms in hospitals, fire stations and police stations.

PROJECT DIRECTION

Geographical Risk Analysis	Each layer represents a specific area of risk which certain institutions pose to a student. By including each layer it allows users to visualize possible risks in each locale. Layers - Health Risk (Tobacco Store / Unhealthy Eatries) - Terrorist Risk (Petrol Station) - Entertainment Risk (Arcade)
Functionality	1. Visual Risk Analysis - With our visualization, users will be able to determine the possible risk exposure in each school. - Upon adding layers the increased overlapping areas will increase in color intensity for the school. - The more saturated the color, the more risk exposed the school is. 2. Statistical Risk Analysis - Our visualization calculates the risk score based on *distance of each locale* from a potential risk point. - Users will be able to view user selected locales "risk score" to determine how much risk is each area exposed to.
Usage	Our visualization allows users to comprehend how much potential risk each area is exposed to. From this information, we hope to aid in Singapore's goals of Smart City planning to take into consideration not only ease of access to the population but also the risk different institutions pose to inhabitants.

PROJECT OBJECTIVE

	Issue 1 1. Identify and highlight the institutions that conduct activities that pose risks to schools and students 2. Analyse and evaluate the risk score of each school
	Issue 2 1. Identify and highlight the institutions that conduct activities that pose risks to society 2. Identify and highlight the institutions that could respond to these risks and their response limitations 3. Analyse and evaluate whether or not a risk can be responded to in a given time frame based on limitations such as coverage of response institutions

PROJECT DATA SOURCE

Our team's datasets are retrieved from https://data.gov.sg

Title	Format	Used for Issue 1 or 2	Website Link
Primary/Secondary School	CSV	Both Issue	https://docs.onemap.sg/#onemap-rest-apis
Petrol Station	Unformatted (format to csv)	Issue 1: Risk to Students	https://www.caltex.com/sg/find-a-caltex-station.html https://www.esso.com.sg/our-stations https://www.spc.com.sg/our-business/spc-service-station/station-locator https://www.shell.com.sg/motorists/shell-station-locator.html
Unhealthy Eateries	KML	Issue 1: Risk to Students	https://data.gov.sg/dataset/healthier-eateries
Tobacco License	CSV	Issue 1: Risk to Students	https://data.gov.sg/dataset/listing-of-licensed-tobacco-retailers
Acrades	Unformatted (format to csv)	Issue 1: Risk to Students	http://www.streetdirectory.com/businessfinder/company_branch/157187/127270/ https://www.yelp.com.sg/search?cflt=arcades&find_loc=Singapore http://www.timezone.com.sg/Location
Hospital	CSV	Issue 2: Response Time	https://docs.onemap.sg/#onemap-rest-apis
Police Station	CSV	Issue 2: Response Time	https://docs.onemap.sg/#onemap-rest-apis
Fire Station	CSV	Issue 2: Response Time	https://docs.onemap.sg/#onemap-rest-apis

PROJECT TIMELINE AND MILESTONES

This a ROUGH timeline of our entire project. Milestones indicated are according to IS415 AY1718 Project Wiki Page (Detailed timeline will be updated)

PROJECT FUNCTION COMPLETION

Function	Developer	Expected Completion Date	Status
Required Working Files: Sorting of SHP/KML and CSV files (get required conversion if needed) for application	Kaiyang	Week 10	Done
Issue 1: Conversion of Shape File to Raster Data	Kaiyang	Week 10	Done (REMOVED)
Issue 1: Linking required variables from Server code to UI code	Kaiyang	Week 11	Done
Issue 1: Set up of Raster Data based on Singapore Map	Kaiyang	Week 11	Done (REMOVED)
Issue 1: Mapping Data onto Leaflet	Kaiyang	Week 11	Done
Issue 1: Calculation of Raster Data using Interpolation to show choropleth map on Singapore Map	Kaiyang	Week 12	Done (REMOVED)
Issue 1: Integrating Code with UI	Kaiyang	Week 12	Done
Issue 1: Create buffer for each risk and calculate the risk score for each school	Kaiyang	Week 13	Done
Issue 1: Select the specific of risk within the buffer for each school	Kaiyang	Week 13	Done
Poster	Kaiyang	Week 13	Done
User Guide: PDF User Guide	Kaiyang	Week 14	Done
Issue 2: Testing One Map API to extract location data	Mark	Week 10	Done
Issue 2: Conversion of One Map CSV data to Spatial data for mapping	Mark	Week 10	Done
Issue 2: Calculate route distance and timing of Risk from K Nearest Neighbour of the Response layer using One Map API	Mark	Week 11	Done
Issue 2: One Map API Extraction of Location Data to CSV files	Mark	Week 11	Done
Issue 2: Mapping data onto Leaflet Map	Mark	Week 11	Done
Issue 2: Linking required variables from Server code to UI code	Mark	Week 12	Done
Issue 2: Integrating Code with UI	Mark	Week 12	Done
User Interface: Number Sliders (for buffering / proximity timing), Dropdownlist (for Color, Basemap)	Mallika	Week 10	Done
User Interface: Set up observation on R Server Code	Mallika	Week 10	Done
Backend: Pre-loading of SHP/KML/CSV files to the server	Mallika	Week 11	Done
User Interface: Set up dynamic input/output and link with Map	Mallika	Week 11	Done
User Interface: Home Page to explain the project's goals and descriptions	Mallika	Week 12	Done
Server Development	Mallika	Week 13	Done
Server Debugging	Mallika	Week 13	Done

Most functionalities are delayed due to the requirement of completing other module's project work as the deadline is earlier. The team will be dedicating the entire 6th April to 10th April to be working on GeoSpatial Project and only GeoSpatial Project

PROJECT REFERENCES TO RELATED WORK

Over Function (https://cran.r-project.org/web/packages/sp/vignettes/over.pdf)

Using this as a reference to calculate the total risk score of each school based on whether their location is within the certain distance buffer of the risk factor Used for: Issue 1: Risk to Students

Dengue Outbreak Mapping (http://outbreak.sgcharts.com)

Using this as a reference to create a colored distance buffer for user to know if a location that user chose is beside a risk location. Used for: Issue 2: Reponse Time

Shiny Example (http://shiny.rstudio.com/gallery/superzip-example.html)

Using this as a reference to create R shiny map. Used for: UI

Distance Length Mapping (https://rstudio.github.io/leaflet/shiny.html)

Using this as a reference to create setting for response time estimation. Used for: Issue 1: Risk to Students & UI

PROJECT STORYBOARD

Issue 1: Risk to Students

Our team plans to create a dashboard for our users to run an analysis on how different risk (Petrol Stations, Tobacco Store, Unhealthy Eateries and Arcades) could affect schools within their vicinity. Risk score will be calculated for each school. Users will be able to look at the highest and lowest risk score schools. Users will also be able to find out specific risk and their location for each school on in a table form.

Issue 2: Response Time Analysis

Our team plans to create a dashboard for our users to run an analysis on different government bodied (Hospital, Police Station, Fire Station, etc) location and their ability to respond to emergency events at certain high risk location. User will be able to select a specific government body, response time and risk location to reach.

PROJECT PROTOTYPE

Issue 1: Risk to Students (NEW)

Our team plans to create a dashboard for our users to run an analysis on how different risk (Petrol Stations, Tobacco Store, Unhealthy Eateries and Arcades) could affect schools within their vicinity. Risk score will be calculated for each school. Users will be able to look at the highest and lowest risk score schools. Users will also be able to find out specific risk and their location for each school on in a table form.

Issue 2: Response Time Analysis

Our team plans to create a dashboard for our users to run an analysis on different government bodied (Hospital, Police Station, Fire Station, etc) location and their ability to respond to emergency events at certain high risk location. User will be able to select a specific government body, response time and risk location to reach.

PROJECT KEY CHALLENGES

No.	Key Technical Challenges	Description	Proposed Solution	Outcome
1.	Unfamiliarity with R	The team plans on using R and creating an R dashboard for the project.	- Initial hands-on experience during class - DataCamp course on R shiny and geospatial analysis - Independent learning on R - Independent learning on R - Peer learning and sharing	We managed to solve our technical challenge with the following: - Found examples online - Asked Professor Kam for help
2.	Data Cleaning and Transformation	The data collected is generally in different formats. The scales of the data, the coordinate reference system etc. may all be different.	Having a systematic process while working together in order to maximise efficiency e.g. taking turns to clean, transform and perform checks on the data to ensure accuracy.	We managed to solve our technical challenge with the following: - Found examples of geocoding and csv conversion online - Used Excel to do basic data transformation - Used QGIS to convert csv to SHP file
3.	Integrating Relevant Data from Multiple Sources	Narrowing down data collected to what is relevant to our project is imperative before we can begin its analysis.	Working together and meeting up to understand and decide on what data to extract, to analyse or to reject.	We managed to solve our technical challenge with the following: - Meeting up and integrate together - Search for debugging solutions online
4.	Information Presentation	Determining the most effective way to visualise and display the data in an interactive format is of essence. It is necessary that the most important information is easily discernible from the map.	Gain exposure to different mapping techniques. Follow and revisit techniqes explored in class Also look at datacamp courses.	We managed to solve our technical challenge with the following: - Found examples online - Asked Professor Kam for help

PROJECT TOOLS AND TECHNOLOGIES

The following are the tools and technologies that our team currently aims on using (to be updated)

MEET OUR TEAM

Group 9: RISK MAPPERS

REFERENCES

No.	Website Name	URL
1.	Innovative Solutions for a Smart City	https://www.smartnation.sg/resources/innovative-solutions-for-a-smart-city
2.	Smart Nation Leveraging on Technology to Improve our Urban Environment	https://www.smartnation.sg/initiatives/Living/leveraging-technology-to-improve-our-urban-environment
3.	Topographic Map	http://en-sg.topographic-map.com/places/Singapore-1331643/
4.	Shiny Widget	https://shiny.rstudio.com/gallery/widget-gallery.html https://rstudio.github.io/leaflet/shiny.html
5.	Shiny Dashboard	https://rstudio.github.io/shinydashboard/ https://www.rstudio.com/resources/webinars/dynamic-dashboards-with-shiny/ http://shiny.rstudio.com/gallery/superzip-example.html
6.	Leaflet Notes	https://www.youtube.com/channel/UCVvv6HyBo9pg5-URPeJPQEw
7.	Isochrones Example	https://www.hydrol-earth-syst-sci.net/2/265/1998/hess-2-265-1998.pdf
8.	Isochrones Example	https://www.atlasobscura.com/articles/isochrone-maps-commutes-travel-times
9.	Over Function Example	https://cran.r-project.org/web/packages/sp/vignettes/over.pdf
10.	Sapply Function Example	https://cengel.github.io/rspatial/3_spDataOps.nb.html
11.	Batch Geocoding	https://www.doogal.co.uk/BatchGeocoding.php
12.	JSON to CSV	https://json-csv.com/
13.	CSV to SHP	https://mygeodata.cloud/converter/csv-to-shp

COMMENTS

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