RiskMappers Proposal2
We are all aware that we are sucseptible to multiple types of risk at any given point in time. Since 2014, Singapore has been striving to achieve the vision of being a smart nation. This vision also includes the ability to respond to threats and risks in the most efficient and effective manner possible. It is for this reason that SGSecure was launched in 2016. One integral component of response time to risk is proximity to response institutions, such as Police Stations, Hospitals and Fire Stations. Therefore, for the purpose of this study, we would be analysing whether given your proximity to an institution that has a risk associated with it, the response institutions will be able to effectively and efficiently respond and attend to the threat. The response institutions are, 1. Hospitals, |
Geographical Proximity Response Time to Risk Analysis |
Each risk layer represents a specific area of risk which certain institutions pose to an individual. By including each risk layer it allows users to visualize possible risks in each locale,
Each response layer represents certain institutions response to risk based on the proximity of the location. By including each risk layer it allows users to visualize the institution's possible extension to the risk layer proposed by the user,
|
Functionality |
1. Visual Risk Analysis |
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. |
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
For now, our team's datasets are retrieved from https://data.gov.sg
Title |
Format |
Website Link |
Rail Stations and Line |
KML/SHP |
https://data.gov.sg/dataset/mp08-rail-station
|
SLA Cadastral Map |
KML/SHP |
|
Underground Line |
KML/SHP |
https://data.gov.sg/dataset/master-plan-2014-underground-line |
Zika Cluster |
KML/SHP |
|
Monument Site Boundary |
KML/SHP |
https://data.gov.sg/dataset/master-plan-2014-monument-site-boundary |
Community Use Sites |
KML/SHP |
|
Waste Disposal |
KML/SHP |
|
Malaria Receptive Areas |
KML/SHP |
|
Death Facilities |
KML/SHP |
|
CHAS Clinics |
KML/SHP |
|
Licensed Vet Centres |
KML/SHP |
|
Clinics (yellowpages) |
Unformatted |
|
Hospital (yellowpages) |
Unformatted |
|
Petrol Station |
Unformatted |
http://www.sgcarmart.com/news/carpark_index.php?LOC=all&TYP=petrol |
Flood List |
Unformatted |
Our team is still searching online for more available datasets. Unformatted Data are in different formats (For example, Excel Format). We placed them as part of our data because we require some of the information inside the data. Our team will be doing data cleaning / data transformation on data do not have a proper KML/SHP file format.
This a ROUGH timeline of our entire project. Milestones indicated are according to IS415 AY1718 Project Wiki Page (Detailed timeline will be updated)
Topographic Map Singapore (http://en-sg.topographic-map.com/places/Singapore-1331643/) |
Using this as a reference to create a risk intensity map of the entire singapore based on the locations of the particular high risk structure and its risk factors |
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. |
Weather Mapping (http://www.weather.gov.sg/weather-rain-area-50km) |
Using this as a reference to create each different risk in different colors. Adding checkboxes to allow filters in between risk based on conditions.
|
Our team plans to create a dashboard for our users to run an analysis on different risk factors and how it cause to some of Singapore key locations (Such as potential Terrorist attacks on MRT Stations) and how these risk locations can affect other locations nearby such as (Government Sectors location, MRT Stations, Schools, etc). User will be able to choose the specific risk type, potential risk locations, potential nearby locations that might be affected as well as set the buffer size for the risk identified. |
Our team plans to create a dashboard for our users to run an analysis on different risk factors and how it cause to some of Singapore key locations (Such as potential Terrorist attacks on MRT Stations) and how these risk locations can affect other locations nearby such as (Government Sectors location, MRT Stations, Schools, etc). User will be able to choose the specific risk type, potential risk locations, potential nearby locations that might be affected as well as set the buffer size for the risk identified. You may download our HTML file here, https://drive.google.com/file/d/1Lh5BZ8vJY7OM-P5F2Fcpz19wg4oWSzG6/view?usp=sharing |
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 |
(Add Later) |
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. |
(Add Later) |
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. |
(Add Later) |
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. |
(Add Later) |
The following are the tools and technologies that our team currently aims on using (to be updated)
- Leaflet
- Open Street Map
- Mircosoft Excel
- Shiny
- R Language
- Digital Ocean
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 |
|
4. |
Shiny Widget |
|
5. |
Shiny Dashboard |
https://rstudio.github.io/shinydashboard/ https://www.rstudio.com/resources/webinars/dynamic-dashboards-with-shiny/ |
We appreciate and cherish all comments. If you have any suggestion on how to improve our project. Please help us by keying in your name and comment. Don't worry, we want to know your name so that we can thank you and ask for more details. And we don't bite. (:
No. |
Name |
Date |
Comments |
1. |
Insert your Name here |
Insert Date here |
Insert Comment here |
2. |
Insert your Name here |
Insert Date here |
Insert Comment here |
3. |
Insert your Name here |
Insert Date here |
Insert Comment here |