4. 1. Team Profile
Team Name : The ONI
Team Vision : Endless charms that transcends nations
JunPyo Park
Majoring in Mathematical Science
Process Design
Data Analysis
Data Visualization
JaeHo Kim
Majoring in Management Engineering
Collect Data
Data Preprocessing
Deliver Insight
13. Brief Process Introduction Analysis PlanDataMethods Benefits
Has Multiple Features
- Longitude & Latitude
- Road Traffic Volume
- Number of Apartments
- Population Distribution
- Number of Office Worker
- Average Income Class
Multiple Logistic Regression
to get Odds or Probability
14. 5. Expected Benefits Analysis PlanDataMethods Benefits
Who is expected to benefit from our project?
- A CEO trying to increase the number of its franchise
- A person who needs a quantitative value of expected benefits of a
certain location
- A real estate agent trying to persuade customers using quantitative
values
- A person who is trying to invest in a certain company
Hello this is Team The ONI and I am the presenter JunPyo Park, I’ll talk about our topic.
This is the Contents
Here is motivation, Why there are no STARBUCKS near to UNIST?
As you can see, here is UNIST and STARBUCKS are over there, there are no STARBUCKS near to UNIST
Okay this is Team Profile
Our team name is The ONI and team vision is Endless cha…..
I’m JunPyo Park …..
And He is Jae Ho Kim ….
I’ll introduce some tools that we’ll using for this project
This is Gephi, Network Analysis and Visualization Tool
This is folium, it is GIS Analysis Tool
We can make interactive map like this figure, easily
Okay, now I’ll show you about Data Collection Plan
Okay, before introducing the collection plan, I’ll show what do we have now.
We now have the road network data for whole Ulsan.
We have Node, Edges, and it’s length as a edge weight.
This is collection plan, we have to combine this traffic data into our node dataset.
And this is for other data, population, apartment, income_class, number of office worker… etc…
Figure shows the number of house and population distribution for each unit area.
Next I’ll briefly show our analysis plan
Okay, this is Ulsan Map.
We divide it into appropriate lattice like this.
Then for each unit cell, it has multiple features
Conducting multiple logistic regression, we can get Odds or Probability, something that could be regarded as a location score.