Geo-Spatial Analytics and Visualization of Shopping Mall Location Strategy
To find an appropriate location of building a shopping mall to meet the NUS students’ shopping demand.
1. Data source and Data processing
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/
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.
However, in terms of non-school days, quite a lot students moved to shopping malls or business central areas.
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. Bus Stop
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 of Shopping Mall (500M)
Buffer of Train Station (100M)
Buffer of Home Address (500M)
4. ArcMap Analysis Process
1st Iteration Analysis
Intersection of Buffer_HA+BusStop
Intersection Analysis – Buffer of Home Address and Bus Stop location for convenient purpose.
Erase Shopping Mall from 1st Intersection
Erasion Analysis – Removing existing buffer of Shopping Mall from remaining area of intersection analysis.
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
Intersection of Buffer(HA+TS)+BusStop
Intersection Analysis – Buffer of Home Address, Buffer of Train Station and Bus Stop for a specific location.
Erase Shopping Mall from 2nd Intersection
Erasion Analysis – Remove existing Buffer of Shopping Mall from the previous outcome of intersection analysis.
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.
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.
Ma Min (A0163305N)
Yu Yue (A0163377X)
Liu Cong (A0163299R)
Hao Suya (A0163339B)
Zhang Dongxue (A0163353J)