User probability based on socio economic factors

2. USER PROBABLITY OF
INTERNET SERVICES BASED
ON SOCIO ECONOMIC
FACTORS
STUDENT NAME :Muhammad Waqas Ahmed
FACULTY : RIZWAN ALVI
COURSE : ADVANCE SPATIAL DATABASE AND PROGRAMMING
CLASS : MS(RS/GIS)

3. GROUP MEMBERS
WAQAS AHMED
SHARIQ IFTIKHAR
SIDRA KARWANI
ABDUL HAKEEM

4. 1/19/2019Department of Geography, uoK 4
 Internet is the fastest booming industry in Pakistan
for the past decade.
 Effective strategy is required for any business to
grow.
 GIS can help improve decision making progress by its
built-in statistical & spatial analysis.
 This study helps identify the most attractive zones in
terms of profit generation and helps devise a strategy
to plan infrastructure development.
BACKGROUND

5. 1/19/2019Department of Geography, uoK 5
PROJECT SCHEDULE
The image shows baseline schedule of the project.

6. Steel town was selected due to:
 Effective planning.
 Data accessibility.
STUDY AREA
1/19/2019Department of Geography, uoK 6

7.  To determine the number of users using Remote
Sensing data.
 To analyze what internet users already pay.
 Based on the gathered information predict what
users would be willing to pay or avail our service.
SCOPE OF WORK
1/19/2019Department of Geography, uoK 7

8. METHODOLOGY
Questionnaire & Database Design
Data Collection
Spatial Mapping
Linear Regression Modeling
Results
1/19/2019Department of Geography, uoK 8

9.  Questionnaire was designed for data collection using
google forms
 Each surveyed user was mapped geographically using
Google Earth.
 Due to time constraints sample size was limited to 51
units.
DATA COLLECTION
1/19/2019Department of Geography, uoK 9

10. DATABASE
(LOGICAL DESIGN)
 Each table is connected with user ID.
 The relational table has one to many relationship.
1/19/2019Department of Geography, uoK 10

11. SPATIAL MAPPING
The figure shows survey data mapped with respect to their attributes.
1/19/2019Department of Geography, uoK 11

12. DATABASE
(PHYSICAL DESIGN)
 Database of our choosing is file geodatabase due
to its simplicity.
 This project targeted a relatively smaller area &
did not require multi-user editing.
 Target_area.gdb has a feature class of
placemarks.
 Users are mapped on Survey.
 Where as blocks are polygon data .
1/19/2019Department of Geography, uoK 12

13.  Although in conceptual design our tables had one to
many relationship but while converting that design
into reality we had to alter the relationship.
 The relationship class we had to create was one to
one as we had only two features.
 The Survey feature was linked to blocks using one to
one relationship, the key was in text format
DB TABLE
RELATIONSHIP CLASS
1/19/2019Department of Geography, uoK 13

14.  The picture shows the
relationship class.
DB TABLE
RELATIONSHIP CLASS
1/19/2019Department of Geography, uoK 14

15.  To perform regression analysis Arcmap has built in
Geographical Weighted Regression tool in the system
toolboxes.
 Income was selected to be independent variable
(Constant horizontal axis) whereas Pay_already
column was selected as dependent variable.
 The results showed the predicted amount which each
household in that zone could pay.
LINEAR REGRESSSION
MODELING
1/19/2019Department of Geography, uoK 15

16. LINEAR REGRESSION
MODELING
1/19/2019Department of Geography, uoK 16

17. LINEAR REGRESSION
MODELING
500
1000
1500
2000
2500
3000
3500
20000 40000 60000 80000 100000 120000 140000
Predicted
Predicted
Linear (Predicted)
Income
T
a
r
i
f
f
1/19/2019Department of Geography, uoK 17

18. Serial # Block Predicted
Tariff
Number of
housing
units
Predicted
Monthly Fee
collected
1 A 890 - 1000 180 170100
2 D 1084 - 1381 198 244035
3 E 1381 – 1746 198 309573
4 G 1746 - 2881 50 115675
RESULTS
1/19/2019Department of Geography, uoK 18

19.  Spatial queries applied
to identify the area of
maximum potential.
1/19/2019Department of Geography, uoK 19
SPATIAL QUERIES

20.  The result shows that based on socio-economic factors
block E has the highest profit rate and can be the most
feasible target.
 Using the GIS model a feasibility of a service, supply
demand analysis & profit predictions can be easily
calculated.
 This model can be effective in identifying business
potential of a locality.
 Using Big Data Analytics & machine learning algorithms
this analysis can be performed on much larger scale with
automated tasks.
RESULTS
1/19/2019Department of Geography, uoK 20