=Paper=
{{Paper
|id=Vol-1494/paper4
|storemode=property
|title=Development of Geospatial Dashboard with Analytic
Hierarchy Processing for the Expansion of Branch Office Location
|pdfUrl=https://ceur-ws.org/Vol-1494/paper4.pdf
|volume=Vol-1494
|dblpUrl=https://dblp.org/rec/conf/iwost/NuradiansyahB15
}}
==Development of Geospatial Dashboard with Analytic
Hierarchy Processing for the Expansion of Branch Office Location==
https://ceur-ws.org/Vol-1494/paper4.pdf
Development of Geospatial Dashboard with
Analytic Hierarchy Processing for the Expansion
of Branch Office Location
Adrian Nuradiansyah1 , Indra Budi2
1
Technische Universität Dresden, Dresden, 01069, Germany
adrian.nuradiansyah@tu-dresden.de
2
Universitas Indonesia, Kampus UI, Depok, 16424, Indonesia
indra@cs.ui.ac.id
Abstract. One of the considered business opportunities that exploit the
geographical aspect is the branch office expansion. However, this activ-
ity still has some problems to learn various location criteria, either in
quantitative or qualitative form. Moreover, there is a need to analyze
those criteria quickly to get a proper location priority that will be ex-
panded. Therefore, this research develops a semantic technology of the
form geospatial dashboard using analytic hierarchy processing (AHP)
method that can display the distribution of location and its criteria in a
map. Furthermore, it recommends the list of top priority locations will
be expanded implicitly from the various kinds of data. This research
takes a case study in one local company in Indonesia. At the end, this
research shows that geospatial dashboard using AHP method is consid-
ered capable of producing accurate solution up to 90.48% to determine
the priority order of locations to be expanded.
Keyword: analytic hierarchy processing, geospatial dashboard, business intelli-
gence, geographic information system, expansion of branch office location
1 Introduction
Ventana [13] mentioned that the current business object that can be a valuable
asset in business analysis activities is the location. Companies can use it to
determine the distribution of location data customer, partner, or distribution of
such products more widely and deeply. The existence of this location analytics
also increases the role of business intelligence (BI) in analytics technology that
combines some aspects of geographic information system (GIS). Eckerson [3] on
the BI Delivery Framework 2020 stated that the future of technology intelligence
is needed in terms of changing the data and information into knowledge and
potential business opportunities. Even, Halper [5] created a survey which was
published in The Data Warehouse Institute (TDWI) report that stated that
there was a result of big data technology trends that will emerge since 2014
which is a technology with the analytics types such as visualization tools (96%
respondents), predictive analytics (88% of respondents), and geospatial analytics
(70% of respondents).
Based on this survey result, we note that more users need technology that
can support their location analytic skills in the form of geographic information
and equipped by predictive analytic capability to predict which business steps
that must be executed further and gives the semantic of given variety of data.
This kind of technology brings us to the conclusion that the needs of an enter-
prise to look the appropriate data in the various type, such as qualitative and
quantitative, and then process them into an obvious visualization are the sort
of challenges that always be faced to the capability of BI and GIS technology.
The output of this research is geospatial dashboard as the form of visualiza-
tion and geospatial analytics which uses Analytic Hierarchy Processing (AHP)
to strengthen aspects of predictive analytics to determine the location of branch
offices to be built first. This research takes a case study at Rekan Usaha Mikro
Anda (RUMA) Company 3 , which is one of the Microfinance Company that has
some various types of product and some branch office locations in Indonesia.
This company is feasible for a case study due to its main condition is fit to the
challenge of BI and GIS technology that is mentioned above. Their problem in
terms of analyzing location data to expand their branch office was divided into
two aspects, which are business and technology. In business aspect, they do not
find such a method that can calculate qualitative and quantitative data based
on many criteria and some location alternatives. In technology aspect, they still
try to look for the technology that can give recommendations to expand their
branch office in the priority order of location. In following, this paper will use
the involvement of Zone Expansion Manager (ZEM) of RUMA as a division that
takes care to the management of branch office expansion in RUMA.
This paper is written and arranged to focus on the use of AHP to solve
the problem of branch office expansion. The notion of geospatial dashboard will
be introduced only as a tool which support the decision making process. This
paper initially introduces the theoretical background of geospatial dashboard
and AHP in section 2. Then, in section 3 we explain the methodology of this
research, meanwhile in section 4 we provide the evaluation on the AHP method.
At the end, we put the conclusion and future work of this research in the last
section.
2 Preliminaries
Here we introduce the theoretical background of geospatial dashboard and AHP.
Since the main subject of these two terminologies are business intelligence, then
the detailed explaination about business intelligence can be read in [12].
3
www.ruma.co.id
2
2.1 Geospatial Dashboard
One thing that is currently needed for the non-technical user to understand the
data into the form of information, decision, and action is the translation of data
into a form of visualization. Technology that covers that needs is called data
visualization. According to [11], the data visualization is a gathering of various
domains of science, such as information technology, cognitive science, as well as
the graphical interface which aims to enrich the interpretation and the exchange
of the information of users.
According to [2], basically geospatial dashboard is a combination of BI and
GIS application on a single architecture that combines spatial analysis and vi-
sualization maps with a BI tool that is expected to assist companies in making
decisions related to the analysis of location data.
To develop the architecture of geospatial dashboard, we can start from the
point of view of BI architecture [8] . Then, to integrate it with the GIS technology,
[2] state that spatial capabilities are needed to be injected into the structure of
BI architecture. These capabilities include reading and writing GIS file formats,
performing coordinate transformations, and other spatial reference system.
2.2 Analytic Hierarchy Processing
According to [10], AHP is a theory that measures assessment of any expert or
executive in making decisions through pairwise comparison method with the use
of scales of priority. Pairwise comparison is a comparison between two alterna-
tive decision-making based on the specific criteria. Each criterion will also be
compared in pairs. This comparison is done to look at the interest rate of one el-
ement with another element. Results produced on this comparison was obtained
from qualitative assessment conducted by the experts who have the knowledge
to the problems of making the decision. This assessment could indeed be rela-
tive or inconsistent, but that is the focus of the use of AHP. AHP is expected
to solve that inconsistency problem of the form of mathematical modeling. The
scales of priority range from 1 to 9 which means that the bigger the priority, the
more important that criterion or alternative to the others. In addition, AHP is
not only used for qualitative assessment. The quantitative criteria can also be
involved in this method [6].
The paper [10] states that there are steps that can be followed to carry out
this AHP method as follows:
1. State the objective of the decision making, define the criteria, and pick the
alternatives. We turn these three components into an AHP Tree Structure.
2. Construct the pairwise comparison matrix for the criteria and then turn
the matrices into a ranking priorities of criteria by using the eigenvector [9]
calculation.
3. Construct the pairwise comparison matrix for every alternative based on a
certain criterion and then also turn it into a ranking priorities of alternatives
based on the chosen criterion by using the eigenvector calculation.
3
4. If the criterion is quantitative, then we only need to do a normalization on
the alternatives’ value based on that criterion. Otherwise, do the step 3.
5. Combine the ranking priority value for each alternative based on a criterion
into a matrix and then multiply that matrix by the criteria ranking we
obtained in step 2.
Every computed eigenvector gives us the relative ranking for our criteria or
alternatives. The eigenvector calculation is computed by running this algorithm.
1. Raise the pairwise matrix to powers that are successively squared each time.
2. Calculate the row sums and the normalize it.
3. The computation is stop to iterate when the difference between the normal-
ized row sums in two consecutive calculations is smaller than an assigned
value.
Example 1:
We want to choose which city that will be expanded based on the given criteria
and the assigned objective. By following the previous explained steps to do the
AHP cal culation, first we need to define the objective, criteria, and cities. To
make it general, we state that we have criteria C 1 , C 2 , and C 3 . Here we treat
the criterion C 3 as a quantitative criterion. Then, we have cities which are A1 ,
A2 , and A3 . We construct these components into AHP tree structure which is
depicted in Figure 1.
Fig. 1: Example of AHP Tree Structure
Next, we do the second step by constructing pairwise comparison matrix and
turn it a criteria ranking by eigenvector computation. The values in this matrix
are obtained by the agreement between the domain experts.
C C2 C3 Criteria’s Priority Value
1
C1 1 0.33 2.00 C1 0.287
C2 3.00 1 0.25 =⇒ C2 0.299
C3 0.50 4.00 1 C3 0.414
These values state that criterion C 3 is the most important criterion. Similar to
4
obtain the criteria ranking, we obtain the alternative ranking by running the
third step. First, we compute the alternative ranking based on criterion C 1 .
A1 A2 A3 C1
A1 1 4.00 0.11 A1 0.250
A2 0.25 1 5.00 =⇒ A2 0.352
A3 9.00 0.20 1 A3 0.398
Next, we compute the alternative ranking based on criterion C 2 .
A1 A2 A3 C2
A1 1 0.33 0.33 A1 0.131
A2 3.00 1 0.25 =⇒ A2 0.247
A3 3.00 4.00 1 A3 0.622
Since, the criterion C 3 is quantitative, then we only do the fourth step which is
normalizing the alternatives value based on C 3 .
C C
3 3
A1 30 A1 0.287
A2 10 = A2 0.299
A3 20 A3 0.414
Last, we compute the final alternatives ranking priority by combining all the
alternatives ranking priority into a matrix and then multiply it by the criteria
ranking.
C1 C2 C3 Criteria’s Priority Value Alternatives’ Priority Value
A1 0.250 0.131 0.500 C1 0.131 A1 0.376
A2 0.352 0.247 0.167 ∗ C2 0.247 = A2 0.211
A3 0.398 0.622 0.333 C3 0.622 A3 0.413
Therefore, we conclude that alternative A3 has the greatest value as the most
important city to be expanded first.
3 Research Method
The methodology of this research is adopted based on the stages in the devel-
opment of geographic information system project ([1], [7]). It consists of five
stages. The first stage is named as the initiation stage. It aims to determine the
problems and case studies will be discussed on this research. The second stage,
which is the requirement analysis stage, purposes to identify which requirements
must be used to support this research and how to gather the data from the data
source. The next stage is how to design the geospatial dashboard, especially in
5
terms of its architecture. The fourth stage is to implement the geospatial dash-
board and AHP from the design sketch in previous stage that will be integrated
with the AHP algorithm. Finally, the last stage is how to evaluate the result
from the implementation. The explanation of this research method is depicted
in the following Figure 2. The analysis to model this case study into AHP tree
structure as well as the implementation of geospatial dashboard and AHP will
be explained more detail in the following subsections.
Fig. 2: Research Methodology
3.1 AHP Component Analysis
Here, we analyze the needs of some components of AHP model, which are, ob-
jective, criteria and alternatives. We take the case study of looking for the order
of priority of branch office locations that will be expanded as the objective of
AHP model. To analyze the criteria of decision making for the expansion of
branch office location, we adopt the structure of the criteria based on [4] about
the common criteria to determine the location that will be expanded by geospa-
tial analytic techniques. The criteria cover some factors which are target market,
competitors in the area around the site, operational considerations, and changes.
The determination of these criteria was also supported by the recommendation
from RUMA’s stakeholders.
First, the type of target market criteria which is owned by RUMA was the
data of amount of stalls and population density in a region. Next, the competitive
aspect that is used to learn the competitors in the area around the site was the
price of a competitor’s product. Then, many types of operational activity of
RUMA make us to focus on the number of criteria used, such that the top
operational consideration taken for this research is the regional minimum wage.
The criterion based on change factor is determined by some changes that will
occur in an area, both physical and non-physical, which can affect the increase
of the number of potential customers for the company. In terms of RUMA’s
case study, we use the population of its business centers of Bank XYZ who had
cooperation with RUMA in terms of financial transactions for micro-enterprises.
In addition to the above four types of criteria, we also take a more qualitative
criterion. That criterion is the potential PT. RUMA to recruit the number of
stalls as customers in a region.
Then, this research exploits the alternative component of AHP method by
taking some cities/counties that were the current branch office location up to
February 2014 which is the period when this research began first time. There
6
are 22 cities/counties taken into this research which were Bandung, Bogor,
Bekasi, Ciamis, Canjur, Cilegon, Cirebon, Garut, Indramayu, Jakarta, Kendal,
Klaten, Lebak, Majelengka, Semarang, Serang, Sukabumi, Sumedang, Tabanan,
Tangerang, Tasikmalaya, and Yogyakarta. Last, all these components of AHP
are structured into AHP tree (Figure 3).
Fig. 3: AHP Tree Structure
3.2 Implementation of Geospatial Dashboard and AHP
The name of this geospatial dashboard is “Sistem Ekspansi Lokasi Kantor Ca-
bang PT. RUMA” (System of Expansion of Branch Office Area at RUMA).
Geospatial Dashboard has some features that are useful to facilitate RUMA,
especially ZEM team to know the priority of branch office locations that are
first to be built using AHP approach. Some features of geospatial dashboard are
described as follows:
1. Selecting the decision making criteria
2. Selecting the city that will be expanded
3. Running the calculations of AHP
4. Showing the distribution of each locations in a form of map After the user
runs the AHP, the application will display a map with the position of the
selected location. Each location will have a different colour marker which
indicates the priority of that location to be expanded. There are three kinds
of priority order. The first priority which is indicated by red colour means a
high priority. The second priority is interpreted with a yellow one means a
medium priority, while the third priority indicates a low priority for a city
with a green marker colour. Users can view detailed information of a city /
county on the map by selecting the corresponding marker.
5. Showing the order of priority value of each branch office locations in the
form of table.
7
4 Evaluation on the AHP Method
The main purpose of this evaluation is to measure the accuracy of AHP method
used in geospatial dashboard. This evaluation compares the resulted priority
order of city from geospatial dashboard with the data of order of all branch
office location of RUMA based on the level of profit per transaction of product.
This data was produced in the February 2014. For the simplicity, we name this
ordered data as a targeted list for the further experiment. We take this data
with a reason that the resulted list from geospatial dashboard is the order of
city/county with the profit expectation from the top to the bottom based on
the criteria that we analyzed before. This comparison technique directly looks
into a correlation between two difference lists of order, such that this technique
require such a statistic method that is so-called Spearman Rho [14].
The next step is how to determine the chosen criteria and city during the
evaluation. We make two group of experiments that have different format of
chosen criteria and city. Every group had 3 experiments. The first group will use
the chosen criteria based on some recommendations from having focus group
discussion with ZEM of RUMA. The other group of experiments use criteria
which is obtained by conducting a little experiment aided by spearman rho.
In this evaluation, every criterion would be divided into two groups that are
basic criteria and non-basic criteria. Basic criteria are criteria that always become
top consideration for ZEM of RUMA once analyzing a location to be expanded,
while non-basic criteria are only supporting criteria. Therefore, every two groups
of experiment will have different basic and non-basic criteria. According to focus
group discussion with ZEM of RUMA, we have some basic criteria that are
density of population (DP), density of stalls (DS), and price of competitor’s
product (P), while the non-basic criteria are regional minimum wage (RW),
population of business center (PB), and potency of RUMA to recruit stalls (PR).
Moreover, for another group of experiment we have some following steps to
choose criteria based on the spearman rho method, which are:
– Run 6 little experiments that only use one criterion in each of them. Recall
that we only have 6 criteria in this research. For every experiment, choose
all 22 cities and run the geospatial dashboard.
– Once obtaining the result, compare it to the list of order of cites/counties
based on the level of profit per product transaction. In other words, we
compare the rank of each city in these two lists.
– Calculate this comparison by using spearman rho and in the end every little
experiment will have an accuracy value. This accuracy value directly become
a value for the used criterion in this little experiment.
– From these little experiments, we have the basic criteria which are price of
competitor’s product (P), regional minimum wage (RW), and population of
business centre (PB), while the non basic criteria are the rest.
For every group of experiments, we will do 8 sub-experiments in which the
number 8 is obtained as the combination of three basic criteria and all subsets
of non-basic criteria. We name the experiments in the first group as A, B, and
8
C, while in the second group we have D, E, and F. All sub-experiments will be
treated by running geospatial dashboard, choosing all 22 cities, and use one of
the chosen criteria.
However, there are different ways of comparing the resulted ordered list with
the targeted list. For experiments A and D, we compare the similarity of rank
of 22 cities between these lists. For experiment B and E we only choose first
8 cities from the resulted ordered list of geospatial dashboard to be compared
with their rank in the targeted list as a consideration that 8 cities are the first
priority of cities to be expanded in the middle term planning. For experiment C
and F, similar to experiment B and E, but we only chose first 4 cities because
among 8 cities as the first priority, there were only 4 cities to be observed first
in the short term planning of ZEM.
Table 1: Result of Evaluation of AHP Method in 1st Experiment Group
Table 1 and table 2 show the result of this evaluation with the information of
accuracy value with some chosen criteria. Based on table 1, we can conclude that
in 1st experiment group which used the chosen criteria from having focus group
discussion with ZEM, we can get at most 80.95% accuracy which only uses the
first 4 cities from the resulted list of geospatial dashboard for the comparison
and the criteria used are density of population, density of stalls, and price of
competitor’s product. On the other hand, table 2 shows that we can get at
most 90.48% accuracy which only uses the first 8 cities from the resulted list
of geospatial dashboard for the comparison and the criteria used are price of
competitor’s product, density of population, and density of stalls.
9
Table 2: Result of Evaluation of AHP Method in 2nd Experiment Group
5 Conclusion
Here we develop the geospatial dashboard based on analytic hierarchy process-
ing technique for the expansion of branch office location by using a case study
of Rekan Usaha Mikro Anda Company. First, we show how to analyze the com-
ponent of AHP, such as objective, criteria, and alternatives. Then, we construct
them into an AHP tree structure. Next, we implement the geospatial dashboard
by introducing its featurs. Using the evaluation in terms of the accuracy of AHP
method, we can get at most 80.95% of accuracy by using chosen criteria from
focus group discussion with ZEM of RUMA and at most 90.48% of accuracy by
using the criteria that are determined from spearman rho calculation.
Other research that can be developed from this research is the development
of data warehouse or online analytical processing (OLAP) for the utilization of
geospatial dashboard. With the similar situation in case study, this technology
can provide data which is multidimensional, historic, or even real-time, so the
decision maker viewpoint to a location data can be wide and varied. Some ma-
chine learning techniques to read and process the variety of data can be injected
further into this application for the improvement process of decision making.
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