# QuIS: The Question of Intelligent Site Selection

Sebastian Baumbach, Florian Sachs, Sheraz Ahmed, and Andreas Dengel  Sebastian
Baumbach, Sheraz Ahmed, and Andreas Dengel are with German Research Center for
Artificial Intelligence, Kaiserslautern, Germany. Sebastian Baumbach, and
Andreas Dengel are with University of Kaiserslautern, Germany. Florian Sachs
is with University of Magdeburg, Faculty of Economics and Management,
Magdeburg, Germany.
Email<EMAIL_ADDRESS>
###### Abstract

Site selection is one of the most important decisions to be made by companies.
Such a decision depends on various factors of sites, including socio-economic,
geographical, ecological, as well as specific requirements of companies. The
existing approaches for site selection are manual, subjective, and not
scalable. The paper presents the new approach QuIS for site selection, which
is automatic, scalable, and more effective than existing state-of-the-art
methods. It impartially finds suitables site based on analyzing decisive data
of all location factors in both time and space. Another highlight of the
proposed method is that the recommendations are supported by explanations,
i.e., why something was suggested. To assess the effectiveness of the
presented method, a case study on site selection of supermarkets in Germany is
performed using real data of more than $200$ location factors for $11.162$
sites. Evaluation results show that there is a big coverage ($86.4\%$) between
the sites of existing supermarkets selected by economists and the sites
recommended by the presented method. In addition, the method also recommends
many sites ($328$) where it is benefial to open a new supermarket.
Furthermore, new decisive location factors are revealed, which have an impact
on the existence of supermarkets.

###### Index Terms:

Site Selection, Socation Factors, Geospatial Data, Machine Learning, Learning,
Decision Support, Cartography

##  1 Introduction

Selecting a facility location is a constitutive investment decision of
critical importance for every company that wants to operate a successful
business [1, 2]. Enterprises face such a decision problem at least once in
their lifetime. Negative influences of a site’s location cannot be compensated
by other factors or actions [3, 4]. Numerous geographical, social, political,
economic, or socio-economic factors for each site are usually be taken into
account by economists and compared to the specific requirements of a company
[5].
In the area of site selection, research work traces back nearly a century [6,
7, 8]. However, due to globalization and digitization, geographical search
space as well as amount of data has enormously increased. This data covers not
only detailed maps like Google Maps or OpenStreetMap, but also data about
demographical and social attributes of inhabitants (for instance, offered by
the Federal Statistical Office of Germany as open data). In addition, there
exist many companies focusing on the creation of complex and commercial
datasets, ranging from regional purchasing power to consumer behavior of
citizens. This exponential growth of decisive data makes it increasingly
difficult for decision-makers and experts to analyze all relevant information
manually. This is why locations are selected top – down (i.e. from state to
region) in practice. As a consequence, some regions are excluded subjectively
by experts and the selection is commonly based on subjective criteria [7, 9,
10]. The remaining sites are weighted with respect to their descriptive
attributes and the ”best” site wins this election. However, this weighting and
selection are highly biased and often based on the individual experience and
assessment of the involved experts [11, 12, 13, 14]. As a result, the location
decision is not consistently predicated on all information.
The aim of this paper is to objectively find a suitable site based on
analyzing decisive data of all location factors. An attempt is made to build a
data driven quantitative model for site selection, where all locations as well
as the companies’ requirements are treated respectively. Thus, sites are
described by their attributes, e.g. purchasing power, number of inhabitants or
proximity to suppliers, which are called location factors in economics. This
approach makes it necessary to collect data of all sites together with all of
their location factors and integrate it into a huge dataset. Companies’
requirements are expressed by a combination of these location factors and
appropriate weights. Following this approach, a suitable site is a location
where its corresponding location factors fulfill the requirements of a
specific company.
This paper presents the novel approach QuIS for data-driven site selection,
which analyzes location factors and company requirements. The presented method
first builds a data model for which it captures all location factors by
incorporating geospatial-temporal data from different sources. This model is
structured in geographical, temporal, and hierarchical dimensions and combined
with an enhanced recommendation system, where user requirements (constraints
and preferences) are matched to the corresponding data by the use of means-end
relations. The proposed system is intuitive, flexible, easy to use, and only
requires knowledge about the company itself. The flexible and intuitive nature
of QuIS allows it to be used in many different industries with diverse
requirements. The existing approaches for site selection are based on complex
decision support systems that are mostly refused by the industry as these
models tend to be too complex, cumbersome and confusing [15]. To resolve this
issue, the presented method not only provides the recommendation but also
provides detailed explanation about how a decision was made. In particular,
this paper made following contributions:

  * •

A generic, hierarchical data model for geospatial-temporal data. With this
model, arbitrary homogeneous geospatial data as well as their changes over
time can be integrated and queried according to the three dimensions (space,
time and features). It can be used not only for site selection but for all
applications, which benefit from geospatial data like crisis prediction.

  * •

A large dataset consisting up of more than $200$ location factors for all
$11,260$ municipalities of Germany. The existing datasets used in literature
are quite small and contain only limited information i.e., either all required
location factors or all available municipalities of a nation, but not both.

  * •

An extended recommendation system capable of dealing with large geospatial-
temporal data for the purpose of site selection.

  * •

A case study about German’s supermarkets where the actual locations of $1,700$
supermarkets in Germany are compared with the recommendations of the site
selection system. The results showed that there is a big overlap ($86.4\%$)
between existing stores selected by the expert and sites recommended by the
proposed methods.

  * •

An explanation engine facilitating decision makers with explanation on
decision, i.e., why a specific site was selected.

  * •

New location factors, which have an impact on the suitability of sites for
supermarkets. Unknown patterns behind locations and its characteristics are
identified by analyzing sites and its location factors in-depth. These results
are used to overcome the cold start problem of the proposed site recommender
system.

Evaluation results of the case study shows that the each supermarket chain
follows its own strategy and prefers different sites in Germany for their
stores.

The rest of this paper is organized as follows. In Section 2, an introduction
to site selection and background information are provided. Section 3
summarizes and assesses the state-of-the-art for site analysis in economics,
maths and computer science as well as locational datasets. Section 4 presents
the proposed approach for data-driven site selection, whereas Section 5
depicts the corresponding hierarchical data model and the dataset used to
perform the evaluation. Section 6 presents the conducted case study about
supermarkets in order to demonstrate the viability of QuIS. Section 7
introduces the in-depth analysis for identifying hidden, influencing location
factors and Section 8 describes the explanation engine. Finally, the results
are discussed in Section 9 together with a summary and an outlook for future
work.

##  2 Site Selection: An Overview

The site or location of a company is the geographic place where a company does
business [9]. The location characteristics, which are relevant for the
operating performance, are called location factors [8]. While the economic
viewpoint examines the global distribution of companies from a broader
perspective, the intra-company viewpoint deals with the spatial arrangement of
resources within buildings, which is also known as layout planning. In this
paper, the focus is on the business perspective of site respective location
selection rather than on the economic or intra-company point of view.

Site selection is mainly done by analyzing and comparing the location factors
of different places. These factors are the properties that are influential
towards a company’s goal achievement [16]. The important location factors are
usually selected by a company based on their own demands and each company
might possess different requirement. Table I shows the most common location
factors grouped into 10 major categories [17].

TABLE I: Categorized Location Factors, a Selection

Category |  Location Factor
---|---
Transportation |  Highway facilities, railroad facilities, waterway transportation, airway facilities, trucking services, shipping cost of raw material, cost of finished goods transportation, shipping cost of raw material, warehousing and storage facilities.
Labor |  Low cost labor, attitude of workers, managerial labor, skilled labor and wage rates, unskilled labor, unions, and educational level of labor.
Raw Materials |  Proximity to supplies, availability of raw materials, nearness to component parts, availability of storage facilities for raw materials and components, and location of suppliers.
Markets |  Existing consumer market, existing producer market, potential consumer market, anticipation of growth of markets, marketing services, favorable competitive position, population trends, location of competitors, future expansion opportunities, and size of market.
Industrial Site |  Accessibility of land, cost of industrial land, developed industrial park, space for future expansion, availability of lending institution, closeness to other industries.
Utilities |  Attitude of utility agents, water supply, wastewater, cost and quality, disposable facilities of industrial waste, availability of fuels, cost of fuels, availability of electric power, and availability of gas.
Government Attitude |  Building ordinances, zoning codes, compensation laws, insurance laws, safety inspections and stream pollution laws.
Tax Structure |  Industrial property tax rates, state corporate tax structure, tax free operations, and state sales tax.
Climate & Ecology |  Amount snow fall, percent rain fall, living conditions, relative humidity, monthly average temperature, air pollution, and ecology.

Location factors can be further divided into hard and soft factors. The first
group is quantifiable and has direct impact on the economic viability of
locations as they directly influence the cost and revenue calculations. This
includes, among others, the number of skilled workers and their education, the
proximity to suppliers, and tax rates. The second group contains qualifiable
factors, which cannot be integrated into the calculations of the company due
to their descriptive and non-numerical nature. This comprises, for instance,
political conditions, prestige of sites, or leisure programs. However, the
later group is becoming more and more important for site analysis [5].
These criteria are highly dependent on the industry and the specific company.
For retail stores, the close proximity (purchasing power, public transport, or
prestige) is of crucial importance. In comparison, the availability of labor,
infrastructure, and tax considerations are of high interest for factories.
Sites for the production of hazardous substances require the lack of
residential and other industrial facilities in their close surrounding.

As a consequence, methods for site selection do not only have to take all
necessary location factors into account, but also model the specific
requirements of companies in their new sites. These requirements, however,
differ from company to company.

##  3 State-of-the-Art in Locational Datasets and Site Selection Methods

The research community mainly focuses on the collection of new locational
datasets (containing both a list of sites and location factors) as well as the
development of algorithms to link these datasets with companies’ requirements.
Within the last century, economists have published various methods for site
selection. In the last decade, approaches originally came from the area
computer science were often applied in site analysis. So far, recommender
systems, however, have not been used for site selection, although they were
developed to make suggestions to users. The rest of this section summarizes
and assesses the current state of research on locational datasets, site
analysis in science and industry, and recommender systems.

###  3.1 Locational Datasets

Researchers have studied many locational datasets over time. Their
investigations are analyzed regarding three criteria, which are I) the spatial
granularity (nations, countries or municipalities/cites as area of focus), II)
the number of sites, and III) the number of location factors. The ultimate
goal is to analyze with a high spatial granularity (municipalities/cites) all
existing sites within the area of focus by considering a large number of
locational factors. Table II shows the findings.

TABLE II: Taxonomy of Locational Datasets used in Site Selection Studies

Studies | Number of Location Factors | Number of Sites | Spatial Granularity
---|---|---|---
Assaf et al. [18] | $23$ | $123$ | Nations
Amini [19] | $11$ | $15$ | Districts
Arauzo-Carod [20] | $4$ | $720$ | Municipalities
Aydin et al. [21] | $18$ |  $5$ Provinces |  $250m\times 250m$ Cells
Carver [22] | $16$ | $5,878$ |  $2\times 2km$ Cells
Donevska et al. [23] | $12$ |  $1$ region | Area for landfills
Gorsevski et al. [24] | $7$ | $27$ | Countries
Mohajeri and Amin [25] | $45$ | $5$ | Cities
Önüt et al. [26] | $8$ | $6$ | City Districts
Rikalovic and Cosic [14] | $4$ | $45$ | Municipalities
Vahidnia [27] | $5$ | $5$ | Cities
Vasiljević et al. [28] | $14$ |  $1$ region | Area for Landfill

In conclusion, investigations of sites have only been made on the basis of
small datasets with both a small number of location factors and assessed
sites. None of the studies has used datasets, which fulfills likewise all
three requirements. Mohajeri and Amin [25] analyzed the most location factors
($45$) in order to find the best candidate for a railway station, but only
evaluated five sites. Arauzo-Carod [20] studied $720$ Catalan municipalities,
but took only $4$ location factors (population density, skill level of human
capital, and infrastructure) into account. Assaf et al. [18] evaluated
international hotels and the location factors that matter most. They collected
a dataset for $123$ sites, which contains $23$ location factors (e.g.
performance of the hotel industry, crime rates, or tax rates). However, they
only focused nations as spatial entity, instead of considering sites with an
higher spatial granularity, such as countries or municipalities.

###  3.2 Site Selection in Science and Industry

In literature, many approaches have been proposed how a site can be selected.
In fact, there is no fixed procedure. However, few guidelines and operating
procedures exist, with each having its own drawbacks and limitations. In
general, the existing methods for site selection used by economists are very
complex and time extensive (as they require a lot of manual configuration and
analysis), which limit their applicability in practice [10, 29, 30]. Most of
these methods do not scale with data or request certain constraints (i.e.
homogeneous distribution), which are normally not fulfilled in practice.

#### Hierarchical Analysis

In literature and practice, the site selection process is often divided into
multiple phases where the regional focus is reduced in each phase. Zelenovic
split it up into a macro and micro selection [10]. The first phase addresses
the issue of finding the right state while the second one looks for a specific
site within the previously chosen state. Bankhofer divided the whole process
into four phases of selecting continent, country, municipality and then final
location in that order [31]. However, this division ”top-down” is impartial
and inefficient as it requires manual analysis and selection. The exploration
and selection of all decisive location factors by hand is not feasible since
the amount of data is increasing exponentially.

#### Regression Analysis

Models like Discrete Choice Models [32] or Count Data Model [33] for weighting
and selection of location factors have become increasingly complex over the
years [30]. They include not only numerous, but also constantly changing
factors based on the varying contexts of each model. There is no similar
study, which reveals identical findings, but suggests different subsets of
location factors [5]. Even though these models are theoretically helpful, they
are too complex and therefore, time and cost intensive in a real scenario.
This leads managers to base their decisions not only on given facts, but
rather on their personal and emotional judgment [11, 12, 13, 14].

#### Methods Used by Economists

Woratschek and Pastowski studied different methods in economics used for
location selection, namely Checklist Methods, Selection by Elimination and
Scoring Models [29]. These ”conventional approaches” utilize managers’
judgment in terms of their knowledge and experiences. This is the reason why
these methods should only provide a set of systematic steps for problem
solving since the capabilities and experiences of the analyst significantly
influence the final results [34].

Checklist Methods are a very basic way to assist in the selection process.
Relevant location factors for a company get listed and weighted by experts for
different sites. These weights are the degree how good a location fulfills a
given requirement. Table III shows an example of a checklist for site
selection.

TABLE III: Checklist Method, Example.

Location Factors | Location 1 | Location 2 | Location 3
---|---|---|---
Availability of Resources | + | + | o
Income Structure | + | - | +
Consumer Structure | - | o | o
Infrastructure | + | + | +
Taxes | o | o | +

The advantage of the Checklist Method is its simplicity and the little time it
needs to be done. However, such a checklist does not provide any in-depths
analysis. In addition, the presented results are only of qualitative, but not
of quantitative nature.

Selection by Elimination is an extension to Checklist Method, which
immediately removes a location not fulfilling a requirement. At the end of
this process, after gradually eliminating all non-sufficient alternatives,
only sites that satisfy all given requirements are left for further
selections. However, this method has some drawbacks. Since all of the criteria
can eliminate a site, it is implicitly assumed that criteria cannot compensate
each other. This might lead to the exclusion of sites that are just below an
expected quality of one requirement, but are excellent in all other aspects.

Scoring Models are another extension of the Checklist Method approach, where
location factors are weighted and sites are rated by a given user-defined
scale (e.g. from 1 to 5). Those ratings are multiplied by the respective
weight given to location factors and summed up in the end. This leads to an
actual numerical result and a comparable ranking of locations based on pre-
defined factors and their importance. Table IV gives an example.

TABLE IV: Scoring Models, Example.

| Location A | Location B
---|---|---
Criteria | Weighting | Points | Total Points | Points | Total Points
Consumer | 60 % |  |  |  |
Avg. Income | 15 % | 4 | 0,6 | 5 | 0,75
No. of inhabitants zone 1 | 25 % | 2 | 0,5 | 3 | 0,75
No. of inhabitants zone 2 | 20 % | 2 | 0,4 | 4 | 0,8
Competitors | 30 % |  |  |  |
No. of competitors | 10 % | 1 | 0,1 | 1 | 0,1
Market share | 20 % | 5 | 1 | 1 | 0,2
Infrastructure | 10 % |  |  |  |
Customers | 5 % | 2 | 0,1 | 2 | 0,1
Transportation | 5 % | 3 | 0,15 | 1 | 0,05
Result |  $\Sigma$ 100 % |  | 2,85 |  | 2,75

#### Multiple-Criteria Decision Analysis

Site selecting can also be modeled as Multiple-Criteria Decision Analysis
(MCDA) problem in which a new facility is located among several alternatives
by minimizing or maximizing at least one objective function (e.g. costs,
profit, revenue, or distance to suppliers). Farahani et al. have published a
detailed survey of these multiple criteria facility location problems [35].

Multiple MCDA techniques have been used for site selecting in literature, such
as TOPSIS [36], ELECTRE [37], and PROMETHEE [38]. Many studies have been
conducted in order to rank alternative sites, especially in the field of
landfill site selection [23, 28], environmental problems [39, 40, 41], or
renewable energy systems [42, 24, 21, 43].
Saaty [44] introduced the Analytical Hierarchy Process (AHP), which is one of
the most important method in selecting optimized alternatives [27]. The
limitation of AHP is that it does not allow the items (here, location factors)
of the hierarchical model to have dependences on each other. To overcome these
drawbacks, the Analytic Network Process (ANP) [45] was proposed modeling ”the
decision process as a sequence of uni-directional, hierarchical relationships
rather than complex networks of objectives” [46]. ANP allows independence
interaction and feedback between items.

MCDA methods for site selecting focus merely on the analysis of location
criteria and their ranking by experts, rather than on the investigation of
criteria and their actual impact on the business performance of sites in
practice. A detailed analysis of advantages and disadvantages of these MCDA
methods has been published by Velasquez and Hester [47].

###  3.3 Recommender Systems as Site Selection

The existing recommender systems commonly used for suggesting movies [48],
books [49], or music [50] cannot be used for site selection. This is because
these systems cannot be configured to incorporate an application context with
a complexity similar to location analysis. Recommender systems mainly operate
in the two-dimensional $User\times Item$ space. Traditionally, items are
defined by their attributes, which contain keywords or numeric values. Users
are described by their preferences basically expressing their interest in
certain items. A detailed introduction to recommender systems can be found in
[51]. However, this kind of model is not suitable for recommending sites for
three reasons.

For suggesting suitable sites to companies, different forms of knowledge have
to be employed, which is more than just interest on specific items. These
knowledge dimensions cover preferences, constraints, context, and domain
knowledge about the users’ needs, about other users and the locations
themselves as well as past recommendations [52]. As a consequence, companies’
requirements and sites cannot be modeled with the traditional concepts of
users respective items as they cannot completely hold the aforementioned
knowledge dimensions.

Furthermore, this focus of recommender systems to make suggestions based upon
information about users and items lead to another disadvantage. These systems
do not incorporate additional contextual information beyond the two dimensions
$User\times Item$, which is crucial in this scenario. However, an extended
recommender system with the capability of multidimensionality can utilize
supplemental dimensions to enhance the suggestions. Evident examples are
aggregated users’ demands extracted from social media, information about the
hierarchical structure of locations (”Munich is a city in Bavaria, which is a
state in Germany”), or temporal data as the location factors are naturally
changing over time [53].

Finally, most recommender systems deal with single-criterion ratings, such as
ratings of movies. In site selection, however, it is essential to integrate
multi-criteria ratings, e.g., a company can have different constraints, which
must be fulfilled, as well as multiple preferences, which should be satisfied
[51]. These kind of constraints cannot be employed in recommender systems as
the utilized profiles just model the users’ interest in items. They do not
contain any information about the degree of fulfillment, whether a criteria is
mandatory, preferable, or optimal to fulfill.

##  4 QuIS: Data-driven Site Selection

The proposed approach for site selection is based on a novel hierarchical data
model and an enhanced version of recommender system. QuIS translates
companies’ requirements for site selection into their location factors and
simultaneously, utilizes the available knowledge concerning locations and
companies’ requirements along with the data model. It turns the _manual
exploration of possible sites by decision-makers and experts_ into a
_definition of requirements by decision-makers & getting recommendations by
the expert system_. Figure 1 illustrates this comparison between traditional
and data-driven site selection.

Figure 1: Site Selection: Existing vs. proposed Site Selection.

Site selection is a task that is executed with low frequency, which means that
there is no chance to develop a user profile based on past transactions.
Additionally, companies do not usually provide feedback regarding the
profitability of their locations in detail. Hence, the common methods used in
content based and collaborative approaches cannot be applied here, as they are
highly dependent on either user profiles or rated items respectively.
A strategy to deal with such cold start problems is user questioners to
manually create an initial profile. QuIS however, employs a semi-automatic
approach to create these profiles (hereafter called User Requirement
Profiles). They are built by the help of the users respective companies.
Section 7 introduces an semi-automatic approach for creating these user
profiles.
Once the user profiles are defined, the recommendation system utilizes these
profiles to identify the suitable sites. The actual recommendation task is
defined as a constraint satisfaction problem. Figure 2 provides an overview of
the presented method where the recommender system uses User Requirement
Profiles to separate suitable sites from non-suitable sites.

Figure 2: Illustration of the Recommender System Idea

###  4.1 User Requirement Profile

The most important problem in site selection is that decisions should be made
based on qualitative measurement and evaluation. However, data available is
mostly quantitative. Inspired by the recommendation methods used by economists
(see [29]), all requirements are concretized to criteria and linked to
quantitative data, given by location factors, in the first step. Afterwards,
these criteria are weighted based on their relevance for the company before
they are used for ranking sites. Figure 3 illustrates this concept behind the
use of User Requirements Profiles.

Figure 3: Conceptual View on the proposed User Requirements Profile.

The User Requirements Profile is a composition of Decision Criteria,
Qualitative Ratings, and Means-End Assignments. Figure 4 shows the workflow
for creating User Requirements Profile. These profiles incorporate ideas and
concepts from Felfernig and Burke in which constraints, preferences and means-
ends are applied in a recommender system [52]. Decision Criterion represents a
company’s requirements for a new location (green). This requirement
description is then linked to the actual location factors using Means-End
Assignment (orange). Qualitative Rating is the evaluation of a criterion based
on the quantitative data given by the location factor (red) and thus, used to
make qualitative suggestions.

Figure 4: Abstraction of the URP Model.

#### Decision Criteria

Decision Criteria are the top-level components of User Requirements Model,
representing abstract requirements, such as good infrastructure or weak
competitors. A set of multiple criteria together makes up a single user
profile. The importance of each criterion is reflected by an associated
weight. Decision Criteria also utilize the concept of ’Scoring and Weighting’
of location factors originally introduced by scoring models in economics. (see
[29]). It simply combines the idea of ’Criteria’ and ’Weighting’ (see Table
IV).

Furthermore, the concept of ’Selection by Elimination’ is used where all
criteria are ranked based on their importance and locations get eliminated if
they do not fulfill all of the requirements. In contrast, in the proposed
system each criterion can be marked independently from each other [52].
Decision Criteria can either be classified as ’Must-Have’ (representing an
elimination condition if this criterion is not fulfilled) or as constraint in
form of ’Preferences’ or respectively ’Nice-To-Have’. This adjustment helps to
accurately represent user requirements.

Using the concepts of ’Scoring and Weighting’ and ’Selection by Elimination’
has the advantage of being already approved in practice as it takes two
established commonly used methods and combining their individual strengths
while leaving out their disadvantages. Furthermore, it also enables an easy
transition from the existing manual site selection to the proposed approach.
Since requirements for site selection are already known by experts, these
requirements can easily be rebuilt within the proposed site evaluation model
without any need to restructure or remodeling them.

#### Means-End Relation of Requirements and Location Factors

Each Decision Criterion are evaluated based on the given location factors.
However, companies’ needs cannot always be directly translated to location
factors. In most cases, requirements represented by Decision Criteria cannot
be derived from a single factor. To resolve this problem, the presented
approach uses fuzzification by the use of Means-End Assignments [54]. A
location factor might satisfy different quality needs and in return a
qualitative need encompasses multiple attributes. The proposed approach
utilizes means-end relations incorporating domain knowledge, which allows
reasoning about ”how particular items (the means) satisfy particular needs or
requirements of the user (the ends)” [52].

The purpose of Means-End Assignments is to associate a Decision Criterion with
$n$ location factors. As a consequence, companies’ requirements are translated
to multiple location factors.

User Requirements Profile uses weights to describe the importance of each
Decision Criterion and thus, specifying the association between the profile
and all of its criteria. In case of the Decision Criteria and Means-End
Assignments, however, the means-end can be expressed in any mathematical
formula to build up Decision Criterion. In the following example, four
associated means-end, representing four location factors at the end, are
combined in the following way:

$\frac{(ME_{1}~{}+~{}ME_{2})^{ME_{3}}}{2*ME_{4}}$ (1)

This way, the utilized Means-End Assignments allow flexibility and accuracy
when it comes to constructing decision criteria.

###  4.2 Qualitative Rating: Evaluating Decision Criteria

The Decision Criterion is used to compare and rank sites. However, existing
algorithms used in recommender systems are commonly used to find items that
are similar to ones already rated positively [51]. This is not the case for
site recommendation due to the given application context where no user rating
is available in advance. Therefore, pre-defined ideal values for Decision
Criteria are used by the recommendation engine for comparison. Thus, a
Decision Criterion is the expression of a company’s requirement towards a
site’s attributes in relation to a given ideal value. This way, a degree of
fulfillment can be calculated how a good a particular site fulfills the
company’s requirement. Additionally, such a requirement can be optimally
marked as ’mandatory’.
The calculation of this ideal value is done by using a bounding box, which can
be defined by the user. It expresses a range by specifying both lower and
upper bounds for this Decision Criterion. The degree of fulfillment is
calculated in relation to these bounds. For example, a certain criterion is
defined as the number of inhabitants and the corresponding bounds are set to
$bound_{low}=2.000$ and $bound_{up}=10.000$. Considering a site with a
population of $8.500$ $value_{site}$, which lies within these bounds, the
degree of fulfillment can be declared as:

$\frac{value_{site}-bound_{low}}{bound_{up}-bound_{low}}$

For the above example, the formula results in a score of 0.8. However, this is
actually just one possible method for the calculation. To make the system more
adaptive to the companies’ requirements, following other evaluation strategy
were also integrated into the system:

  * •

a boolean strategy that evaluates whether a value is within the bounds set,

  * •

strategies that use exponential or logarithmic formulas to calculate scores,

  * •

different handling of values that are outside of the bounds set.

Thresholds are used to give users respective companies the possibility to
specify the degree of fulfillment, which is considered by them to be
sufficient. Means-End Assignments expressed in arbitrary mathematical formula
provides the required flexibility to meet the user’s needs as the exact way of
comparing and calculating a score heavily depend on the specific requirement.
For instance, a retail store is aiming at the maximization of the average
purchasing power whereas the crime rates should score better the lower the
actual values are.
To sum up, the proposed system offers the following features regarding
Decision Criteria:

  * •

Defining compare values in a meaningful way.

  * •

Marking them as ’mandatory’ with a necessary degree of fulfillment.

  * •

Combine multiple location factors in a flexible and mathematical way.

  * •

Enable varying means of evaluation.

###  4.3 Calculating Recommendations

Ultimately, the recommendation task is defined as a constraint satisfaction
problem, which has been adapted to site selection. The solution is a list of
sites, such that this list is consistent with the requirements of the company
as well as the location factors of sites.

Given a set of the company’s requirements, the recommendation can be
calculated by using a constraint-based recommender $R_{constr}$, which
computes solutions {$site_{i}\in AllSites$} for a given site recommendation
task.

######  Definition 1.

Based on [52], a site recommendation task is defined as Constraint
Satisfaction Problem $(C,S,CR\cup COMP\cup FILT\cup SITES)$ where $C$ is a
finite set of variables representing potential requirements of the company and
$S$ is a set of variables defining the basic properties of the sites.
Furthermore, $CR$ is a set of the company’s Decision Criteria, $COMP$
represents a set of (incompatibility) constraints, $FILT$ is a set of filter
constraints, and $SITES$ specifies the set of offered sites.

A solution to a given site recommendation task $(C,S,CR\cup COMP\cup FILT\cup
SITES)$ is a complete assignment to the variables of $(C,S)$ such that this
assignment is consistent with the constraints in $(CR\cup COMP\cup FILT\cup
SITES)$. A weighting can be defined on those relations to fine tune the
relevance of every location factor according the companies’ Decision Criteria.

##  5 Data Model and Dataset

In order to rank sites according the companies’ requirements, QuIS needs to be
connected with a data model capturing sites as well as location factors. The
data model incorporates geospatial-temporal data from various sources and
structures it in a geographical, temporal, and hierarchical way.

###  5.1 Hierarchical Data Model

The data model organizes all available sites in a hierarchy according to a
nation’s federalization.

Figure 5: Administration Hierarchy Germany, By David Liuzzo CC BY-SA 2.0.

Figure 5 shows the structure of the data model: each state, county, district,
and municipality is represented as site. Besides these sites, the data model
contains time series, which are the technical representation of location
factors. A single time series describes one factor and contains multiple time
points as shown in the simplified example:

######  Exampe Time Series.

name: Population; unit: Count;
data:[2000, 2001, 2002 … 2013, 2014, 2015];
siteID: 120645410340; values:[4000, 4500, 4300, … 6000, 6250, 6300]

Figure 6 shows the time series population for the county Märkisch-Oderland.

Figure 6: Population for the County Märkisch-Oderland.

Sites in Germany can be identified by using the unique region key officially
introduced and employed by the German Government111See Wikipedia for more
Information. The region key is an twelve-digit key that not only uniquely
identifies a site in Germany, but also describes the relationships between the
four levels (state, county, district, and municipality). The first two digits
of the code indicates the state (e.g 12 for Brandenburg). The third digit
designates the government district, which is deprecated meanwhile, but still
exists in the regional key for consistency (e.g. it is 0 in the most cases).
The fourth and fifth digit indicates the county (e.g 12 0 64 for Märkisch-
Oderland). The sixth, seventh, eighth, and ninth digit represent the district
(e.g. 12 0 64 5410 for Amt Neuhardenberg). The last three digits describe the
municipality (e.g 12 0 64 5410 340 for Neuhardenberg).

$RegionKey=\underbrace{\overbrace{\underbrace{\overbrace{12}^{State}0\
64}_{County}5410}^{District}340}_{Municipality}$

For a location factor (for instance, population or purchasing power) to be
stored in the data model, the corresponding time series are stored as a geo-
referenced time series in the data model.

Due to the hierarchical structure of the data model, it is possible to
aggregate values along the hierarchy in both directions: upwards and
downwards. The population of a state for example is the sum of the inhabitants
of all regions residual in that state (upwards). Information, which describes
large geographic regions in general (e.g. languages or dialects), are linked
to the highest level (e.g. states and counties) and get inherited top-down
along the hierarchy (downwards). The inheritance of location factors allows
storing information only on the lowest level of the hierarchy and by this,
reduces data redundancy.

The separation of sites, location factors, and its values give the necessary
flexibility to update, chance, or adopt the data model. New location factors
or geospatial information can be added easily. Such use case might be, for
example, radio network coverage or resource availability. Changes in or
extensions of the hierarchy are also possible to realize.

###  5.2 Dataset

QuIS is evaluated on real data using location factors collected from the
German Federal Statistical Office. This data is available for all
municipalities and cities of Germany. Figure 5 gives an overview of the
administration hierarchy in Germany. This hierarchy contains Germany with its
$16$ states, $402$ counties, $4,520$ districts, and $11,162$ municipalities as
well as information about the hierarchy. As for now, municipalities are the
lowest level of territorial division. This information is provided by the
Federal Statistical Office of Germany and can be used without any restrictions
and free of charge.

Data for over $200$ location factors have been integrated into the data model,
which is also provided by the Federal Statistical Office. Available location
factors cover topics, such as net income, purchasing power, various
information about inhabitants & population, employees & unemployed persons,
education, land costs, households in number & size, and companies itemized in
size, number, profit, industry, & employed people. Table V provides an example
for Berlin with a selection of most decisive location factors222The location
factors have been chosen based on the results of Section 7.1.. Ultimately,
there are $200$ location factors in total containing data for the last $15$ to
$20$ years (depending on the specific location factor) and for all sites (the
sum of all states, counties, districts, and municipalities).

TABLE V: Selected Location Factors for Berlin.

Factor | Value
---|---
Avg. GDP per Employee | € 71,209
Available income per Inhabitant | € 22,586
Inhabitant | 3,484,995
Employment Rate | 78.9 %

Most of the location factors are available for districts and some have been
created on the granularity level of municipalities. Each location factor (e.g.
net income) exits for all sites within the specific hierarchy level it has
been created for (e.g. districts). Location factors are inherent up- and
downwards: the inhabitants of a district (blue) are the sum of inhabitants of
all municipalities whereas the average net income (red) for a district is also
assigned to all of its municipalities and cities. In case of the location
factor net income, there are $402$ time series in the data model, one for each
of the $402$ counties. Figure 7 illustrates these examples.

As stated in Section 5.1, each location factor do not only cover values for
the current moment, but also for the history. Thus, each location factor
technically represents a time series and stores data for the past $15$ to $20$
years. For this study, the latest values for the current year 2016 have been
used.

Figure 7: Hierarchical Data Model with Location Factors.

##  6 Evaluation: A case study of Supermarkets

To evaluate the effectiveness of the presented approach, a case study on
supermarkets is conducted. This case study first focus on site selection for
well known supermarkets in Germany and then compare these recommended sites
with the sites selected by human experts. The relevant location factors have
been provided by experts [13]. However, QuIS is generic and can be applied to
datasets collected from any country as well as to a wide range of problems,
e.g., gas stations, (fast food) restaurants, cinemas, gold or oil mines.
Besides evaluating the performance of the proposed recommender system, the
collected dataset (see Section 5.2) was analyzed in order to find
differentiators for sites with a supermarket and sites where a hypermarket is
located. What are the strategies behind companies’ decision when they open a
new supermarket?
For effective comparison, the study contains the following supermarket chains
Marktkauf, E-Center, Edeka Supermarket, E-Reichelt, E-Aktiv discounter, NP
Niedrig Preis and Lidl. The regional focus of this study is defined by the
sphere of influence of NP. This compasses Lower Saxony, Bremen, Saxony-Anhalt,
Berlin, Brandenburg, and East-Westphalia, a region of Northrhine-Westphalia.
In total, this area contains $1,704$ municipalities respective cities. Table
VI shows their distribution across the different states. All $200$ location
factors for these sites were taken into account, which includes, among others,
purchasing power, population, purchasing power in total, per person and as
index, and the average available income per household. Table VII shows the
number of stores within the area of study (according to their annual report
[55]).

TABLE VI: Municipality Distribution

Brandenburg | East-Westphalia | Lower Saxony | Saxony-Anhalt | Bremen | Berlin
---|---|---|---|---|---
417 | 70 | 996 | 218 | 2 | 1

Also has to be noted that these stores belong to different types of retailing
categories. These categories are explained below as well as an attribution of
the chosen retailers.

  * •

Supermarkets historically are the ’default store’. They sell groceries and
other goods of daily needs. Their assortment is of medium size and includes
brand-name products as well as home brands. In general, the sales floor of a
supermarket is around $1.000m^{2}$ \- $2.000m^{2}$. From the retailers listed
above Edeka is categorized as supermarket.

  * •

Discounters, in comparison to supermarkets, are more dense and focus on lower
prices. In general their assortment is simpler and smaller. They focus on fast
selling goods with a large percentage of home brands. Because of this, their
sales floor is smaller. Lidl and NP are commonly considered as discounter.

  * •

Hypermarkets (SB-Warenhaus in German) are characterized by their large sales
floor of at least $5.000m^{2}$ and a huge assortment of all different kinds of
goods. They also include counter for meat, cheese and fish. What separates
them the most are their department for non-food goods, such as electronics,
clothing or toys for example. E-Center are categorized as such a hypermarket.

TABLE VII: Crawled Retailer Distribution

Supermarket | Lidl | E-Center | Edeka | Niedrig Preis
---|---|---|---|---
Stores | 556 | 90 | 589 | 321
Locations | 454 | 90 | 480 | 256

###  6.1 Site Selection by Experts

Greiner, in charge of location selection in the _REWE_ Group analyzed in depth
how this decision is generally performed and which location factors are of
interest for _REWE_ supermarkets [13]. Figure 8 shows the site selection
process, which can be summarized in two phases.

Figure 8: Retailing Site Selection Process

The macro or pre-selection phase is mostly based on quantifiable numbers.
_REWE_ mainly uses the location factors number of inhabitants in this phase
for selection municipalities in Germany. As for Edeka and Lidl, they both list
core & urban population as well as sales floor & plot area as initial
requirements for their retailers (see Table VIII). Beside this factor, the
availability of parking places as well as real estate of certain size and
their rents are other requirements, which is, however, beyond the scope of
this paper (see Section 2). Any potential site that does not pass those
criteria gets sorted out.
In phase two, the remaining sites are analyzed in-depth. Experts directly
visit and assess the suitability of the pre-selected places. This phase
includes on-site assessments, analyzing the market structure and competitors,
defining the catchment area of a potential store as well as estimating sales.
This selection is done by checking quantifiable criteria. The resulting sites
are finally checked for their profitability; if the conclusion is positive,
the site will most likely be bought.
According to their expansion strategies, _Edeka_ , _Lidl_ , and _NP_
supermarket chains assert to apply the same criteria333Source: www.lidl.de and
www.edeka-verbund.de..

TABLE VIII: Location Requirements for Supermarkets

| Edeka | E-Center | Niedrig Preis (NP) | Lidl
---|---|---|---|---
Core Population | 5.000 | 10.000 | 2.500 | 5.000
Area Population | 8.000 | 25.000 | 5.000 | 10.000
Plot Area ($m^{2}$) | 6.000 - 12.000 | 15.000 - 25.000 | 3.000 - 6.000 | 4.000 +
Sales Area ($m^{2}$) | 850 - 2.300 | 2.300 - 4.500 | 600 - 850 | 800 - 1.400

For the evaluation in this paper, the number of core and area inhabitants are
used as requirements. Plot and sales area are site object specific parameter
and thus, actually not a part of the pre-selecting phase.

###  6.2 Site Selection by the Proposed System

$86.4\%$ of all locations can be explained by only taking these location
factors into account (see Table IX). The requirements for suitable locations
for supermarkets as described in the economic literature have been used to
configure the recommender system and its User Requirement Profile. This
profile contains the number of inhabitants and the regional focus. _NP_ is
looking for sites where more than $2,500$ inhabitants live, whereas _Edeka_
and _Lidl_ search for locations where at least $5,000$ inhabitants are living.
_E-Center_ , which are stores times larger than the ones mentioned beforehand,
are seeking for places with more than $10,000$ inhabitants. Finally, the
regional focus has been set to the states Saxony-Anhalt, Brandenburg, and
Lower Saxony as the chain _NP_ operates in these states only. Within this
regional focus, there exist $1,704$ municipalities respectively cities with
currently $481$ _Edeka_ , $90$ _E-Center_ , $256$ _NP_ , and $453$ _Lidl_
stores (sum $1,280$).

TABLE IX: Site Selection : Expert compared to the proposed System.

| Edeka | E-Center | Lidl | NP | Overall
---|---|---|---|---|---
No. of existing Supermarkets | 481 | 90 | 453 | 256 | 1280
Recommended Sites | No. | 364 | 80 | 428 | 224 | 1096
Percentage | 74.8 % | 88.8 % | 94.5 % | 87.5 % | 86.4 %

The lower accuracy of $74.8\%$ for Edeka can be explained by the fact that
Edeka works as a franchise with independent market owners. These stores
probably do not take advantage of the full optimization potential, as it is
the case for the centrally controlled company _Lidl_.

Additionally, $328$ suitable sites were found where currently no supermarket
of competitors is located and which can be recommended. Table X contains these
recommendations.

TABLE X: Recommended Sites for new Supermarket Stores.

| Edeka | E-Center | Lidl | NP
---|---|---|---|---
Recommended Sites | Total | 270 | 303 | 412 | 559
Without Markets | 88 | 49 | 51 | 140

Table XI shows the distribution of supermarket stores. $94.5\%$ of the _Lidl_
, $74.8\%$ of _Edeka_ , $87.5\%$ of _NP_ and 88.8 % of _E-Center_ stores are
located at municipalities, which fulfill the location factor number of
inhabitants. Only in $25$ ($5.5\%$) cases, _Lidl_ opened a store at a site
with less than $5,000$ inhabitants. $23.3\%$ and $11.1\%$ of _Edeka_
respectively _E-Center_ stores can be found at sites, which has less
inhabitants than actually required. _NP_ has selected in $32$ cases ($12.5\%$)
a municipality with less than $2,500$ inhabitants.

TABLE XI: Relation of the Supermarket Chains to the No. of Inhabitant.

Store | Criteria Inhabitants Fulfilled | Overall
---|---|---
yes |  no
Lidl | yes |  428 |  25 | 453
no |  412 |  839 | 1251
Overall |  840 |  864 | 1,704
Edeka | yes |  364 |  117 | 481
no |  270 |  953 | 1223
Overall |  634 |  1,070 | 1,704
E-Center | yes |  80 |  10 | 90
no |  303 |  1311 | 1614
Overall |  383 |  1,321 | 1,704
NP | yes |  224 |  32 | 256
no |  599 |  849 | 1448
Overall |  783 |  881 | 1,704

Additionally, the quality of these results was measured by calculating Recall,
Precision, and Fβ-Measure. Table XII gives the result for these measures. For
the $F_{\beta}$-Measure, $\beta=2$ was used, which favors recall over
precision. The reason is that the conducted study focused on the distribution
of supermarkets between sites in Germany rather then the existence of a
supermarket in a particular site.

TABLE XII: Relation of the Supermarket Chains to the No. of Inhabitant.

|  | Edeka | E-Center | Lidl | NP | Total
---|---|---|---|---|---|---
| Number of
---
existing Supermarkets
481 | 90 | 453 | 256 | 1280
Recommended Sites | Number | 364 | 80 | 428 | 224 | 1,096
Recall | 75,7% | 88,9% | 94.5% | 87,5% | 85.6%
Qualified Sites | Number | 634 | 383 | 634 | 823 | 2,474
Precision | 57,4% | 20,9% | 27.2% | 67,5% | 44.3%
F2-Measure | 71,1% | 53.8% | 60.6% | 87,5% | 72.1%

The recall of the proposed system is high with $85.6\%$ across all
supermarkets. Thus, the requirement of number of inhabitants can be declared
as being precise. Especially Lidl has only $25$ locations ($5.5\%$) that do
not fulfill their own requirements of at least $5,000$ inhabitants. Compared
to recall the precision of the recommendation is lower with an overall
$44.3\%$. This, however, is accounted for by the economical reasonability:
Just because a site is technically suitable for a store, it does not
necessarily mean it is also wise to open it there. The potential growth of a
company over time is limited and new stores contribute less in the beginning.
Area effects, supply chains and competitors also have to be considered, which
is out of the scope of this paper.

##  7 Identification of hidden influencing Factors

Since the location factors, as described by experts and supermarket companies
themselves, are precise to a high degree, the follow-up question is: Are there
any more decisive location factors, irrespective of whether or not they are
consciously used by the companies?
For the evaluation, location factors for supermarkets were extracted from
sites where supermarket stores are located. The results show that there is not
only an additional dependency on the purchasing power, but also different
strategies of the supermarket chains where to place their stores.
As a consequence, these decisive location factors, derived automatically from
the data, can be used then to initialize the User Requirements Profile
accordingly. Thus, users respective companies do not have to particularize all
of their requirements from scratch, but only have to fine tune existing
profiles regarding their specific needs.

###  7.1 Correlation Analysis of Location Factors

By means of a correlation analysis444The correlation is a number between $0$
(no correlation) and $+1$ (high correlation) that measures the degree of
association between the number of stores per supermarket chain and the
corresponding location factors, all $200$ location factors have been analyzed
regarding their impact on the availability of supermarkets. Table XIII
presents the location factors with the highest correlation scores. It can be
seen that, beside the dependency on the number of inhabitants, the purchasing
power has an impact on the existence of supermarkets. However, this dependency
is only high for _Edeka_ and _Lidl_ , which are, additionally, correlating
among each other. Furthermore, it can be observed that _NP_ neither attach
importance to the inhabitants nor the purchasing power in the way _Edeka_ and
_Lidl_ set value on it.

TABLE XIII: Correlation Matrix for Location Factors. $0$ (No Correlation) and
$+1$ (High Correlation)

Attribute | Num. of Edeka | Num. of E-Center | Num. of Lidl | Num. of NP
---|---|---|---|---
Num. of Edeka Stores | 1.0 | 0.61 | 0.94 | 0.48
Purchasing Power | 0.95 | 0.61 | 0.99 | 0.47
Num. of Inhabitants | 0.94 | 0.60 | 0.99 | 0.46
Num. of Households | 0.94 | 0.60 | 0.99 | 0.46
Num. of Lidl stores | 0.94 | 0.59 | 1.0 | 0.45
Num. of E-Center | 0.61 | 1.0 | 0.59 | 0.47
Population Density | 0.51 | 0.32 | 0.61 | 0.32
Num. of NP Stores | 0.48 | 0.47 | 0.45 | 1.0
GDP per Inhabitant | 0.23 | 0.11 | 0.24 | 0.09

Figure 9 gives an example case in detail where the county _Gütersloh_ is
visualized together with the number of inhabitants per municipality respective
city and their purchasing power index. The district _Herzebrock-Clarholz_ has
$15,969$ inhabitants and a purchasing power index of 100. This location is
among the recommended sites for a new supermarket as it fulfills the
requirements. Additionally, there are no supermarkets in the close surrounding
available.

Figure 9: Distribution of Supermarket Locations within the District
_Gütersloh_. Municipalities & Cities are colored by Purchasing Power Index.
Height of Bars visualizes the Numb. of Inhabitants.

###  7.2 Revealing the Expansion Strategies of Companies

Finally, the question is: Why have the supermarket chains chosen these sites
among the places, which also suit their requirements? In order to investigate
this question, the decisive location factors as calculated by the correlation
analysis were used to examine the companies’ strategies in detail. For this
purpose all locations were grouped into the following ranges based on their
population count555Note that the sample size for cities with more than
$100.000$ inhabitants was too small and thus, they were excluded.:

  1. 1.

5.000 and below

  2. 2.

from 5.001 to 10.000

  3. 3.

from 10.001 to 25.000

  4. 4.

from 25.001 to 50.000

  5. 5.

from 50.001 to 100.000

Figure 10 presents the average purchasing power666The purchasing power index
describes the purchasing power of a certain region per inhabitant in
comparison to the national average, which itself gets the standard value of
100. If the purchasing power index is $0.84$ for a municipality, these people
only possess $84\%$ of the national average purchasing power.. This
distribution indicates that _NP_ mainly focuses on smaller municipalities with
lower purchasing power, whereas _Lidl_ is concentrated in bigger cities. The
strategy of _Edeka_ is most likely to be available in all municipalities
respective cites with a medium purchasing power as they are mostly avoided by
their competitors. The huge _E-Center_ stores are mainly located in cities
above $10,000$ inhabitants.

Figure 10: Distribution of Supermarket Chains according to the Average
Purchasing Power Index and Grouped by Population.

Figure 11 shows the average purchasing power index and the average
unemployment rate for all municipalities where at least one supermarket of the
given chain is located. Thus, the average is presented for _NP_ (light blue),
_Lidl_ (green), _Edeka_ (yellow), and _E-Center_ (red). As regards the
comparison of all possible sites, the distribution for all locations with
(purple) and without (pink) supermarkets (regardless of the chain) as well as
the all locations (blue) are also presented.
It can be observed that _Lidl_ , _Edeka_ , and _E-Center_ are focusing
locations with higher than average purchasing power and lower than average
unemployment rate. In contrast, _NP_ opens stores at sites with lower than
average purchasing power and higher than average unemployment rate. The
Wilcoxon rank sum test (with continuity correction) confirmed that these
differences regarding the Purchasing Power Index between Edeka and NP (p =
0.0046) as well as between NP and Lidl (p = 0.0055) are statistically
significant. Figure 11 shows the population mean for the purchasing power
index, which rank differ for the different supermarket chains and the
population ranges. Sites without any supermarket have a low purchasing power,
which is most likely why no supermarket has been opened there.
This can be explained with the two different categories of stores in food
retailing: supermarkets and discount stores. Discount stores (e.g. _NP_ and to
certain extend _Lidl_) aim to sell products at prices lower than normal
supermarkets (_Edeka_ and _E-Center_) with a focus on price rather than on
service, display, or wide choice. Thus, supermarkets have to choose sites with
higher purchasing power as they are selling more expensive products than
discount stores, which can occupy the market segment of places with lower
available money as their products are less expensive.

Figure 11: Distribution of Supermarket Chains according to the Average
Purchasing Power Index and Unemployment Rate.

##  8 Explanation engine

Decision-makers in companies request to understand recommendations made by
support systems. If this requirement is not fulfilled as the system acts as a
black box, companies will not trust its results and thus, they will also not
make crucial business decisions based on these recommendations [15]. To
resolve this issue, QuIS not only provides recommendations, but also detailed
explanation about how decisions are made. By explaining how the system works
and how recommendations are generated, the proposed approach becomes more
transparent and trustful.

The proposed site selection system offers important benefits in terms of the
ease with, which the recommendations can be explained and justified. The data
model and the User Requirements Profile together enable the explanation engine
to describe recommended sites according the user’s requirements. A site
represented by its location factors are compared to the user’s requirements
and these comparisons are used to describe and explain the recommendations.
Figure 12 shows an example.
In the proposed approach, the data model contains all sites where each site is
modeled quantitatively with location factors. The requirements, in turn, are
represented with Decision Criteria, which are directly linked to location
factors by the use of Means-End Assignments. Since the weights of all Decision
Criteria are also given by the respective Qualitative Ratings, recommended
sites can be described by using the underlying location factors. As the
explanation engine knows the user’s requirements stored in the User
Requirements Profile, recommendations can be described with only these
location factors, which are relevant to the user.

Figure 12: Explaining the Recommendations for two Sites.

Following this procedure, the explanation engine can explain recommendations
to users why a site is recommended. For decision-makers, however, it is also
of interest why a site is not recommended given alternatives. Sites can be
described by the explanation engine with respect to the user’s requirement,
regardless if this particular site was recommended or not. The aforementioned
procedure can be used to explain why certain sites are not recommended.

##  9 Discussion and Future Work

In this paper, the novel approach QuIS for data-driven site selection is
proposed, which integrates Big Data into the decision making process of
companies. It combines a data model with an enhanced recommendation system,
which utilizes the existing knowledge in this application context. In contrast
to economic methods, the system does not need manual analysis or expert
knowledge. Additionally, it is capable of handling all available information
about sites. More than $200$ different attributes for all $11,162$
municipalities in Germany have been aggregated and analyzed. The evaluation of
supermarkets in Germany shows that there is a big coverage ($86.4\%$) between
existing stores and sites recommended by the proposed methods. Furthermore,
QuIS also recommends sites ($328$) for supermarket where a store should be
opened. Finally, decisive location factors, such as purchasing power, were
revealed, which have an impact on the existence of supermarkets.

Most important for the data-driven site selection approach is the dataset.
Currently, the level of granularity with municipalities being the smallest
unit limits the usability of system. As seen in Section 6, cities with
millions of inhabitants (such as Munich and Berlin) are only represented as
one site by a singly entity. A more detailed segmentation is needed to
generate better recommendations. The reason is neither a conceptual nor
technical limitation, but rather a practical issue. In theory, the data model
and based upon it, the proposed approach for recommending sites scales in both
breadth and depth: other nations or continents can be added as well as a
higher granular data (e.g. city districts or streets). In practice, however,
it is challenging to find data sources for geospatial data with both a high
spatial and temporal granularity.

There is much contextual information that could be incorporated into the
recommendation process in the future. Adomavicius and Sankaranarayanan [53]
used a multidimensional approach to incorporate such contextual information.
Additional dimensions, can add domain knowledge of locations or contextual
information about companies’ requirement. Nature reserves are protected areas
where construction is not permitted and nuclear power plants had to be kept at
a minimum distance to residential areas. Supplementary information might be
vital for some companies in their decision process and those additional
dimensions help excluding unsuitable locations from the very beginning.

In the future, most important is the conducting of larger experiments with
more location factors, investigated companies, and other industrial sectors
such as food chains or shopping malls in order to perform more robust
evaluations. Especially companies with hundreds or thousands of stores (e.g.
supermarkets, fast food restaurants, or cinemas) are applications of interest
for the proposed data-driven site selection.

The application focus can be easily widen as site selection is not limited to
industrial companies alone, but governments and private person as well can
make use of this system. Especially for private persons however the site
hierarchy has to be refined to include smaller area types, such as city
districts or even building blocks. Authorities could use the overall
recommendation system in reverse and analyze, which kind of companies might be
interested in a certain region that leads to another new area for economic
research.

The question that is most intriguing is what can be learned from geospatial
data. Information about the quality of existing sites will be investigated in
order to learn good and poor locations as well as further studies together
with economist. The inclusion of qualitative feedback about locations
regarding whether it is a ’good’ or a ’bad’ site will help to further increase
the performance. With such information sophisticated machine learning and data
mining techniques could be applied to possibly reveal more decisive location
factors even unknown to experts in this area. However, such information is not
readily available so this approach is most likely only possible in cooperation
with one or more companies.

Beside the focus of site selection in this study, the challenge for the future
is on pattern recognition and semantic analysis, regardless of the
application. In particular, the collected data are not yet further
investigated (right now, the temporal aspect is not incorporated) or combined
with inferable information like proximity measures. The position of companies,
stores and factories, extractable from (linked) geospatial data sources, can
also be used to realize data-driven economic competitor and customer analysis
in the future.

## Acknowledgments

This work was sponsored by Federal Ministry of Education and Research (grant
01IS12050).

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