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GENERAL INTRODUCTION  This thesis is breathing new life into the location choice models of establishments. The need for methodological advances in order to model more realistically the complexity of establishments' decision-making processes, such as their optimal location choices is the key motivation of this thesis. An establishment is defined as a distinct economic unit that produces goods or services at a single physical location. In contrast, a firm is a legal entity that consists of one or more establishments or plants under common ownership and control (van Wissen, 2000). 1.1 XXX GENERAL XXX  First, location choice models use geo-referenced data, for which choice sets have an explicit spatial component. It is thus critical to understand and represent spatial aspect in location choice models. Second, what makes these discrete choices particularly interesting and challenging to analyze is that decisions of a particular establishment are interrelated with choices of the others. These thorny problems posed by the interdependence of decisions generally cannot be assumed away, without altering the realism of the model of establishment decision making. The conventional approaches to location selection, i.e., traditional theory and methods, fail by providing only a set of systematic steps for problem-solving without considering strategic interactions between the establishments in the market. Less is known about how to correctly adapt location choice models to study establishments' discrete choices when they are interrelated. Third, a firm can open a number of units and serves the market from multiple locations. Once again, a conventional location theory may not be suitable to situations wherein individual establishments instead of locating independently from each other, form a whole large organization under common strategy and control. 1.2 KEY QUESTIONS  This thesis is breathing new life into the location choice models of establishments. The need for methodological advances in order to model more realistically the complexity of establishments' decision-making processes, such as their optimal location choices is the key motivation of this thesis. A clear distinction between an establishment and a firm should be made in the first place. An establishment is defined as a distinct economic unit that produces goods or services at a single physical location. In contrast, a firm is a legal entity that consists of one or more establishments or plants under common ownership and control (van Wissen, 2000).  A number of key questions will be addressed along this thesis. First, location choice models use geo-referenced data, for which choice sets have an explicit spatial component. It is thus critical to understand how to represent spatial aspect in location choice models. Second, what makes these discrete choices particularly interesting and challenging to analyze is that decisions of a particular establishment are interrelated with choices of the others. These thorny problems posed by the interdependence of decisions generally cannot be assumed away, without altering the realism of the model of establishment decision making. The conventional approaches to location selection fail by providing only a set of systematic steps for problem-solving without considering strategic interactions between the establishments in the market. One of the goals is to explore how to correctly adapt location choice models to study establishments' discrete choices when they are interrelated. Third, a firm can open a number of units and serves the market from multiple locations. Once again traditional theory and methods may not be suitable to situations wherein individual establishments instead of locating independently from each other, form a whole large organization following common strategy and control.  1) 1.2.1) WHY SHOULD WE BOTHER?  In this thesis, we concentrate our research on the Paris region, a vibrant and innovative region with over 5,6 million jobs, 37 percent of French executives, and 40 percent of national workforce in research and development. It is the first R&D hub in Europe and the third worldwide. 11.7 million people or over 19 percent of the country's population reside in the area which occupies only 2.2 percent of the surface of France. The Paris region is the third World touristic destination (in 2013) (Global Destination Cities Index 2015) with 16 millions of visitors from abroad. The GDP of the region amounts to 29 percent of total French GDP (IAU IdF, 2014) %31 percent of total French GDP (http://www.grand-paris.jll.fr/fr/paris/chiffres-cles/) that is 612 milliards euros (2012). It is 1st European city considering the number of firms classified in Fortune 500 (July 2014).  
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Much work has been done in the domain of location choice models, however, several issues arise when analyzing involved phenomena, which scholars have yet to fully inquired: 1) addressing the excess of zeros problem in the location choice model in highly heterogeneous geographic areas and 2) determining an appropriate way to accommodate spatial effects in location decisions. We respond to the complaint voiced by Liviano-Solis and Arauzo-Carod (2013) and Bhat et al. (2014) that heretofore the hurdle model technique has not been well investigated when analyzing location patterns. These are the first challenges that we face in the first chapter of this thesis.   2) 1.2.2)  %When selecting the appropriate location in which to set up in the market, an establishment may consider not only the characteristics of a particular area, but also the characteristics of neighboring zones.   The second chapter extends the research on the hurdle model and the study presented in the first part of this thesis. The final decision of an establishment seems to be related to the surrounding economic landscape. When accounting for the linkage between neighboring observations, the decision on the spatial weight matrix specification should be made. Yet, since there exist no solitary claim on the concept of space, the form of the weight matrix is largely debated. One of the problems hides in the definition of distance usually based on the straight-line segment connecting two locations. Euclidean distance is typically used and has been utilized in the first chapter to account for spatial spillovers in location choice model. However, Euclidean distance is believed to be only one simplistic possibility out of an infinite number of shortest path relations. Other alternative distance metrics may be proposed when building the spatial distance weight matrices. 
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%In spite of recognizing the importance of incorporating spatial effects in establishments location decision processes, the literature is still scarce on previous attempts.   3) 1.2.3)  In the third chapter we further enhance the literature on the location choices, this time incorporating strategic interactions among establishments. We shed light on strategic interactions, fundamental in establishments’ location choices, yet largely unheeded in the empirical literature. If establishments acted in isolation, it would be a relatively simple task to adapt existing discrete-choice models. Yet, being non-strategic means that an establishment ignores other players’ decisions. Less is known about how to correctly adapt location choice models to study establishments’ discrete choices when they are interrelated. In very sparse empirical applications, when locational choice models are developed for several activity sectors, each of the model is typically run independently. 
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4) 1.2.4)  The motivation of the last chapter comes from the fact that most previous discussion on locational decisions has one common feature of making unrealistic and restrictive assumptions and perceives the industry in terms of independent stores. The analysis of multi-store competition has started already with the trailbraking work of Teitz (1968) who introduced the idea that a firm can open multiple facilities in the context of Hotelling’s linear city model and serves the market from a number of locations. Yet, the subject of location of competing firms with multiple component units seems to have been largely unsung/unheeded in the spatial location literature (Peng and Tabuchi, 2007). This gap is inquisitive/inquiring for the systems which dominate in the market (Karamychev and van Reeven, 2009; Iida and Matsubayashi, 2011 ; Janssen et al., 2005 ; Pal and Sarkar, 2002 ; Peng and Tabu- chi, 2007). The conventional single-store location theory may not apply to situations wherein individual stores are part of larger organizations under common strategy, intuition, and control, where a centralization is applied to reach global goals and consider the interest of a firm as a whole (Thill, 1997). Conceptually, a firm selects a distribution of locations instead of choosing a point location (Chu and Lu, 1998). Our main motivation for this chapter is to combine a number of novel solutions into one model and to rectify several mathematical and methodological misconceptions made in numerous existing store-location papers. We incorporate strategic interactions between stores within the same firm and stores that belong to different chains. We consider spatial competition, business stealing and learning effects. We pay a particular attention to correctly capture market segments and to select potential customer groups by observing their characteristics, their mobility patterns, their trip chaining behavior, and activities’ purposes during the day and during the week. A clear distinction between a daytime and nighttime population present in a particular area is needed, and therefore a more appropriate distance measure to a store traveled by a potential customer is carefully proposed and applied in a more realistic manner than it has been done in the existing literature. Further, we consider the markets as being interdependent. A combination of all these elements can create a more authentic/more realistic and original model.