Economie informelle et pauvreté en Turquie : une analyse des comportements individuels sur des données des dépenses monétaires et temporelles de 2003 à 2006. / Since it was first introduced by Hart in 1973, the concept of “informal economy” has had vast implications for social-scientific research. Over the last four decades, informal economy has received increased attention in literature and has been keenly discussed by public authorities and scholars. There were two main motivations behind these efforts to identify the informal economy: to measure its size and to know its determinants. From a practical point of view, informal economy has been an enigma for economists seeking to identify its nature and to measure activities that have various economic motivations. Informality has been denoted by many names, such as “shadow”, “underground”, “second” or “parallel” economy- a plethora of terms resulting from the struggle to define informality. Likewise, the various approaches to studying the phenomenon differ greatly in the way that they relate to socio-economic characterization. Although there is great variation between definitions of informality, these diversifications allow authorities to deal more easily with the source of the problem, being able to inform themselves and create accurate policies. Generally speaking, these policies aim to increase the level of productivity for any given sector and to protect growth in an economy as a whole. The implicit goal of these strategies is to prevent informal earnings by protecting formal market transactions (Schneider and Enste, 2002) and thereby combat informality. To this end, identifying the stimulating economic factors behind informal activities by gathering information about participants, their actions and the concurrency of these activities becomes essential for the optimal distribution of economic resources.

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Les empreintes digitales recueillies par détection systèmes offrent de nouvelles façons de percevoir les comportements d'un individu. En outre, de grandes collections d'empreintes numériques des communautés apportent de nouvelles compréhensions des comportements humains. La perception des comportements humains à partir des empreintes digitales de détection permet de construire des nouvelles applications sur les systèmes de détection. D’après les empreintes digitales recueillies avec l'accéléromètre embarqué dans les téléphones mobiles et les taxis équipés avec GPS, nous présentons ici notre travail sur la reconnaissance des comportements individuels, la capture des comportements communautaires et la démonstration des nouveaux services activés. En reconnaissant les comportements individuels, nous présentons la reconnaissance des activités physiques d'une personne avec les lectures de l'accéléromètre recueillies à partir des téléphones mobiles mis dans les poches autour de la zone pelvienne. Avec les empreintes GPS d'un taxi, nous résumons les comportements anormaux du transport des passagers pour un individu et améliorons l'efficacité de la reconnaissance de la méthode existante IBOAT. En outre, sur la base des observations dans la vie réelle, nous proposons une méthode pour détecter les événements de changement de service d’un taxi individuel. Avec des traces GPS à grande échelle et à l’aide des milliers de taxis, nous étudions les comportements anormaux pour le transport des passagers et les comportements de changement de travail et exploitons les stratégies de service de taxi. En outre, nous présentons un système intelligent de taxi comme une étude exemplaire des nouvelles applications qui s’appuie sur les comportements perçus individuelles et communautaires / The digital footprints collected from the prevailing sensing systems provide novel ways to perceive an individual's behaviors. Furthermore, large collections of digital footprints from communities bring novel understandings of human behaviors from the community perspective (community behaviors), such as investigating their characteristics and learning the hidden human intelligence. The perception of human behaviors from the sensing digital footprints enables novel applications for the sensing systems. Bases on the digital footprints collected with accelerometer-embedded mobile phones and GPS equipped taxis, in this dissertation we present our work in recognizing individual behaviors, capturing community behaviors and demonstrating the novel services enabled. With the GPS footprints of a taxi, we summarize the individual anomalous passenger delivery behaviors and improve the recognition efficiency of the existing method iBOAT by introducing an inverted index mechanism. Besides, based on the observations in real life, we propose a method to detect the work-shifting events of an individual taxi. With real-life large-scale GPS traces of thousands of taxis, we investigate the anomalous passenger delivery behaviors and work shifting behaviors from the community perspective and exploit taxi serving strategies. We find that most anomaly behaviors are intentional detours and high detour inclination won't make taxis the top players. And the spatial-temporal distribution of work shifting events in the taxi community reveals their influences. While exploiting taxi serving strategies, we propose a novel method to find the initial intentions in passenger finding. Furthermore, we present a smart taxi system as an example to demonstrate the novel applications that are enabled by the perceived individual and community behaviors