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The Concept of Information / Communi-cation
The concept of information is present at the heart of several debates, which go far beyond the circle of economists. Various theoretical approaches were mobilized in order to better understand the way economic agents search for, process and disseminate the information. The idea that information is worth something, opens the way to a broader set of de nitions. It is a di erence, a variation, something that could be perceived or estimated. As de ned by G. Bateson:
\Information is a di erence that makes a di erence » (Bateson, 1972).
Another well-known citation comes as follows:
\Information is data processed for a purpose » (Curtis, 1989).
Initially, the concept of information was linked to the transmission or com-munication processes; more speci cally, a transformation process for those who produce the information, and an acquisition one for those who receive it (Porat, 1977). With the multiplication of sources and the sophistication of transformation mechanisms, the use of information is increasingly fram-ing the decision-making practices of the economic agents (Hurwicz, 1994, Bernardo & Smith, 2009, Kochenderfer, 2015). In a technological context ex-tremely accelerating, highlighting the e ciency of these processes in uenced by any activities related to the collection and producing the information is necessary (Gallouj, 1994; Coiera, 2000; Niyato et al., 2016). Due to the devel-opment of the internet, the telecommunication systems and the technology, the information induces organizational e ects with a very broad scale (Foray & Lundvall, 1996; Castells, 2010). In addition to structural changes and transformation in the nature of activities, we emphasize the market expan-sion and the emergence of innovation processes, aiming almost every domain in the recent economy (Freeman, 1995, Langlois & Garrouste, 1997, Gawer et al., 2009).
The Information Theory
The information theory initiated with the works of (Shannon, 1948) and (Shannon & Weaver, 1949). The two authors underlined the link and the non-separability of the information and communication concepts. The basic idea of information theory is to measure the rate of information ow as the rate of uncertainty reduction. It therefore starts with a measure of uncer-tainty, called entropy. Then information is thought of as moving through a \channel, » in which one enters input data, and output data emerges, possi-bly error carried. The Shannon’s formula below, describes the informative content of a random source: n X H (X) = pi log pi i=1.
With H(X) representing the amount of information contained or deliv-ered by an information source (signal); X discrete random variable having n symbols, with each symbol xi having a probability pi to appear. While con-sidering a set of signals, the average amount of information included within the signals could be computed. The emission of these signals constitutes a stochastic process, invariable over time. In other words, the signals are trans-mitted to an agent via possible events and states in which the system may exist. Thus, the distribution of probabilities concerns a given set of events, and the quantity of information is therefore maximized once the events are with equal probability.
However, there were several objections to the philosophical generalization of this theory. (Savage, 1954) saw in Shannon’s formula no interest in the theory itself, except through the developments it allows. On the other hand, (Boulding, 1955) estimated that this theory identi es the signals and the time needed to acquire the information, while neglecting its proper value. This could also be found years later in (Arrow, 1984), commenting Shan-non’s work for what it represents as a useful notion of the cost of acquisition of information, and not of the weight of the value of information. The sev-eral limitations of the Shannon’s results concerned primarily the economists, starting with the fact that it is not a theory of information, but a mathemat-ical theory of communication and signal processing. Despite these facts, the economists were not restrained from applying these concepts in studying the economic forecasting and integrating the basic informational statistical works into econometrics. As an example, (Onicescu & Botez, 1978) were among the rst researchers who introduced the statistical theories of information in econometrics.
On the other hand, further works avoided a direct application of the in-formation theory, to move towards a development of the \economics of infor-mation », based on the analysis of the supply and demand for the information itself.
The Economics of Information
By seeking to identify an economic concept of information, it is understood that in its complexity, this concept is irreducible to a nite model. In order to formulate an economy of information, we are taken to consider a diversity of the paradigmatic eld in which this perception ts.
The literature trying to introduce a signi cance to the information, do not leave the economists indi erent. Dealing with the economics of informa-tion leads to several possible de nitions, either in the market organization or in the establishment of productive structures. Figure 2.1 describes the inci-dence of the term \information economics » and \economics of information » normalized by the use of the word \economics » in books published from 1900 to 20081.
The Geospatial Information (GI)
The geospatial information (GI), is an information describing the location of things and the way they relate to one another on the earth’s surface (Dev-illers et al., 2005). This information includes statistical data, remote sensing, surveying technologies and mapping, charting and related products. We nd the terms « geospatial information », « spatial information »and « location in-formation », often used for the same purpose.
Among the rst applications beginnings of the 20th century, the US farm-ers in the 1930s, used several basic techniques based on spatial information (aerial photo, sky shot, etc.) to perform land analysis coupled with an eco-nomic reasoning (Donaldson & Storeygard, 2016). Since then, an impressive change occurred in the way the Earth is being watched from above. With the emergence of the new technology, the computer sciences and the inter-net, the GI is increasingly present in a numerous elds (Tonneau et al., 2017).
Recently, GI is taking a fundamental role in the economics of our emerg-ing information society. It is contributing in monitoring several of the world’s greatest issues and considered one of the most essential elements underpin-ning the decision-making with applications targeting multiple domains: the environment, the land and resource management, the climate change, the health risks, the ecosystem services monitoring, the demographic statistics, the smart cities, etc. (Borzacchiello & Craglia, 2012; Vernier et al., 2017; Roche, 2016, 2017).
In our context, we were interested in a particular type of GI, that result-ing from satellite observation. Therefore, one of the economic constraints linked to that kind of information, lies in the capacity of acquisition of the satellites, the initial data providers. It is in direct link with the ecosystem of the GI and it is important to think about it from an economic point of view. The satellites, once put in orbit, the costs of GI production are negligi-ble. The remote sensing technologies can collect panel data at low marginal cost, repeatedly, and at large scale on proxies for a wide range of characteris-tics. By the time the satellites are in orbits, the production costs of GI data may be ignored (they are powered by solar energy, run on software already programed in advance, with technicians managing on land the operations, etc.) compared to the initial investment phase. This issue reminds us about the old paradox of the \Voyageur de Calais » related to the railway infras-tructures. The paradox illustrates on one hand, the discontinuous nature of marginal costs when xed costs play a predominant role in relation to the variable costs, and on the other hand, the di culty of integrating long-term investments (Allais, 1989).
Table of contents :
1.2 Problem Statement
1.3 Thesis Objectives
1.4 The GEOSUD SDI
1.5 Thesis Structure
2 SDI & Information
2.1 The Concept of Information / Communication
2.2 The Information Theory
2.3 The Economics of Information
2.4 The Geospatial Information (GI)
2.5 The Spatial Data Infrastructures (SDIs)
2.5.1 The Concept of SDIs
2.5.2 The Economic Challenges of SDIs
2.5.3 The SDI Management
2.5.4 The Economic Valuation of Geospatial Information via a SDI
2.5.5 The valuation of SDIs
2.5.6 The SDI Market Stability
3 Summary of Papers
3.1 Paper I – Spatial Data Infrastructure Management: A twosided market approach for strategic re ections
3.2 Paper II – How much would you pay for a satellite image?
Lessons learned from a French Spatial Data Infrastructure
3.3 Paper III – Making the most of \heterogeneous » information by using Blackwell and Entropy theories: A decision support policy applied to forests’ clear-cut control
3.4 Paper IV – Identifying the economic impacts of a Spatial Data Infrastructure
3.5 Paper V – Examining market stability using the Records theory: Evidence form French Spatial Data Infrastructures .
4 SDI Management
4.2 Background literature
4.3 Spatial Data Infrastructures as two-sided market
4.3.1 The satellite imagery market
4.3.2 The image-based applications market
4.3.3 Network externalities & Non-neutrality of prices .
4.4 Case study: the GEOSUD SDI
4.4.1 Data collection & analysis
4.4.2 Case description
4.5 Results: challenges and dynamics of the GEOSUD SDI according to a two-sided market approach
4.6 Discussion: what are the lessons to be learned?
5 SDI Economic Valuation
5.2.1 Description of the case study: The GEOSUD SDI users and data characteristics
5.2.2 Survey design and data collection
5.2.3 Model estimation
5.2.4 Application to the GEOSUD SDI
5.3.1 Descriptive results
5.3.2 Statistical Results
22.214.171.124 Sector-by-sector analysis
126.96.36.199 Membership analysis
188.8.131.52 Volume Analysis
5.4.1 The satellite imagery WTP
5.4.2 The satellite imagery WTP among sectors
5.4.3 The membership fees WTP
5.4.4 The HR satellite images: a place between the free MR and the VHR commercial images
5.4.5 The SDI pooling mechanisms supporting the use and access to satellite imagery
5.4.6 The satellite images meet the organizational routine concept
5.4.7 The satellite imagery as an `informational asset’
5.4.8 From an image-based towards a data streaming model
6 SDI Information Structure
6.2 Review of classical results
6.3 General context and notations
6.3.1 Blackwell’s approach
6.3.2 Entropy approach
6.4 Case study
6.4.1 Data collection and analysis
6.4.2 Case description
6.5.1 Entropy Results
6.5.2 Blackwell results
7 SDI Economic Impacts
7.2 L’information satellitaire et l’IDGS GEOSUD comme source de productivite et d’innovation
7.3 Methodologie de l’evaluation pour la gestion des coupes rases .
7.3.1 Elaboration du questionnaire et realisation de l’enqu^ete
7.3.2 Presentation de l’echantillon enqu^ete
7.4 Details de l’evaluation et des resultats par type d’impact .
7.4.1 Impacts de la production et de la fourniture des images
184.108.40.206 La valeur ajoutee generee par ADS
220.127.116.11 Les economies de mutualisation generees par GEOSUD
7.4.2 Impacts de l’usage des images satellitaires de GEOSUD au sein des DRAAF et DDT(M)
18.104.22.168 Economies de co^uts de fonctionnement
22.214.171.124 Economies de temps de travail pour le suivi des coupes rases
126.96.36.199 Impacts sur les recettes publiques
188.8.131.52 Impacts sur le reseau et la gouvernance
184.108.40.206 Impacts sur les competences
7.4.3 Impacts sur les acteurs de la liere-bois
7.5 Synthese des resultats et discussion
7.5.1 Des gains de co^ut de transaction et des eets de creation de valeur ajoutee signicatifs
7.5.2 Un appui a moyen terme en faveur de processus d’innovation ouverte
8 SDI Market Stability
8.2.1 SDI characteristics’|GEOSUD / PEPS
8.2.2 Satellite-images schemes
220.127.116.11 GEOSUD high resolution satellite imagery .
18.104.22.168 PEPS satellite imagery: Landsat, Sentinel and SPOT
8.2.3 Data collection
8.2.4 Records theory model | general context
22.214.171.124 Classical model
126.96.36.199 Beyond iid |Yang-Nevzorov
188.8.131.52 Distribution free estimation of
184.108.40.206 Goodness of t test
8.3 Results and discussion
9 Conclusions and Future Works
9.2 Future works