François Pinet is a researcher at the French National Research Institute for Agriculture, Food and the Environment.Mireille Batton-Hubert is a professor at the École Nationale Supérieure des Mines de Saint- Étienne, France.Eric Desjardin is a lecturer at the University of Reims Champagne-Ardenne within the STIC Research Center (CReSTIC), France.

• Preface xiFrançois PINET, Mireille BATTON-HUBERT and Eric DESJARDINChapter 1 Implementation and Computation of Fuzzy Geographic Objects in Agriculture 1Mireille BATTON-HUBERT, François PINET and André MIRALLES1.1 Fuzzy geographic objects 11.2 Evaluation of the deposit on crops: formalizing fuzzy data 51.3 From the formalization of the problem to the presentation of the objects and their manipulation 61.3.1 Materials and methods 71.3.2 Elements for the construction of manipulated fuzzy objects and their associated quantities 121.4 Implementation and storage of fuzzy objects in a relational database 161.5 Some examples of calculations on fuzzy objects 181.5.1 Intersection between fuzzy areas 181.5.2 Amount of product associated with an α-cut of a treatment area 191.5.3 Calculating the fuzzy surface of a fuzzy space object 191.5.4 Calculation for an α-cut of its possible product quantity using its area 201.5.5 Going further 221.6 Conclusion 231.7 References 24Chapter 2 Representation and Analysis of the Evolution of Agricultural Territories by a Spatio-temporal Graph 25Aurélie LEBORGNE, Ezriel STEINBERG, Florence LE BER and Stella MARC-ZWECKER2.1 The data: the land parcel identification system 252.2 The model: a fuzzy spatiotemporal graph 272.2.1 Graph structure 272.2.2 Spatial relationships 282.2.3 Spatiotemporal relationships 302.2.4 Relationships of filiation 302.3 The method: searching for frequent patterns 312.3.1 Overview 312.3.2 Methods for subgraph mining 322.4 Characterizing agricultural regions by spatial-temporal patterns 322.4.1 Data from Gers 342.4.2 Data from Bas-Rhin 352.4.3 Data from Eure-et-Loir 362.4.4 Data from the Somme 362.5 Conclusion and outlook 382.6 References 39Chapter 3 Agricultural Areas in the Face of Public Environmental Policies: Spatiotemporal Analyses Using Sensitive Data 41Jean-Michel FOLLIN, Nathalie THOMMERET and Marie FOURNIER3.1 Project context and issues 423.2 What are the methods for anonymization? 443.2.1 For the attributes 443.2.2 For localization 443.3 Data presentation: data in an agricultural context 453.4 Treatments at the farm level: spatial structure versus AECM measures 503.4.1 Determining the virtual seats 503.4.2 Structure analysis: the indicators used 513.4.3 Analysis of AECM intensity 533.4.4 Introduction of uncertainty at the level of the location by squaring 543.5 Treatments at plot level: typology of land changes 573.5.1 Construction of a multi-date database 573.5.2 Classification 593.5.3 Measurement of uncertainty based on class homogeneity percentages 613.6 Conclusion and perspectives 643.7 Acknowledgments 643.8 References 65Chapter 4 The Representation of Uncertainty Applied to Natural Risk Management 67Jean-François GIRRES4.1 Introduction 674.2 Natural hazards: uncertain phenomena 684.2.1 Risk, hazard and exposure 684.2.2 Spatial and temporal uncertainty of natural hazards 704.2.3 Sources of uncertainty in natural hazard modeling 714.3 Spatial representation of uncertainty: methods and interpretation 734.3.1 Representation of uncertain spatial objects 744.3.2 Visual variables for representing uncertainty 754.3.3 Representation of uncertainty and decision-making 774.4 Analysis of uncertainty in natural hazard prevention maps 784.4.1 Risk prevention plans 784.4.2 Modeling of hazard zones in risk prevention plans 804.4.3 Methodology for analyzing the representation of uncertainty 814.4.4 Results and comments 824.5 Representation of uncertainty in risk maps: assessment and perspectives 864.5.1 How uncertain are risk prevention plans? 864.5.2 Contributions to the spatial representation of uncertainty 884.6 Conclusion 904.7 References 91Chapter 5 Incorporating Uncertainty Into Victim Location Processes in the Mountains: A Methodological, Software and Cognitive Approach 95Matthieu VIRY, Mattia BUNEL, Marlène VILLANOVA, Ana-Maria OLTEANU-RAIMOND, Cécile DUCHÊNE and Paule-Annick DAVOINE5.1 Introduction 955.2 Sources of imperfection 985.2.1 Imprecision in location expression associated with a clue 995.2.2 Uncertainty in location expression associated with a clue 1005.2.3 Incompleteness of geographical data 1015.3 Detecting uncertainty and imprecision in the interface 1015.3.1 Formalization of an alert according to the ontologies of the CHOUCAS project 1025.3.2 Specific acquisition components 1045.4 Taking imperfection in spatialization into account 1075.4.1 Relationship between CLZ and PLZ and the construction of the PLZ 1075.4.2 The process of creating CLZs 1085.4.3 Taking imprecision into account 1095.4.4 Taking uncertainty and incompleteness into account 1115.5 Restoring uncertainty in the interface 1125.5.1 "Classic" solution 1125.5.2 Solution based on figures of varying sizes 1145.5.3 Solution by combining representations 1165.5.4 Illustration of the Grand Veymont Alert 1185.6 Conclusion and perspectives 1225.7 References 123Chapter 6 Uncertainties Related to Real Estate Price Estimation Scales 127Didier JOSSELIN, Delphine BLANKE, Mathieu COULON, Guilhem BOULAY, Laure CASANOVA ENAULT, Antoine PERIS, Pierre LE BRUN and Thibault LECOURT6.1 Introduction 1276.2 The effect of spatial support in real estate price estimation 1296.2.1 The generation of uncertainty in the choice of aggregation scales 1296.2.2 Real estate price representation scales rarely questioned 1316.2.3 Different scales of real estate price structuring 1336.3 Data and indicators for estimating the sensitivity of house prices to the scale of aggregation 1356.3.1 Different territorial grids to test the effects of aggregation 1356.3.2 A national database of geolocalized real estate transactions in France: DVF 1406.3.3 Representation of aggregated statistics in the form of scalograms 1436.4 Methodology for studying variations in real estate price estimates according to scale 1446.4.1 Preliminary methodological considerations 1446.4.2 A random sample generator to eliminate scale uncertainty 1456.4.3 Presentation of analysis elements in the form of a composite graph 1476.5 Results: highlighting structural effects linked to territorial units and scale salience 1486.5.1 An analysis of changes in estimates of average and median prices for apartments and houses in the Provence Alpes Côte d'Azur region from 2014 to 2020 1486.5.2 Analysis of changes in standard deviation estimates of average prices for apartments and houses in theProvence Alpes Côte d'Azur region from 2014 to 2020 1516.5.3 Cross-sectional analysis on composite graphs 1556.6 Conclusion and discussion 1576.7 References 160Chapter 7 Representing Urban Space for the Visually Impaired 165Lisa DENIS, Jérémy KALSRON and Jean-Marie FAVREAU7.1 Introduction 1657.2 Landmarks as tools for moving around and finding your location 1667.2.1 Gathering needs and uses 1677.2.2 Modeling landmarks and their uses 1697.2.3 Model expressiveness 1757.3 Integration of landmarks in tactile and multimodal maps 1777.3.1 Background map 1777.3.2 Integration of landmarks 1807.4 Integrating uncertainty into text descriptions 1837.4.1 Integrating the probability of detecting landmarks 1837.4.2 Integrating blurred distances and angles 1847.5 Conclusion and perspectives 1867.6 Acknowledgments 1877.7 References 187List of Authors 189Index 193