Geographical Data Imperfection 2 E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Geographical data often contains imperfections associated with insufficient precision, errors or incompleteness. If these imperfections are not identified, taken into account and controlled when using the data, the potential for errors may arise, leading to significant consequences with unforeseeable effects, particularly in a decisionmaking context. It is then necessary to characterize and model this imperfection, and take it into account throughout the process. In the previous volume, we introduced different approaches for defining, representing and processing imperfections in geographic data. Volume 2 will now present a number of concrete applications in a variety of fields, demonstrating the practical application of the methodology to use cases such as agriculture, natural disaster management, mountain hazards, land management and assistance for the visually impaired.
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Seitenzahl: 299
Veröffentlichungsjahr: 2024
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Table of Contents
Cover
Table of Contents
Title Page
Copyright Page
Preface
1 Implementation and Computation of Fuzzy Geographic Objects in Agriculture
1.1. Fuzzy geographic objects
1.2. Evaluation of the deposit on crops: formalizing fuzzy data
1.3. From the formalization of the problem to the presentation of the objects and their manipulation
1.4. Implementation and storage of fuzzy objects in a relational database
1.5. Some examples of calculations on fuzzy objects
1.6. Conclusion
1.7. References
2 Representation and Analysis of the Evolution of Agricultural Territories by a Spatio-temporal Graph
2.1. The data: the land parcel identification system
2.2. The model: a fuzzy spatiotemporal graph
2.3. The method: searching for frequent patterns
2.4. Characterizing agricultural regions by spatial-temporal patterns
2.5. Conclusion and outlook
2.6. References
3 Agricultural Areas in the Face of Public Environmental Policies: Spatiotemporal Analyses Using Sensitive Data
3.1. Project context and issues
3.2. What are the methods for anonymization?
3.3. Data presentation: data in an agricultural context
3.4. Treatments at the farm level: spatial structure versus AECM measures
3.5. Treatments at plot level: typology of land changes
3.6. Conclusion and perspectives
3.7. Acknowledgments
3.8. References
4 The Representation of Uncertainty Applied to Natural Risk Management
4.1. Introduction
4.2. Natural hazards: uncertain phenomena
4.3. Spatial representation of uncertainty: methods and interpretation
4.4. Analysis of uncertainty in natural hazard prevention maps
4.5. Representation of uncertainty in risk maps: assessment and perspectives
4.6. Conclusion
4.7. References
5 Incorporating Uncertainty Into Victim Location Processes in the Mountains: A Methodological, Software and Cognitive Approach
5.1. Introduction
5.2. Sources of imperfection
5.3. Detecting uncertainty and imprecision in the interface
5.4. Taking imperfection in spatialization into account
5.5. Restoring uncertainty in the interface
5.6. Conclusion and perspectives
5.7. References
6 Uncertainties Related to Real Estate Price Estimation Scales
6.1. Introduction
6.2. The effect of spatial support in real estate price estimation
6.3. Data and indicators for estimating the sensitivity of house prices to the scale of aggregation
6.4. Methodology for studying variations in real estate price estimates according to scale
6.5. Results: highlighting structural effects linked to territorial units and scale salience
6.6. Conclusion and discussion
6.7. References
7 Representing Urban Space for the Visually Impaired
7.1. Introduction
7.2. Landmarks as tools for moving around and finding your location
7.3. Integration of landmarks in tactile and multimodal maps
7.4. Integrating uncertainty into text descriptions
7.5. Conclusion and perspectives
7.6. Acknowledgments
7.7. References
List of Authors
Index
Other Titles
End User License Agreement
List of Tables
Chapter 2
Table 2.1. Characteristics of graphs for each region studied
Chapter 3
Table 3.1. Statistics of the three categories of land evolution in relation to...
Chapter 4
Table 4.1. Number of hazard intensity classes in the marine submersion, avalan...
Table 4.2. Visual variables used to represent hazard intensity classes
Chapter 7
Table 7.1. Summary of the four route descriptions
List of Illustrations
Chapter 1
Figure 1.1. A representation of a fuzzy geographic object.
Figure 1.2. Example of fuzzy spatial regions represented by a set of α-cuts...
Figure 1.3. A fuzzy subset A and its α-cuts for which μα(x) ≥ α....
Figure 1.4. Triangular fuzzy number.
Figure 1.5. Intersection between two fuzzy geographical areas.
Figure 1.6. Q possible fuzzy quantity of treatment product on an area.
Figure 1.7. Processing area A: the fuzzy spatial object is a stack of α-cuts A...
Figure 1.8. Representation of a fuzzy geographic object.
Figure 1.9. Calculating the intersection between fuzzy areas.
Figure 1.10. Visualization of the total quantity of an object.
Chapter 2
Figure 2.1. Extract from the 2019 edition of the LPIS for the Somme (Crécy-en-...
Figure 2.2. (a) Example of the evolution of an agricultural territory between ...
Figure 2.3. The DC, EC and PO relations between two regions A and B
Figure 2.4. The different configurations of the EC relationship for regions wi...
Figure 2.5. Thickening of the boundary of a parcel on both sides of the origin...
Figure 2.6. Three subgraphs (blue, yellow, red) in a graph G: the blue and yel...
Figure 2.7. Location of the studied areas (Google Maps background
©
).
Figure 2.8 Examples of frequent patterns found for the Gers: in blue, soft win...
Figure 2.9 Example of a pattern found for the Bas-Rhin: in purple, vines for w...
Figure 2.10 Examples of frequent patterns found for the Eure-et-Loir: in blue,...
Figure 2.11 Examples of frequent patterns found for the Somme (Ponthieu region...
Chapter 3
Figure 3.1. The four study areas of the FARMaine project in the Maine River Ba...
Figure 3.2. Inconsistent parcel boundaries between 2009 (background layer) and...
Figure 3.3. Centroid of a farm: the polygons represent the blocks of a farm an...
Figure 3.4. Principles of the fragmentation (left) and dispersion (right) indi...
Figure 3.5. Method of calculating the dispersion index
Figure 3.6. Overlay of the operating centroids with the grid: the points and s...
Figure 3.7. Mapping the intensity of AECM adoption in 2015 in a BVA sector.
Figure 3.8. Mapping of fragmentation in 2015 in one area of the BVA.
Figure 3.9. AECM adoption intensity (height) versus fragmentation (color) in B...
Figure 3.10. Representation of the variables according to the factorial axes 1...
Figure 3.11. Map of the evolution of the plots on the FarmSIG site (openstreet...
Chapter 4
Figure 4.1. The risk management cycle [FOC 01]
Figure 4.2. Spatiotemporal classification of hazards according to [LEO 10]
Figure 4.3. Uncertainty components of hazard zone delineation in the face of m...
Figure 4.4. Marine submersion areas modeled with different water heights (a), ...
Figure 4.5. The egg yolk model used to delineate vague spatial objects.
Figure 4.6. Continuous (left) and discrete (right) representation of fuzzy obj...
Figure 4.7. The seven visual variables proposed by Bertin [BER 67].
Figure 4.8. Examples of uncertain boundary representations between a safety zo...
Figure 4.9. Risk prevention plan for marine submersion in Loire-Atlantique. Gu...
Figure 4.10. Location of PPRs used in the analysis [SOL 16].
Figure 4.11. Example of legends used in different PPRs.
Figure 4.12. Residual hazard zones (in gray) on the Flood PPR of Nîmes [PRE 12...
Figure 4.13. Symbology used for avalanche occurrence maps [INR 05].
Figure 4.14. Proposal of uncertainty representation for point data, using inte...
Figure 4.15. Representation of imprecise boundaries of landslide areas [ARN 09...
Figure 4.16. Representation of objects with uncertain contours by using “sketc...
Chapter 5
Figure 5.1. Situation map for the Grand Veymont Alert.
Figure 5.2. Clue creation panel – first clue (left). Clue creation panel – thi...
Figure 5.3. Table of clues and hypotheses.
Figure 5.4. Example of the masking of the map background by the rendering of a...
Figure 5.5. Comparison of solutions based on the use of transparency and symbo...
Figure 5.6. Example of possible confusion between the figures and the elements...
Figure 5.7. Overview of the ISA and fragments of the PLZ (in red), calculated ...
Figure 5.8. Detailed view of three fragments of the PLZ (represented in propor...
Figure 5.9. Display (contour + transparency) of the CLZs corresponding to (A) ...
Figure 5.10. PLZ display – overview: OpenTopoMap map background (OpenStreetMap...
Figure 5.11. Display of the main fragment of the PLZ using the proportional ci...
Figure 5.12. Display of the main fragment of the PLZ, as well as the contours ...
Figure 5.13. Representation of the victim’s position for the Grand Veymont Ale...
Chapter 6
Figure 6.1. Nested administrative divisions in the Provence Alpes Côte d’Azur ...
Figure 6.2. Focus on the Martigues sector: localized mutations, INSEE grids an...
Figure 6.3. UML diagram presenting the organization of the multi-scale databas...
Figure 6.4. Exploration of cadastral sections of the city of Avignon on the DV...
Figure 6.5. Exploration of the mutated parcels of a cadastral section of the c...
Figure 6.6. Example of an aggregated statistical theoretical calculation for n...
Figure 6.7. Example of a price permutation constrained by the location of the ...
Figure 6.8. Variations (top) of medians of medians (left) and averages of aver...
Figure 6.9. Variations (top) of medians of medians (left) and averages of aver...
Figure 6.10. Variations (top) in mean standard deviations for apartments (left...
Chapter 7
Figure 7.1. Synthetic representation of the classification of clues [LON 97] a...
Figure 7.2. Examples of fuzzy numbers describing the probability of detecting ...
Figure 7.3. Landmark ontology for visually impaired people.
Figure 7.4. Examples of inferences obtained using the HermiT reasoner by popul...
Figure 7.5. Classification and fuzzy number modeling the probability of detect...
Figure 7.6. Examples of city- or neighborhood-scale maps from our collection....
Figure 7.7. Example of a map showing a square with several complex intersectio...
Figure 7.8. Steps in the production of a 3D map [JIA 21] from OpenStreetMap da...
Figure 7.9. Enhancement of a background map for tactile printing using the cor...
Figure 7.10. Proposed instructions associated with the seven sections of the f...
Guide
Cover Page
Table of Contents
Title Page
Copyright Page
Preface
Begin Reading
List of Authors
Index
Other Titles
WILEY END USER LICENSE AGREEMENT
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Geographical Data Imperfection 2
Use Cases
Edited by
François Pinet
Mireille Batton-Hubert
Eric Desjardin
First published 2023 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.
Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address:
ISTE Ltd27-37 St George’s RoadLondon SW19 4EUUK
www.iste.co.uk
John Wiley & Sons, Inc.111 River StreetHoboken, NJ 07030USA
www.wiley.com
© ISTE Ltd 2023The rights of François Pinet, Mireille Batton-Hubert and Eric Desjardin to be identified as the authors of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988.
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s), contributor(s) or editor(s) and do not necessarily reflect the views of ISTE Group.
Library of Congress Control Number: 2023944988
British Library Cataloguing-in-Publication DataA CIP record for this book is available from the British LibraryISBN 978-1-78630-298-4
Preface
Geographic data often contain imperfections that are related to a lack of precision, errors, incomplete data, etc. If these imperfections are not identified, taken into account and controlled when using these data, then risks of errors can appear and lead to important consequences with unforeseeable effects, especially in a decision-making context. It is then necessary to characterize and model this imperfection, and then to take it into account throughout the processing.
In the previous volume, we presented different approaches to define, represent and deal with the imperfection of geographic data. In this volume, we present several concrete applications in various domains: agriculture, natural disaster management, mountain risks, land management and assistance to visually impaired people.
The book is organized as follows. Chapter 1 aims at modeling the uncertainty of agricultural inputs on plots using fuzzy subsets. One of the objectives is to estimate the possible quantities of products during agricultural treatment coverage over time. Chapter 2 analyzes the evolution of land use from the agricultural parcel register. The methods presented take into account the imperfect nature of the spatial objects and extract the different spatiotemporal relationships existing between the objects. Chapter 3 shows how it is possible to voluntarily integrate uncertainty in data in order to guarantee the confidentiality of personal information, while allowing an exploitation of aggregated data. The application concerns agricultural areas. Chapter 4 shows how uncertainty is taken into account in the delimitation of danger areas associated with natural hazards, and how it is represented in cartographic documents. Chapter 5 proposes the elaboration of a logical reasoning model allowing us to go from descriptions of relative location to the construction of possible geolocalized zones. The simultaneous consideration of verbatim and their transcription by a spatial analysis manipulating imprecision and uncertainty is developed in the context of victim location in the mountains. Chapter 6 is devoted to the uncertainty on the estimation of real estate prices for a geographical area. It considers, in particular, the uncertainty related to the “statistical-spatial” distribution of prices by evaluating the effect of spatial support on real estate price estimates for different territorial grids. Chapter 7 is devoted to the role of landmarks in the construction of urban routes for visually impaired people. The aim is to integrate the uncertainty concerning both the permanence and the reliability of a geographic object and its modeling in an adapted ontology and finally to propose a method of integration in the multimodal map.
This book is the result of the collective work of the prospective action Incertitude épistémique : des données aux modèles en géomatique (Epistemic Uncertainty: From Data to Models in Geomatics) of the CNRS MAGIS Research Group. We would like to thank all of the members of the action for their work and reflections that led to this book, and we wish readers a pleasant reading.
François PINET, Mireille BATTON-HUBERT and Eric DESJARDINSeptember 2023
