Geographical Data Imperfection 2

Name: Geographical Data Imperfection 2 | Use Cases
Brand: Wiley-Scrivener
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Use Cases

Francois Pinet Mireille Batton-Hubert Eric Desjardin(Herausgeber*in)

Wiley-Scrivener (Verlag)

1. Auflage

Erschienen am 15. Februar 2024

224 Seiten

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Inhalt

Cover
Title Page
Copyright Page
Contents
Preface
Chapter 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
1.3.1. Materials and methods
1.3.2. Elements for the construction of manipulated fuzzy objects and their associated quantities
1.4. Implementation and storage of fuzzy objects in a relational database
1.5. Some examples of calculations on fuzzy objects
1.5.1. Intersection between fuzzy areas
1.5.2. Amount of product associated with an ƒ¿-cut of a treatment area
1.5.3. Calculating the fuzzy surface of a fuzzy space object
1.5.4. Calculation for an ƒ¿-cut of its possible product quantity using its area
1.5.5. Going further
1.6. Conclusion
1.7. References
Chapter 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
2.2.1. Graph structure
2.2.2. Spatial relationships
2.2.3. Spatiotemporal relationships
2.2.4. Relationships of filiation
2.3. The method: searching for frequent patterns
2.3.1. Overview
2.3.2. Methods for subgraph mining
2.4. Characterizing agricultural regions by spatial-temporal patterns
2.4.1. Data from Gers
2.4.2. Data from Bas-Rhin
2.4.3. Data from Eure-et-Loir
2.4.4. Data from the Somme
2.5. Conclusion and outlook
2.6. References
Chapter 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?
3.2.1. For the attributes
3.2.2. For localization
3.3. Data presentation: data in an agricultural context
3.4. Treatments at the farm level: spatial structure versus AECM measures
3.4.1. Determining the virtual seats
3.4.2. Structure analysis: the indicators used
3.4.3. Analysis of AECM intensity
3.4.4. Introduction of uncertainty at the level of the location by squaring
3.5. Treatments at plot level: typology of land changes
3.5.1. Construction of a multi-date database
3.5.2. Classification
3.5.3. Measurement of uncertainty based on class homogeneity percentages
3.6. Conclusion and perspectives
3.7. Acknowledgments
3.8. References
Chapter 4. The Representation of Uncertainty Applied to Natural Risk Management
4.1. Introduction
4.2. Natural hazards: uncertain phenomena
4.2.1. Risk, hazard and exposure
4.2.2. Spatial and temporal uncertainty of natural hazards
4.2.3. Sources of uncertainty in natural hazard modeling
4.3. Spatial representation of uncertainty: methods and interpretation
4.3.1. Representation of uncertain spatial objects
4.3.2. Visual variables for representing uncertainty
4.3.3. Representation of uncertainty and decision-making
4.4. Analysis of uncertainty in natural hazard prevention maps
4.4.1. Risk prevention plans
4.4.2. Modeling of hazard zones in risk prevention plans
4.4.3. Methodology for analyzing the representation of uncertainty
4.4.4. Results and comments
4.5. Representation of uncertainty in risk maps: assessment and perspectives
4.5.1. How uncertain are risk prevention plans?
4.5.2. Contributions to the spatial representation of uncertainty
4.6. Conclusion
4.7. References
Chapter 5. Incorporating Uncertainty Into Victim Location Processes in the Mountains: A Methodological, Software and Cognitive Approach
5.1. Introduction
5.2. Sources of imperfection
5.2.1. Imprecision in location expression associated with a clue
5.2.2. Uncertainty in location expression associated with a clue
5.2.3. Incompleteness of geographical data
5.3. Detecting uncertainty and imprecision in the interface
5.3.1. Formalization of an alert according to the ontologies of the CHOUCAS project
5.3.2. Specific acquisition components
5.4. Taking imperfection in spatialization into account
5.4.1. Relationship between CLZ and PLZ and the construction of the PLZ
5.4.2. The process of creating CLZs
5.4.3. Taking imprecision into account
5.4.4. Taking uncertainty and incompleteness into account
5.5. Restoring uncertainty in the interface
5.5.1. "Classic" solution
5.5.2. Solution based on figures of varying sizes
5.5.3. Solution by combining representations
5.5.4. Illustration of the Grand Veymont Alert
5.6. Conclusion and perspectives
5.7. References
Chapter 6. Uncertainties Related to Real Estate Price Estimation Scales
6.1. Introduction
6.2. The effect of spatial support in real estate price estimation
6.2.1. The generation of uncertainty in the choice of aggregation scales
6.2.2. Real estate price representation scales rarely questioned
6.2.3. Different scales of real estate price structuring
6.3. Data and indicators for estimating the sensitivity of house prices to the scale of aggregation
6.3.1. Different territorial grids to test the effects of aggregation
6.3.2. A national database of geolocalized real estate transactions in France: DVF
6.3.3. Representation of aggregated statistics in the form of scalograms
6.4. Methodology for studying variations in real estate price estimates according to scale
6.4.1. Preliminary methodological considerations
6.4.2. A random sample generator to eliminate scale uncertainty
6.4.3. Presentation of analysis elements in the form of a composite graph
6.5. Results: highlighting structural effects linked to territorial units and scale salience
6.5.1. 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
6.5.2. Analysis of changes in standard deviation estimates of average prices for apartments and houses in the Provence Alpes Côte d'Azur region from 2014 to 2020
6.5.3. Cross-sectional analysis on composite graphs
6.6. Conclusion and discussion
6.7. References
Chapter 7. Representing Urban Space for the Visually Impaired
7.1. Introduction
7.2. Landmarks as tools for moving around and finding your location
7.2.1. Gathering needs and uses
7.2.2. Modeling landmarks and their uses
7.2.3. Model expressiveness
7.3. Integration of landmarks in tactile and multimodal maps
7.3.1. Background map
7.3.2. Integration of landmarks
7.4. Integrating uncertainty into text descriptions
7.4.1. Integrating the probability of detecting landmarks
7.4.2. Integrating blurred distances and angles
7.5. Conclusion and perspectives
7.6. Acknowledgments
7.7. References
List of Authors
Index
EULA

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