Cécile Duchêne

Methodology for evaluating automated map generalization in commercial software

This paper presents a methodology developed for a study to evaluate the state of the art of automated map generalization in commercial software without applying any customization. The objectives of this study are to learn more about generic and specific requirements for automated map generalization, to show possibilities and limitations of commercial generalization software, and to identify areas for further research. The methodology had to consider all types of heterogeneity to guarantee independent testing and evaluation of available generalization solutions. The paper presents the two main steps of the methodology. The first step is the analysis of map requirements for automated generalization, which consisted of sourcing representative test cases, defining map specifications in generalization constraints, harmonizing constraints across the test cases, and analyzing the types of constraints that were defined. The second step of the methodology is the evaluation of generalized outputs. In this step, three evaluation methods were integrated to balance between human and machine evaluation and to expose possible inconsistencies. In the discussion the applied methodology is evaluated and areas for further research are identified.

CollaGen: Collaboration between automatic cartographic Generalisation Processes

Cartographic generalisation seeks to summarise geographical information to produce legible maps at smaller scales. Past research led to the development of many automated cartographic generalisation processes, each one being more or less specialised to a particular problem: a landscape like urban areas, a data theme like land use, a cartographic conflict like linear symbol overlap or most of the time of mix of the three. This paper deals with the development of a model allowing collaborative generalisation i.e. the collaboration between automatic processes like these in order to tackle the generalisation of a complete map. CollaGen, our proposed model, allows to partition data in geographic spaces and to find to best suited process to generalise each space. The applications of a process on a space are automatically orchestrated. Interoperability between processes is managed thanks to formal constraints and side effects are monitored after each process application. Results from CollaGen prototype are shown and discussed.

Abstracting Geographic Information in a Data Rich World

Changing contexts and new challenges.- Modelling user requirements.- Modelling geographic relationships in automated environments.- Data structures and continuous generalisation.- Integrating Multi source data and user generated content.- Generalisation operators.- Process modelling, web services and geoprocessing.- Object and field generalisation.- Evaluation and usability of map generalisation outputs.- Generalisation in the context of schematic maps / maps on mobile devices.- Generalisation in practice.

Representation of Interactions in a Multi-Level Multi-Agent Model for Cartography Constraint Solving

The objective of cartographic generalisation is to simplify geographic data in order to create legible maps when scale decreases. It often requires to reason at different levels of abstraction (e.g. a building, a city). To automate this process, Multi-Agent approaches have been used for several years. Map objects (e.g. buildings) are modelled as autonomous entities that try to solve constraints through appropriate transformations. Yet, those approaches are not able to deal with all situations that appear between cartographic objects in a map. Indeed, though a map intrinsically involves objects that belong to several description, scale or organisation levels, there is no explicit multi-level representation in agent-based cartographic models. Thus we assume that the use of a multi-level multi-agent model would improve the automated generalisation process. Especially, the PADAWAN model is a multi-agent model offering multi-level capabilities which meet quite well the requirements for the multi-level organisation of cartographic objects. In this paper, we expose how we use this model on the one hand, to reify multi-level relations between cartographic agents, and on the other hand, to represent the constraints and the actions proposed to solve them, as interactions between the agents.

DIOGEN, a multi-level oriented model for cartographic generalization

ABSTRACT Among approaches for automated generalization of vector data, we focus on the multi-agent paradigm: cartographic objects are modeled as agents (autonomous objects) that apply generalization algorithms to themselves to satisfy cartographic constraints. Several agent levels are considered, for example, individual agents, such as a building, and agents representing a group of agents, such as an urban block composed of the surrounding roads and contained buildings. Several multi-agent models were proposed to automate the orchestration of map generalization processes. Existing multi-agent generalization models have different approaches to manage the relations between agent levels. In this paper, we unify existing models, adapting a multi-level simulation model, to simplify interactions between agents in different levels. We propose the DIOGEN model, in which the principle of interactions between agents of different levels is adapted to constraint-driven cartographic generalization. DIOGEN unifies three existing multi-agent generalization models (AGENT, CartACom and GAEL), combine their behaviors and take advantage of their skills. Our proposal is evaluated on different use cases: instances of topographic mapping, and mapping of hiking routes over topographic data as an example of thematic mapping.

Routes visualization: Automated placement of multiple route symbols along a physical network infrastructure

This paper tackles the representation of routes carried by a physical network infrastructure on a map. In particular, the paper examines the case where each route is represented by a separate colored linear symbol offset from the physical network segments and from other routes—as on public transit maps with bus routes offset from roads. In this study, the objective is to automate the placement of such route symbols while maximizing their legibility, especially at junctions. The problem is modeled as a constraint optimiza- tion problem. Legibility criteria are identified and formalized as constraints to optimize, while focusing on the case of hiking routes in a physical network composed of roads and pedestrian paths. Two solving methods are tested, based on backtracking and simulated annealing meta-heuristics respectively. Encouraging results obtained on real data are pre- sented and discussed.

Multi-agent Multi-level Cartographic Generalisation in CartAGen

The objective of cartographic generalisation is to simplify geographic data in order to create legible maps when scale decreases. This demonstration presents the implementation of a work in progress, aiming at defining a multi-agent, multi-level solution for generalisation. The demonstration introduces the basics of cartographic generalisation and shows some aspects of the model currently being developed, including parameterisation and detailed execution of some interactions, as well as results.

A comparative study of existing multi-scale maps: what content at which scale?

GIScience 2016 Short Paper Proceedings A comparative study of existing multi-scale maps: what content at which scale? Marion Dumont 1 , Guillaume Touya 1 , Cecile Duchene 1 Laboratoire COGIT, IGN, 73 avenue de Paris, 94165 Saint-Mande Cedex, France Email: {marion.dumont; guillaume.touya; cecile.duchene}@ign.fr Abstract This paper presents a comparative study of existing topographic multi-scale maps, regarding relations between display scale and level of abstraction (LoA) of the map content. The general trends in zoom levels distribution across scale and the original patterns in transitions between LoAs are especially highlighted. 1. Objectives Multi-scale maps are displayed in mapping applications, i.e. websites where a multi-scale navigation in topographic maps is available. Each producer chooses the display scale and the map content for each zoom level. When users zoom in or out, they actually change the displayed zoom level in the multi-scale map. In some multi-scale maps, the difference of content between two consecutive zoom levels can be strong, partly due to the change of scale. Mackaness (2007) explains that map scale also relates to a level of abstraction (LoA) of the map. It represents the amount of complexity of the map content: which geographic phenomena are represented, and with how much detail? Due to these changes, we believe that general users may have difficulties to recognize the depicted location or the different representations of a same object across zoom levels. Figure 1. Zoom levels of this multi-scale map (IGN France) present large differences To build knowledge from multi-scale maps specifications, we study sixteen existing multi-scale maps, provided by national mapping agencies, private companies or collaborative communities. In this paper, we study the correlation between zoom levels, display scale and level of abstraction of the map content, in general (section 2), then focusing on a particular geographic theme: the settlement areas (section 3). 2. How Zoom Levels, Display Scale and Map Content Are Related? To compare the distribution of zoom levels across scale between multi-scale maps, we first need to define and measure the scale of each zoom level. Besides, most national mapping agencies build their multi-scale map from their topographic paper map series, where each map is designed for a specific printing scale. This map can then be displayed at one or more