scholarly journals POSSIBILITY AND CONSTRAINT PARAMETERS FOR AUTOMATED GENERALIZATION OF MULTI – SCALE BASE MAPS

2018 ◽  
Vol 54 (4B) ◽  
pp. 154
Author(s):  
Nguyen Thi Hong Hanh
Keyword(s):  
Ho Chi Minh City ◽  
Map Generalization ◽  
Process Map ◽  
Thematic Map ◽  
Geographic Scope ◽  
Multi Scale ◽  
Cartographic Generalization ◽  
Map Scale ◽  
Map Data ◽  
Automated Map Generalization

Map generalization plays an important role in map-making process. Map generalization depends on many factors such as purpose, geographic scope, thematic, map scale… and therefore it remains one of the challenging issues in Cartography. This paper discusses the constraint parameters and process of cartographic generalization from map data at 1:2000 to map data at smaller scale such as 1:5.000, 1:10.000, 1:25.000, 1:50.000 and 1:100.000. These constraint parameters are indentified by applying cartographic principles and using experimental procedures with generalization toolset in ArcGIS 10. Defined constraint parameters are then put in cartographic generalization for base maps of Ho Chi Minh City in order to verify their rationalities.Research result shows that, using right parameters and GIS cartographic generalization tools could provide good way to generalize and create base map products fast, efficient and reasonable. It also provides useful information for further research in automated map generalization and in creating multi-scale, multi-purpose map database.

scholarly journals A Multi-Scale Representation of Point-of-Interest (POI) Features in Indoor Map Visualization

2020 ◽  
Vol 9 (4) ◽  
pp. 239 ◽  
Author(s):  
Yi Xiao ◽  
Tinghua Ai ◽  
Min Yang ◽  
Xiang Zhang
Keyword(s):  
Age Groups ◽  
Adaptive Strategy ◽  
Adaptive Method ◽  
Building Structure ◽  
Multi Scale ◽  
Performance Requirements ◽  
Service Information ◽  
Developing Area ◽  
Map Scale ◽  
Functional Partitioning

As a result of the increasing popularity of indoor activities, many facilities and services are provided inside buildings; hence, there is a need to visualize points-of-interest (POIs) that can describe these indoor service facilities on indoor maps. Over the last few years, indoor mapping has been a rapidly developing area, with the emergence of many forms of indoor representation. In the design of indoor map applications, cartographical methodologies such as generalization and symbolization can make important contributions. In this study, a self-adaptive method is applied for the design of a multi-scale and personalized indoor map. Based on methods of map generalization and multi-scale representation, we adopt a scale-adaptive strategy to visualize the building structure and POI data on indoor maps. At smaller map scales, the general floor distribution and functional partitioning of each floor are represented, while the POI data are visualized by simple symbols. At larger map scales, the detailed room distribution is displayed, and the service information of the POIs is described by detailed symbols. Different strategies are used for the generalization of the background building structure and the foreground POI data to ensure that both can satisfy real-time performance requirements. In addition, for better personalization, different POI data, symbols or color schemes are shown to users in different age groups, with different genders or with different purposes for using the map. Because this indoor map is adaptive to both the scale and the user, each map scale can provide different map users with decision support from different perspectives.

Thematic-Map Generalization

1964 ◽  
Vol 54 (1) ◽  
pp. 13 ◽  
Author(s):  
O. M. Miller ◽  
Robert J. Voskuil
Keyword(s):  
Map Generalization ◽  
Thematic Map

On Scale Correction Model of Coastline and its Application for Coastline Remote Sensing Monitoring

2013 ◽  
Vol 303-306 ◽  
pp. 734-739
Author(s):  
Hua Guo Zhang ◽  
Dong Ling Li ◽  
Ai Qin Shi
Keyword(s):  
Remote Sensing ◽  
High Precision ◽  
Coastal Zone ◽  
Absolute Error ◽  
Correction Method ◽  
Remote Sensing Images ◽  
Correction Model ◽  
Remote Sensing Monitoring ◽  
Multi Scale ◽  
Map Scale

This paper focuses on the scale correction of coastline extracted from remote sensing images. Measurement of coastline is one of the basic and core work of coastal zone remote sensing monitoring projects. Based on analysis of coastline scale effect and multi-scale simulation of coastline, a scale correction method is presented for remote sensing coastline. This method can be used to correct remote sensing coastline to specified map scale, in order to obtain high-precision remote sensing monitoring results of coastline. The results of application example showed that the absolute error of coastline length is reduced to about one third of the original error after correction using the presented method. So the presented method can increase the accuracy of remote sensing coastline for specified scale substantially.

scholarly journals Types of Topographic Map Generalization: The Example of the 1: 50 000 Map

2004 ◽  
Vol 11 (1) ◽  
pp. 281-290
Author(s):  
Wiesław Ostrowski
Keyword(s):  
Map Generalization ◽  
Topographic Map ◽  
Cartographic Generalization ◽  
The Creation

Abstract The author first discusses the various ways of classifying cartographic generalization techniques, and then analyzes the course of the generalization process during the creation of a 1:50 000 topographic map on the basis of a 1:10 000 map. Using this analysis as a foundation, the author identifies, discusses and illustrates with examples the five types of cartographic generalization.

scholarly journals The BigBrainWarp toolbox for integration of BigBrain 3D histology with multimodal neuroimaging

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Casey Paquola ◽  
Jessica Royer ◽  
Lindsay B Lewis ◽  
Claude Lepage ◽  
Tristan Glatard ◽  
...  
Keyword(s):  
Best Practices ◽  
Human Brain ◽  
Collaborative Network ◽  
Multimodal Neuroimaging ◽  
Ultrahigh Resolution ◽  
Ongoing Research ◽  
Multi Scale ◽  
Map Data

Neuroimaging stands to benefit from emerging ultrahigh-resolution 3D histological atlases of the human brain; the first of which is 'BigBrain'. Here, we review recent methodological advances for the integration of BigBrain with multi-modal neuroimaging and introduce a toolbox, 'BigBrainWarp', that combines these developments. The aim of BigBrainWarp is to simplify workflows and support the adoption of best practices. This is accomplished with a simple wrapper function that allows users to easily map data between BigBrain and standard MRI spaces. The function automatically pulls specialised transformation procedures, based on ongoing research from a wide collaborative network of researchers. Additionally, the toolbox improves accessibility of histological information through dissemination of ready-to-use cytoarchitectural features. Finally, we demonstrate the utility of BigBrainWarp with three tutorials and discuss the potential of the toolbox to support multi-scale investigations of brain organisation.

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