8A_2_A containment-first search algorithm for higher-order analysis of urban topology
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This document discusses using a graph-based approach to analyze topological relationships in geospatial data. It involves constructing graphs of adjacencies and containments between polygons identified in a triangulated irregular network (TIN) of LiDAR points. Depth-first and breadth-first searches are used to analyze the graph of adjacencies. Further work includes constructing a graph of "touchings" between polygons and using the topological analysis to support applications like land-use mapping and image understanding.
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Summary of survey papers on deep learning method to 3D data
Slide for study session given by Dr. Takashi Nakano (Arithmer inc.) at Arithmer inc.
It is a summary of recent survey papers on deep learning method to 3D data.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
Presentation @SIGIR2015
The “Local Ranking Problem” (LRP) is related to the computation of a centrality-like rank on a local graph, where the scores of the nodes could significantly differ from the ones computed on the global graph. Previous work has studied LRP on the hyperlink graph but never on the BrowseGraph, namely a graph where nodes are webpages and edges are browsing transitions. Recently, this graph has received more and more attention in many different tasks such as ranking, prediction and recommendation. However, a webserver has only the browsing traffic performed on its pages (local BrowseGraph) and, as a consequence, the local computation can lead to estimation errors, which hinders the increasing number of applications in the state of the art. Also, although the divergence between the local and global ranks has been measured, the possibility of estimating such divergence using only local knowledge has been mainly overlooked. These aspects are of great interest for online service providers who want to: (i) gauge their ability to correctly assess the importance of their resources only based on their local knowledge, and (ii) take into account real user browsing fluxes that better capture the actual user interest than the static hyperlink network. We study the LRP problem on a BrowseGraph from a large news provider, considering as subgraphs the aggregations of browsing traces of users coming from different domains. We show that the distance between rankings can be accurately predicted based only on structural information of the local graph, being able to achieve an average rank correlation as high as 0.8.
Geographic Information System unit 1
This document outlines the syllabus for a course on Geographic Information Systems (GIS). It is divided into 5 units that cover fundamentals of GIS, spatial data models, data input and topology, data analysis, and applications of GIS. The objectives of the course are to introduce students to the basic concepts of GIS and provide an understanding of spatial data structures, management processes, and analysis tools.
Fundamentals of GIS
This document outlines the syllabus for a course on Geographic Information Systems (GIS). The course is divided into 5 units that cover fundamentals of GIS, spatial data models, data input and topology, data analysis, and applications of GIS. The objectives are to introduce GIS fundamentals and processes of data management, analysis, and output. Students will learn about spatial data structures, data quality standards, and tools for data input, analysis, and management. The course aims to provide knowledge of GIS concepts and techniques.
SQLBits X SQL Server 2012 Spatial
This document discusses opportunities for using spatial data and analysis. Key points include:
- Increased user sophistication and availability of location-based data and tools enable new integrated systems and applications.
- Spatial data and analysis can be used for real estate development, environmental planning, scientific research, economic analysis and more.
- SQL Server provides robust spatial data types and methods to build high performance spatial solutions and integrate geospatial applications.
Big Data and Geospatial with HPCC Systems
This presentation covers one topic that we have mastered after several years : Geospatial.
We will reveal how we deal with very specific spatial challenges in our day to day use cases :
• Answer questions combining the best of BigData and geospatial analysis.
• Ingestion and use of raster and vector data with our Massive Parallel Processing platform (Thor).
• Store and query spatial information with sub-second queries, using our data refinery (Roxie)
And much more under the umbrella of LexisNexis HPCC Systems (High Performance Computing Cluster), an open source platform for Big Data processing and analytics.
Scattered gis handbook
The document discusses various concepts related to remote sensing and geographic information systems (GIS). It defines key terms like digital and analog images, spatial and spectral resolution, data models, and data types used in GIS. It also describes common data formats, software, techniques for data input and management, and methods for spatial analysis and map making using GIS.
Introduction to GIS systems
GIS systems allow for the input, storage, manipulation, analysis and output of geographic data. Spatial data represents location and attributes provide additional data about features. Data can be represented as vectors using points, lines and polygons, or as rasters in a grid cell format. Key properties of spatial data include projection, scale, accuracy, resolution and how it represents real world features. GIS allows for integrated analysis of spatial and attribute data through functions like classification, measurement, overlay and more.
[GAGIS]A Hierarchical Representation and Computation Scheme of Arbitrary-dime...
This document proposes a multivector tree (MVTree) data structure for representing and computing geographical primitives in geographic information systems (GIS). The MVTree uses an outer product-based representation scheme to express primitives like points, lines, polygons, and polyhedrons in a hierarchical structure. Operations on the MVTree like accessing nodes by child index or level allow for representation and computation of GIS data in a unified manner. The MVTree addresses needs for higher dimensional modeling and a well-defined algebraic system to support GIS data representation and analysis.
Conceptual models of real world geographical phenomena (epm107_2007)
Conceptual models of geographical phenomena abstract and simplify aspects of reality for representation in geographic information systems (GIS). Data can be represented as discrete entities with boundaries or precise attributes, or as continuous fields that vary smoothly over space. GIS uses both vector and raster data structures, with vectors best for objects and topology but rasters more suitable for analysis of continuous surface variables like elevation.
GIS_Intro_March_2014
This document provides an introduction to Geographic Information Systems (GIS) concepts and software. It outlines key learning objectives which are to understand what a GIS is and how it is applied, gain an understanding of basic GIS concepts and vocabulary, and get hands-on experience using GIS software to make basic maps and integrate data from different sources. The document then covers fundamental GIS topics like what GIS is, common questions it can help answer, its software components, vector and raster data models, coordinate systems, cartography principles for map design, and exercises for using GIS software to create maps.
What is Geography Information Systems (GIS)
GIS is a computer-based information system used to capture, manage, update, analyze, display, and output spatial data and information to be used in a decision making context. It integrates hardware, software, data, people, and allows for the visualization and analysis of data with a geographic component. Some key applications of GIS include emergency response, transportation planning, site selection, and natural resource management.
Gis
The document defines GIS as a system for capturing, storing, integrating, manipulating, analyzing and displaying spatially referenced data related to the Earth. It allows for spatial data to be overlaid and analyzed to derive new information and insights. Some key benefits of GIS include better information management, higher quality spatial analysis, ability to model "what if" scenarios, and improved project efficiency through the use of spatial data and tools.
Introduction to Graph neural networks @ Vienna Deep Learning meetup
Graphs are useful data structures that can be used to model various sorts of data: from molecular protein structures to social networks, pandemic spreading models, and visually rich content such as websites & invoices. In the recent few years, graph neural networks have done a huge leap forward. It is a powerful tool that every data scientist should know. In this talk, we will review their basic structure, show some example usages, and explore the existing (python) tools.
gislec1.ppt
This document provides an introduction to geographic information systems (GIS). It defines GIS as a system for storing, retrieving, analyzing and displaying spatially referenced data. Spatial data can be represented in raster format as a grid of cells or in vector format using points, lines and polygons. GIS allows users to input, organize and analyze spatial data layers to support decision making in applications such as site selection, route planning, facilities management and military planning.
GIS Data Types
An introduction to GIS Data Types. Strengths and weaknesses of raster and vector data are discussed. Also covered is the importance of topology. Concludes with a discussion of the vector-based format of OpenStreetMap data.
Graph Theory and Databases
This presentation is an overview of some applications of graph theory that graphdbs has bring into reality.
Similar to 8A_2_A containment-first search algorithm for higher-order analysis of urban topology (20)
Can graph convolution network learn spatial relations ?
Can graph convolution network learn spatial relations ?
Giving MongoDB a Way to Play with the GIS Community
Giving MongoDB a Way to Play with the GIS Community
Summary of survey papers on deep learning method to 3D data
Summary of survey papers on deep learning method to 3D data
[GAGIS]A Hierarchical Representation and Computation Scheme of Arbitrary-dime...
[GAGIS]A Hierarchical Representation and Computation Scheme of Arbitrary-dime...
Conceptual models of real world geographical phenomena (epm107_2007)
Conceptual models of real world geographical phenomena (epm107_2007)
Introduction to Graph neural networks @ Vienna Deep Learning meetup
Introduction to Graph neural networks @ Vienna Deep Learning meetup
More from GISRUK conference
8A_1_To vote or not to vote
This document investigates how changing the location of polling stations can affect voter turnout. It uses a case study of Brent, UK to build binomial machine learning models predicting turnout based on factors like voter density and distance to polling stations. The models show turnout increases by 2-4% if 50-100% of voters are within 500m (European election) or 600m (local election) of stations. Ward-level analyses found relocating stations could increase turnout probabilities by up to 5% depending on election and location. The study supports how geographical accessibility factors influence turnout, especially in lower-salience elections.
7B_3_Matterhorn on the horizon
This document discusses a method for identifying the most salient mountains in images for annotation purposes. It involves analyzing EXIF metadata, computing apparent and prominence salience scores, and using viewshed analysis on digital elevation models. The goal is to annotate images with the most visually prominent mountains, not necessarily the highest summits. The method was tested on mountainous regions in Switzerland with preliminary results showing identification of mountains like the Matterhorn and Monte Cervino. Further work could expand evaluations and consider additional factors like distance, landform shape, and semantic salience from crowdsourced data.
7B_2_Topological consistent generalization of openstreetmap
This document discusses techniques for topologically consistent generalization of OpenStreetMap data for use in location-based services. It examines methods for determining if a simplified map preserves topological properties like planarity without false positives or negatives. The techniques are evaluated based on constraints for different types of topology and simplification. Results show the methods can simplify OpenStreetMap data while maintaining important invariants like shape and size for use on location-based devices. Future work includes simplifying all linear features and progressively transmitting map data.
7A_4_Gps data collection setting for pedestrian activity modelling
This document discusses collecting GPS data for modeling pedestrian activity. It outlines the background, why high frequency data collection is needed for pedestrians, how a test was conducted, analysis of the test results, and conclusions. The test collected walking data every 1 second and then thinned the data to varying rates. The analysis found that positional accuracy was around 15-20 meters, length calculation was most accurate at 20 seconds, speed calculation worked best at 30-60 seconds, and start and end points were most accurate between 10-20 seconds. In conclusion, an epoch rate of 20-30 seconds was determined to be most appropriate for pedestrian route modeling.
5A_3_GIS based spatial modelling for improving the sustainability of aggregat...
This document summarizes a PhD thesis that uses GIS and spatial modeling to analyze the sustainability of aggregate mineral supply in the UK. The research aims to:
1) Build a GIS model to represent consumption, supply, and transport networks of aggregates.
2) Develop a spatial interaction model to simulate aggregate flows via different transport methods.
3) Evaluate environmental and social impacts like carbon footprint to compare policy scenarios for aggregate delivery.
5A_1_Land evaluation techniques comparing fuzzy ahp with ideal point methods
This document compares the Fuzzy Analytic Hierarchy Process (AHP) and Ideal Point methods for land suitability evaluation. Both methods were used to evaluate land characteristics for wheat production, resulting in land suitability maps. The maps were compared and found to have good agreement, with most locations deemed moderately suitable by both methods and few areas found highly or less suitable. While the Ideal Point method showed some bias toward ideal values, both techniques effectively incorporated local knowledge on land characteristics and their relative importance for wheat.
4B_3_Automatically generating keywods for georeferenced imaged
This document discusses automatically generating geographically relevant keywords to describe image content. It presents a process that takes an image's coordinates and extracts potential visible areas from spatial data. Candidate keywords are generated by mapping visible spatial features to concepts and filtering/ranking them based on spatial extent, descriptiveness, and web search popularity. Initial results found ~40% of generated keywords were good descriptors for test images. Lessons learned include directional accuracy issues and benefits/challenges of using open data sources like OpenStreetMap for urban vs. rural areas. National mapping agencies are essential for supplementing rural coverage. The document acknowledges funding support and contributors to the research.
4B_1_How many volunteers does it take to map an area well
This document evaluates the quality of OpenStreetMap (OSM) data compared to Ordnance Survey data. It finds that for major roads, 80% of OSM data falls within a 20m buffer of Ordnance Survey data. For smaller roads, completeness increases with more users per area, but positional accuracy does not clearly increase above 5 users per area. While OSM data quality is high in areas with aerial imagery, Linus' Law about quality increasing with more users does not seem to directly apply to OSM. More research is needed on quality with fewer users and without aerial imagery.
4A_1_Uncertainty in the 2001 output area classification for the census of eng...
1) The document discusses uncertainty in geographic classifications like the UK's Output Area Classification (OAC) system. It notes how few real-world areas perfectly fit the definitions of classification categories.
2) It presents two methods for quantifying this uncertainty - Fuzzy c-Means, which assigns probabilistic membership to multiple classes, and Possibilistic c-Means, which relaxes one of the Fuzzy c-Means constraints.
3) The author applies these methods to Leicester, England and finds that Possibilistic c-Means offers a more satisfying treatment of uncertainty in the OAC system compared to the usual hard classification approach. However, it also has some disadvantages like class areas not summing to
3A_4_Applying network analysis to quantify accessibility to urban greenspace ...
This document outlines a study that aims to quantify accessibility to urban greenspace (UGS) for different socio-economic groups using network analysis. The researchers analyzed greenspace and deprivation data for Leicester, UK to determine how physical access to greenspace varies across socio-economic groups. Preliminary results found that the most deprived groups had significantly less access to greenspace than average or least deprived groups. Further work will involve public participation methods like mapping and interviews to understand how different groups use greenspace.
3A_2_Modelling health-harming behaviours in a socially ranked geographic space
This document discusses using geodemographic classifications to model patterns of health-harming behaviors and social inequalities in health status and lifestyle within a geographic area. It aims to identify these patterns at a local scale to inform health services. The method involves defining domains of health status, lifestyle status, and social status for neighborhoods based on risk of hospitalization, lifestyle proxies, and social/economic/cultural capital measures. Applying this method to Camden, North London reveals a social gradient with neighborhoods having distinct profiles of health and lifestyle status. Limitations include overgeneralization but it offers a way to represent ranked social space and neighborhood differences to support health policy and services.
1A_3_A geodemographic classification of london primary schools
This document summarizes research that classified primary schools in London into 14 clusters based on the socioeconomic characteristics of their pupil populations. The researchers analyzed census and performance data for all maintained primary schools in London to derive variables like free school meal eligibility, English as an additional language status, and mobility rates. They used a k-means clustering algorithm to group schools into 14 relatively homogeneous clusters based on these variables. The classification reveals differences in the profiles and performance of schools in each cluster. The researchers suggest future work could use this classification framework to further analyze relationships between schools and neighborhoods and the impact of mobility on educational attainment.
UK Map Challenge Aidan Slingsby
This document discusses using aerial photographs with interactive legends to better support map interpretation tasks. It aims to enrich raster images with vector data, present feature hierarchies simply, and make the legend more reflective of and supportive for the map. The conclusion is that innovative legend designs for interactive maps have potential to better support map interpretation than static legends.
SP_4 Supporting spatial negotiations in land use planning
The document discusses using multi-criteria analysis (MCA) and negotiation tools to support spatial planning negotiations for land use in the Bodegraven polder region. It outlines how MCA was used to evaluate and rank alternatives, and how a negotiation workshop used a touch table and MCA results on a separate screen to facilitate interactions over exchanging parcels between intensive agriculture and nature uses. The results showed MCA output could support negotiations, the touch table facilitated knowledge exchange, and implicit and explicit knowledge combined successfully with a cooperative attitude.
SP_3 Automatic identification of high streets and classification of urban lan...
This document summarizes research on automatically identifying high streets and classifying urban land use in large scale topographic databases. The research extracts urban boundaries, classifies urban block land use based on building area ratios, identifies commercial or high streets over 250m in length, and concludes with representing land use information at small map scales and enriching databases for automatic map generalization.
9B_1_Trust in web gis a preliminary investigation of the environment agencys ...
This study examined trust perceptions of non-expert users interacting with the Environment Agency's "What's In Your Back Yard" (WIYBY) web GIS application. 10 participants completed tasks and questionnaires about the site. Most tasks failed and users found the site difficult to use, but they still trusted the information despite perceiving a lack of transparency. The results suggest functional attributes like maps may be less important than perceptual attributes in influencing trust of web GIS systems. Future work will test redesigned interfaces to understand how to build trust through design.
9A_2_Automatic classification of retail spaces from a large scale topographc ...
This document discusses classifying different types of retail spaces using various methodologies. It aims to understand the distribution patterns of retail spaces at a national scale. The authors explore using boolean, fuzzy, and Bayesian logic to characterize retail spaces based on measures of urban centrality and accessibility. The Bayesian approach is described as determining the likelihood that an unclassified retail space belongs to a known population, like shopping malls, based on its characteristics. The conclusion suggests the Bayesian method may be best, and discusses broader applications like classifying other urban spaces and using automated techniques with open-source data.
9A_1_On automatic mapping of environmental data using adaptive general regres...
1. The document discusses using an adaptive general regression neural network (GRNN) for automatic mapping of environmental data.
2. A GRNN is a modification of the Nadaraya-Watson nonparametric regressor that can perform nonlinear modeling, feature selection, and characterize uncertainties to produce quality maps.
3. An illustrative case study applies GRNN to precipitation data in Switzerland, showing it can automatically filter out irrelevant variables and produce accurate interpolation maps and uncertainty analyses of residuals.
8B_4_Exploring the usability of geographic information
The document summarizes research into exploring the usability of geographic information through a grounded theory analysis. The research identified key areas of geographic information usability including accessibility, sufficiency, and controllability. Findings were consistent with previous work and identified opportunities for future research such as developing design guidelines and user-centered design tools for geographic information.
8B_2_Using sound to represent uncertainty in address locations
The document discusses research into using sound to represent uncertainty in address location data. The researchers created an ArcGIS extension to represent the positional accuracy of addresses using different piano notes. They evaluated this approach with 49 users through a survey comparing visual and sonic representations. The results showed that users performed better when a higher proportion of the data was represented and with sonic versus solely visual representations. Areas for further research include improving the sounds used and applying this approach to other datasets.
More from GISRUK conference (20)
7B_2_Topological consistent generalization of openstreetmap
7B_2_Topological consistent generalization of openstreetmap
7A_4_Gps data collection setting for pedestrian activity modelling
7A_4_Gps data collection setting for pedestrian activity modelling
5A_3_GIS based spatial modelling for improving the sustainability of aggregat...
5A_3_GIS based spatial modelling for improving the sustainability of aggregat...
5A_1_Land evaluation techniques comparing fuzzy ahp with ideal point methods
5A_1_Land evaluation techniques comparing fuzzy ahp with ideal point methods
4B_3_Automatically generating keywods for georeferenced imaged
4B_3_Automatically generating keywods for georeferenced imaged
4B_1_How many volunteers does it take to map an area well
4B_1_How many volunteers does it take to map an area well
4A_1_Uncertainty in the 2001 output area classification for the census of eng...
4A_1_Uncertainty in the 2001 output area classification for the census of eng...
3A_4_Applying network analysis to quantify accessibility to urban greenspace ...
3A_4_Applying network analysis to quantify accessibility to urban greenspace ...
3A_2_Modelling health-harming behaviours in a socially ranked geographic space
3A_2_Modelling health-harming behaviours in a socially ranked geographic space
1A_3_A geodemographic classification of london primary schools
1A_3_A geodemographic classification of london primary schools
SP_4 Supporting spatial negotiations in land use planning
SP_4 Supporting spatial negotiations in land use planning
SP_3 Automatic identification of high streets and classification of urban lan...
SP_3 Automatic identification of high streets and classification of urban lan...
9B_1_Trust in web gis a preliminary investigation of the environment agencys ...
9B_1_Trust in web gis a preliminary investigation of the environment agencys ...
9A_2_Automatic classification of retail spaces from a large scale topographc ...
9A_2_Automatic classification of retail spaces from a large scale topographc ...
9A_1_On automatic mapping of environmental data using adaptive general regres...
9A_1_On automatic mapping of environmental data using adaptive general regres...
8B_4_Exploring the usability of geographic information
8B_4_Exploring the usability of geographic information
8B_2_Using sound to represent uncertainty in address locations
8B_2_Using sound to represent uncertainty in address locations
8A_2_A containment-first search algorithm for higher-order analysis of urban topology
- 5. Preparation of the polygon data base • Identifying meaningful structures • Understanding the spatial arrangement between them Unstructured data set Retrieving information More meaningful homogeneous regions ( e.g. land-use parcels)
- 6. Test environment: LiDAR data
- 7. First order information TIN based on the Delaunay triangulation, the maximal planar description of the given point set (Kirkpatrick and Radke, 1985)
- 8. TIN facets binary classification ( using 45 ° slope threshold )
- 9. Graph of adjacencies construction
- 10. Depth-first search vs. Breadth-first search 15 graph vertices 6 2 3 12 11 9 7 4 5 8 10 13 15 14 1 6 2 3 12 11 9 7 4 10 15 14 1 8 13 5 Depth - first search Breadth - first search 1 6 2 3 12 11 9 7 4 5 8 10 13 15 14 traversed edges LEGEND 1, 1,…, 6 2 3 12 11 9 7 4 5 10 13 15 14 1 8
- 11. 3
- 13. Problem: containment relationship between rings of “steep” polygons and “flat” polygons 200 Graph vertices Graph edges Polygon arcs Polygon nodes “ Flat” polygons “ Steep” polygons LEGEND 250 256 257 260 200
- 14. Graph of “touchings” between “steep” polygons
- 16. Summary and further work Unstructured data (LiDAR points) TIN Features Understanding process Polygonal regions (x,y,z) coordinates Structures clustering into homogeneous regions Binary classification based on Δ slopes Identification of meaningful structures Cluster shapes delineation, derivation of its characteristics for the identification of higher order structures