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Fangli Ying,Peter Mooney,Padraig Corcoran,Adam C.Winstanley
Department of Computer ScienceNational University of Ireland, Maynooth
Applying the Shape Complexity Measures to Spatial Data AnalysisApplying the Shape Complexity Applying the Shape Complexity
Measures to Spatial Data AnalysisMeasures to Spatial Data Analysis
Personal Details
Contact Info:
Department of Computer Science,
National University of Ireland,Maynooth
Co.Kildare,Ireland
Email:[email protected]
Commenced PhD project- Since October 2009
Project supervisors: Peter Mooney ,Padraig Corcoran
Director of Study: Adam C.Winstanley
Papers
Polygon Processing in OpenStreetMap XML, GISRUK conference 2010
Using Shape Complexity to guide simplification of geospatial data for use in Location-based Services (Submitted to The7th International Symposium on LBS & TeleCartography 2010)
Using Java XML Tool to Process OSM Data (Abstract), State of the Map 2010 Spain
At present, we plan to submit a paper to ACM-GIS 2010…
OverviewOverview
Introduction to the OSM data & Processing Model
Extension of this work-- selective transmission for LBS based on shape complexity & representation
How to calculate if a polygon in OSM should be simplified
References & Related workReferences & Related work
� Measuring the Complexity of Polygonal Objects --Thomas Brinkhoff
� Convexity Rule for Shape Decomposition Based on Discrete Contour Evolution --Longin Jan Latecki and Rolf Lak¨amper
� Progressive Vector Transmission --Michela Bertolotto Max J. Egenhofer
� Location-based algorithms for finding sets of corresponding objects over several geo-spatial data sets--ELIYAHU SAFRA etc.
� Extended Hausdorff distance for spatial objects in GIS --D. MIN, L. ZHILIN and C. XIAOYONG
� Choosing the scale and extent of maps for navigation with mobile computing systems --Julie Dillemuth
� And more……
Example of Simplification
� For the small Screen of mobile device
� Lower detail and less points is enough
�But is with higher performance
RELATED PROJECT:“Topoligical consistent generalisation of OpenStreetMap”Padraig Corcoran
OpenStreetMap---------a free geographic data
OpenStreetMapOpenStreetMap------------------a free geographic dataa free geographic data
� A free editable map of the world
� collected by OSM volunteers
� GPS trails , Paper Maps or Map tracing (WMS)
OSM DATA:
� OSM data is made publicly available for download
� In OSM XML format
� This OSM XML can be processed using XML tools or
� Storage in Database or A GIS System
OpenStretMapOpenStretMapOpenStretMap
XMLXMLXML
UTMUTMUTM
OpenStreetMap---------a free geographic data
OpenStreetMapOpenStreetMap------------------a free geographic dataa free geographic data
Data Extraction: download OSM data for LBS device
Many limitations in Location-based Service device� —limited storage space, lack of GPS capable component, poor network speed
Our Software
� ---Select the OSM data in a small area and Process Data by Stream:
Using OSM 6.0 API:
OpenSource XML Proceesing
Software Tool For Polygon Examination
Automated Extraction of connected lines and polygons from OpenStreetMap data
The problem --with the XML representing the OSM
� 1.Large geographical area� 2.Large number of lines and polygons.
The original topic:�Extract CONNECTED lines and polygons from OSM XML automatically�Check if (Water feature) polygons are correctly connected
Case Study: spatial connectivity feature in OSM Dataspatial connectivity feature in OSM Data
NOT JOINED
Reality-Lakes are joined
Apparently connected
But NOT in data
Connected?
Spatial Measurement and Query
PostGIS Database function:Check the spatial intersects listQuery the connected polygons
Our Connection algorithms for Real time processing :---- Based on common points and minimum distance
1. Checking if the points of one polygon are inside of another polygon2. Checking the Hausdorff distance between polygons3. Looking at some special features like lake or forest etc.
Complexity Measurement
� Circularity
� Area Ratio (convex hull)
� (Perimeter Ratio)
� (Notch Ratio)
Points Representation Measurement
� K-mean
� Number of Points
Shape Characteristics Examination
Kmean
With BIG turning angle and lengths of edges between nodes
the mean of KS--(Kmean) is High
With SMALL turning angle and lengths of edges between nodes
the Kmean is Low
Related Work(1)Related Work(1)
Ref1. “Measuring the Complexity of Polygonal Objects”� What does “complex” mean?� Using a basic set of parameters to describe a polygon and a set of
intuitive lingual properties.
� It presents a complexity model consisting of some quantitative parameters
Ref2. “Convexity Rule for Shape Decomposition Based on Discrete Contour Evolution”
� How significant the vertex are , based on turning angle measurement
�
Survey to establish if a polygon was “Complex” or “Simple”
• 5 participants (2 with GIS experience, 1 IT, 2 non-IT)
• Provided with a computer display of 70 OpenStreetMap polygons from Ireland
• Asked to indicate whether they thought a given polygon had a “SIMPLE” shape or a “COMPLEX” shape
• Majority vote assigned to corresponding polygon
The results of the visual survey revealed two distinct clusters --------based on Area Ratio and Circularitybased on Area Ratio and Circularity
Small Area-Simple:with large number of points
Ireland N = 188, Area= 0.89Km2
Ireland N = 73, Area = 3.8Km2
Large area-Complex:with small number of points
Iceland N = 31, Area = 50Km2
V.Large area-Simple-V.small number of points
Using Complexity score and kmeanto guide simplification
Kmean=0.05800859
N=77
(C,A):
Circularity=0.861512427
Area Ratio=0.011622088
Simple
Simplify
(Over-represented)
Using Complexity score and kmeanto guide simplification
Kmean=0.413741977
N=46
(C,A):
Circularity=0.226001057
Area Ratio=0.345190904
Simple
NOT Simplify
(Under-represented)
Using Complexity score and kmeanto guide simplification
Kmean=0.468329285
N=51
(C,A):
Circularity=0.092120599
Area Ratio=0.724879782
Complex
NOT Simplify
(Under-represented)
Using Complexity score and kmeanto guide simplification
Kmean<0.01
N=647
(C,A):
Circularity=0.049611775
Area Ratio=0.515551784
Complex
Simplify
(Over-represented)
Nearest neighbour for Kmean
Unknown Polygon
Bayes Classification
1
2
3
4
5
6
7
0.05 0.10 0.15 0.20 0.25 0.30
More More significant significant
VertexVertex
More More unsignificantunsignificant
VertexVertex
Complex
Simple
Complex polygons Kmean distribution
Simple polygons Kmean distribution
� Mean and sigma for kmeanof complex and simple polygons
� Estimate the possibility of the kmean for classification
NP
Kmean
Results from Iceland Dataset (50 Polygons)
Disagreement Amongst Classifiers:Complex (1) and Simple (1)
Results from Denmark Dataset(70 Polygons)
Disagreement Amongst Classifiers:Complex (0) and Simple (2)
Results from Ireland Dataset(64 Polygons)
Disagreement Amongst Classifiers:Complex (0) and Simple (1)
Diagreement – Ireland (Simple) OSM ID = 22728087
Bayesian = SimplifyNN = Don't Simplify
Diagreement – Iceland (Complex) OSM ID = 26373032
Bayesian = Don't SimplifyNN = Simplify
Overall we found only a weak linkage between shape complexity and the need to simplify the shape
Human participants – COMPLEXITY was related to visua l aspects of the shape
Suggestion to simplify – based on the shape structure –kmeans,
Comparison: representation of natural features in OSM and OSI Data
Ordnance Survey Data – Made available by OSI to the STRAT-AG project
OSMOrdnance Survey
NO OSM Representation
Under representationby OSM data?
Try to establish the degree of under/over representation in OSM polygons
OSMOrdnance Survey
Discussion of Applications
� A environmental-aware LBS device should aim to inform users about their relevant surroundings.
� E.g. When used for a moving LBS device in the outdoors, our application should draw the map with large geographic information in a limited screen dynamically
� Provide the user with coarser versions of the data before downloading a complete geographic data
� So which part should be delivered as a priority and then progressively displayed?
Extended work: Selective Progressive Transmission ---Based on Shape Complexity
Related Work(2)Related Work(2)
Ref3. ”Progressive Vector Transmission”� describe a model for multiple representations of maps that can be transmitted progressively
Ref4. ”Location-based algorithms for finding sets of corresponding objects over several geo-spatial data sets”
� Using a join algorithm for finding sets of corresponding
objects, When integrating several geo-spatial data sets.
Related Work(3)Related Work(3)
Ref5. “Extended Hausdorff distance for spatial objects in GIS”
� measuring the dispersion and the central tendency of the distance distribution between spatial objects.
� Using the computation of the median Hausdorff distance for structural similarity measure
Ref6. “Choosing the scale and extent of maps for navigation with mobile computing systems”
� Using time-scale-bars and isochrones as potential methods of spatiotemporal information delivery to users of personal navigation systems.
Future work
� This research investigated polygon representation and simplification in OSM at a 1:1 scale
� Future work (1) – look at complexity and representation of OSM polygons at different scales
� Future work (2) – look at groups of polygons and lines in the same geographical area