Applying the Shape Complexity Measures to Spatial …fying/AP/PhD_seminar_Fangli_Ying_2010.pdfUsing Java XML Tool to Process OSM Data (Abstract), ... Overview Introduction to the OSM

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)


Kmean=0.413741977

N=46

(C,A):



Simple

NOT Simplify

(Under-represented)


Kmean=0.468329285

N=51

(C,A):



Complex

NOT Simplify

(Under-represented)


Kmean<0.01

N=647

(C,A):



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)


Results from Ireland Dataset(64 Polygons)


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


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.


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

Documents

Applying the Shape Complexity Measures to Spatial …fying/AP/PhD_seminar_Fangli_Ying_2010.pdfUsing Java XML Tool to Process OSM Data (Abstract), ... Overview Introduction to the OSM