Isovist Analyst 2.0
Upcoming SlideShare
Loading in...5
×
 

Isovist Analyst 2.0

on

  • 1,243 views

Presentation by Sanjay Rana from Blom Group on Esri European User Conference 2011.

Presentation by Sanjay Rana from Blom Group on Esri European User Conference 2011.

Statistics

Views

Total Views
1,243
Views on SlideShare
1,237
Embed Views
6

Actions

Likes
0
Downloads
23
Comments
1

2 Embeds 6

http://www.docseek.net 4
http://www.linkedin.com 2

Accessibility

Categories

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel

11 of 1

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
  • many thanks
    http://georeseau.net/technology/3-recherche-personnel/19-isovist-program-and-analyst.html
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

    Isovist Analyst 2.0 Isovist Analyst 2.0 Presentation Transcript

    • Isovist Analyst 2.0An ArcMap 10 Add-In for Urban ViewshedAnalyses : Visual SurveillanceSanjay RanaIdeas and Improvement Group @ BLOM (UK)
    • Topics Brief Overview of Urban Viewshed Analysis and its application in Visual Surveillance Isovist Analyst 2.0 Visual Surveillance Planning and Evaluation using Isovist Analyst 2.02/ 25
    • Urban Viewshed or Isovist3/ 25
    • Urban Viewshed Or Isovist Definition An urban viewshed or isovist is (typically a 2D) area visible from a location in an indoor or outdoor urban environment. Purpose Evaluating “Visual” Accessibility of Open Spaces is vital for various applications e.g. visual surveillance, evacuation, crime pattern analyses, pedestrian wayfinding, and so on. Example of an Isovist Tate Britain Gallery, London, UK4/ 25
    • Urban Viewshed Or Isovist Brief History Proposed by Tandy (1967) and popularised by Benedikt (1979) for studies in architecture, urban design, urban planning, transport planning. Research on this theme now come under the field of “Space Syntax”. Relevant Wikipedia Entries: isovist space syntax visibility graph analysis space network analysis software5/ 25
    • Urban Viewshed Or Isovist Properties of Isovists Geometrical measures Shortest/Longest line of sights Area Shape Ratios r d Topological measures Clustering Coefficient Control6/ 25
    • Urban Viewshed Or Isovist Dense Mesh of 4298 Isovist Area Observers Tate Gallery, London High Maximum Circularity Diametric Length Low7/ 25
    • Urban Viewshed Or Isovist Some Urban Viewshed Scenarios in Homeland Security: Visual Surveillance Wayfinding in Evacuation Discarded Needles in City of Montreal8/ 25
    • Visual Surveillance9/ 25
    • Visual Surveillance Two types of visual surveillance Passive or Natural Visual Surveillance Design of urban open spaces so as to ensure that occupants can self-regulate themselves and reduce the fear of crime. Cheap, long lasting but involves a risk. Active or Artificial Visual Surveillance Direct and intrusive remote monitoring via devices such as CCTV or human observers. Potentially more effective but costly.10/ 25
    • Passive Visual Surveillance11/ 25
    • Passive Visual Surveillance Passive Visual Surveillance The aim is to create the perception of being observed or feeling safe by suitable architectural design of an open space: Occupants can see other occupants and entry/exist points in space, so that they can self-regulate themselves, e.g., in public spaces, Occupants always feel being watched by an invisible observer. Measures include removal of blind spots, dark alleys, installing one-way screens (Venetian blinds) etc.12/ 25
    • Passive Visual Surveillance Passive Visual Surveillance Perception of safety and well-being in an open space can be correlated to the visibility properties of the open space for instance, poorly lit (e.g., by natural light) areas generate a fear of crime. People follow cues (e.g., long corridors) in the space while exploring open spaces. Isovist measures are correlated to empirical data on perception of crime and people behaviour to design new urban open spaces.13/ 25
    • Passive Visual Surveillance Natural Visual Surveillance Greene, M., and Greene, R., 2004, Urban safety in residential areas, Space Syntax Conference.14/ 25
    • Passive Visual Surveillance Famous Example of Passive Visual Surveillance: The Panopticon Prison Design by Jeremy Bentham A central guard tower, surrounded by prison cells but the prisoners can neither see each other nor the prison guards therefore creating the perception of being under constant invisible observation. Examples include Twin Towers Correctional Facility (Los Angeles) and Pentonville Prison (England).15/ 25
    • Active Visual Surveillance16/ 25
    • Active Visual Surveillance Active Visual Surveillance The use of visual surveillance has dramatically increased in recent years, as demands arose for monitoring, Criminal behaviour in public space, Traffic, etc. etc. Several thousand millions of pounds are spent on sales and rental of CCTV and other types of visual surveillance.17/ 25
    • Active Visual Surveillance CCTV Cameras in Manhattan18/ 25
    • Active Visual Surveillance Active Visual Surveillance The aim is to ensure complete/maximum visual coverage of an open space. It involves an iterative, manual and gut-feel process of trying various layouts until a satisfactory solution has been found. Traditionally done by architects, security consultants and urban planners using CAD software.19/ 25
    • Active Visual Surveillance Example Of Active Visual Surveillance Identification of the Four Suspects In London Transport Network Bombings (July, 2005)20/ 25
    • Active Visual Surveillance Active Visual Surveillance Challenges: Costs of installation and maintenance, and coverage of CCTV cameras need to be addressed. The problem of ensuring maximum visual coverage with a minimum number of CCTV/observers is broadly similar to Location-Allocation, Chinese Postman/Travelling Salesman problems in GIS, and most importantly closely similar to the Art Gallery Problem in Computer Science.21/ 25
    • Art Gallery Problem22/ 25
    • Active Visual Surveillance Art Gallery Problem (AGP) Proposed by Victor Klee in 1973. What’s the minimum number of guards necessary to provide complete visual coverage of an art gallery? Solution of the AGP is going to be relevant for an affordable and effective CCTV network. However, there are no robust algorithms that guarantee a general purpose solution. Mathematically, the problem is NP-hard. Thus, it is analytically and computationally non-trivial and unsolvable at present therefore approximate solutions are used.23/ 25
    • Active Visual Surveillance Art Gallery Problem Most existing algorithms assume a certain topology of the art gallery e.g., rectilinear, convex. A well known formula to solve the AGP is by Chv’atal (1975), For a gallery with n walls and h holes, at most (n+h/3) number of guards are sufficient to provide complete visual coverage, This solution doesn’t guarantee the minimum number in all shapes but definitely yields a number enough for complete visual coverage e.g.24/ 25
    • How Many Guards Are Sufficient to Cover the Gallery ? 71 Walls + 0 Holes (n+h)/3 = 23? 925/ 25
    • Rank and Overlap Elimination (ROPE)26/ 25
    • Active Visual Surveillance Rank and Overlap Elimination (ROPE) For Solving AGP It is essentially a greedy-search method. The open space is discretised with a dense mesh of potential observers. Each observer is assigned a rank based on the number of other observers seen by it. Higher rank means higher visibility. Starting with the highest ranking observer (observer with largest isovist area), an iterative process selects the optimal observers27/ 25
    • Active Visual Surveillance START Pick highest ranking observer remaining in potential observers ROPE Selection Remove all potential observers Process Flowchart visible to highest ranking observer Yes Potential observers left in open space ? No STOP28/ 25
    • Active Visual Surveillance Dense Mesh of Isovist of the highest Two optimal Potential Observers ranking observer observers29/ 25
    • Active Visual Surveillance ROPE technique doesn’t guarantee the minimum number of observers in all shapes. Optimal Observers ROPE Observers But it’s not worse than the analytical solution (Chv’atal, 1975): (10 walls + 0 holes) / 3 = 3 guards30/ 25
    • Isovist Analyst 2.031/ 25
    • Isovist Analyst 2.0 ArcMap add-in for computing isovist and over 20 isovist properties. 1.x versions released about a decade ago as extensions to ArcView 3.x (Screen shot): Still the only such application embedded inside ESRI suite of GIS. Used worldwide for research and teaching purposes. Voted Second Best User Software Application at 2007 ESRI International User Conference. Open space can be of arbitrary topology (i.e., with/out holes) and geometry (i.e., lines, polygons). Key achievement is the integration of urban visibility analyses in GIS.32/ 25
    • Isovist Analyst 2.0 How it works Technique of Ray Tracing is used to cast rays (line of sights) at a user-defined angular interval into the open space from a viewpoint. Nearest points of ray intersection with obstacles are connected together to form the isovist polygon. Requirements Point Feature Layers as Observers/Viewpoints. Polyline or Polygon Feature Layers as Obstacles.33/ 25
    • Isovist Analyst 2.0 Software Components: Combination of superior cartographic visualisation and data interactivity of ArcMap 10 with light-weight and easy to code Open Source geospatial libraries. ArcObjects NetTopologySuite, MapTools, GeoAPI34/ 25
    • Isovist Analyst 2.0 Demonstration35/ 25
    • Isovist Analyst 2.0 Demonstrations: Tate Gallery Isovist Data Used: Tate Gallery Outline. Tate Gallery Demo A (very) crude evaluation of CCTV Coverage of Norwich City Centre, UK. Data Used: Blom3D Level of Data 1 (LOD 1) Building Footprint polygons as Obstacles Blom WebViewer for visualising surveillance context e.g. Landuse36/ 25
    • Planning and Evaluating Visual Surveillance using IA 2.0 Disclaimer: Solely to demonstrate the application context and doesn’t in any way indicate any actual issue being studied. Norwich is an important city in East of England. City was basically chosen because CCTV and Crime data was found on the internet, and BLOM contains good coverage of the city in aerial photographs and vector data Norwich37/ 25
    • Planning and Evaluating Visual Surveillance using IA 2.0 Current distribution of CCTV in Norwich City Centre. Source: http://sites.google.com/site/norwichpolicecctv/, last accessed 23rd October 2011.38/ 25
    • Planning and Evaluating Visual Surveillance using IA 2.0 Where are the 46 Sites after a 6400 CCTVs and 717 for full Potentialsearch greedy 66% street crimes? coverage CCTV uncovered Locations 7288 Norwich City Centre Street Crimes Dec 2010 - Aug 2011 Anti-social behaviour Burglary Other crime Robbery Vehicle crime Violent crime39/ 25
    • Conclusions Conclusions and Future Directions This work demonstrates Visual Surveillance has become a necessary aspect of modern society, Planning of Visual Surveillance is essentially a geospatial exercise. Most GISs do not provide in-built functions for planning visual surveillance. Automated algorithms for evaluating the structure of open spaces and planning visual coverage, in relation to planning visual surveillance can be easily developed in a GIS.40/ 25
    • Disclaimer and Credits All views and work expressed are solely my own. Blom has no responsibility for any errors or opinions. Images: http://www.chelmsford.gov.uk/media/image/m/d/Monitor_wall_%2 8o%29_large.jpg http://urbandesign.tfl.gov.uk/Design-Guidance/London- Rail/Crossrail/Underground-Rail-%281%29/Routes- %281%29.aspx BLOM for access to building footprints and Blom WebViewer data www. isee.com for the CCTV coverage of Manhattan image. Conroy and Dalton (1999) for the Tate Gallery outline. www.police.uk for crime data41/ 25
    • Further Information Sanjay Rana sanjay.rana@blomasa.com Thank you!42/ 25
    • 43/ 25
    • Isovist Analyst 1.x44/ 25