Professor Hendrik Speck - Crimeblips - Web Based Framework for Crime Incident Analysis and Visualization.

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Information about Professor Hendrik Speck - Crimeblips - Web Based Framework for Crime...
Technology

Published on December 7, 2008

Author: hendrikspeck

Source: slideshare.net

Description

Professor Hendrik Speck, Crimeblips, crime statistics, Kriminalstatistik, Polizei, Berlin, mapping, visualizing, map, analysis, visualization, hot spots, trends, incident patters, police department, law enforcement, data mining, GIS, indexing, linguistic processing, clustering, filtering, web based services, graphical user interface, University of Applied Sciences Kaiserslautern

Crimeblips - Web Based Framework for Crime Incident Analysis and Visualization. Stefan Kovachev, Patrick Reichert, and Hendrik Speck University of Applied Sciences Kaiserslautern 10 th International Conference on Information Integration and Web-based Applications & Services Emerging Research Projects, Applications and Services Symposium Linz, Austria November 24 - 26, 2008

1. Introduction 2. Analysis 3. Visualization 4. Application 5. Conclusion Introduction

Crime Mapping Framework Neighborhoods of Berlin (Germany) Analyzes, Maps, and Visualizes Incident Patterns, Hot Spots, and Trends Press Releases of Berlin Police Department. Third Party Data and Statistics Introduction Crimeblips.

Analysis

1. Crawling and Indexing 2. Parsing and Pattern Matching 3. Date/Time 4. Location 5. Categorization 6. Statistics and Analysis 7. Mapping and Visualization 8. Interaction Interface Analysis Structural Anatomy

Analysis Berlin Police Department. Press Release Overview. Source: Berlin Police Department. Press Releases. June 28, 2008, Available: http://www.berlin.de/polizei/presse-fahndung/presse.html

Analysis Example. Press Release Source: Berlin Police Department. Press Release. No. 103842. XML Feed. June 28, 2008, Available: http://www.berlin.de/polizei/presse-fahndung/_rss_presse.xml

Analysis Example. Press Release Pressemeldung. Eingabe: 15.10.2008 - 11:55 Uhr. Laute Musik führte zu Cannabisplantage. Treptow-Köpenick. # 3175 Eine Cannabisplantage entdeckten Polizeibeamte in der vergangenen Nacht in Oberschöneweide, nachdem sie wegen ruhestörenden Lärms gerufen worden waren. Mieter eines Hauses in der Parsevalstraße hatten gegen 21 Uhr 30 die Polizei alarmiert, da sie sich durch laute Musik gestört fühlten. … Bei der Überprüfung des 20-jährigen Wohnungsinhabers stellte sich zudem heraus, dass er per Haftbefehl gesucht wurde. ...

Analysis Example. Press Release. Date , Time Category Borough , ID Category Time , Borough Location Time , Category Category Pressemeldung. Eingabe: 15.10.2008 - 11:55 Uhr . Laute Musik führte zu Cannabisplantage . Treptow-Köpenick. # 3175 Eine Cannabisplantage entdeckten Polizeibeamte in der vergangenen Nacht in Oberschöneweide , nachdem sie wegen ruhestörenden Lärms gerufen worden waren. Mieter eines Hauses in der Parsevalstraße hatten gegen 21 Uhr 30 die Polizei alarmiert, da sie sich durch laute Musik gestört fühlten. … Bei der Überprüfung des 20-jährigen Wohnungsinhabers stellte sich zudem heraus, dass er per Haftbefehl gesucht wurde. ...

Location Analysis Structural Anatomy Date and Time Crime Type Publication Date Time / Period Index Crime Categories Initial Keyword Set Police Press Releases Location Directory Police Press Releases Bayesian Algorithm Term Tables Weighted Term Set Categorization Term Matching Address Cases/Order IP2LL Translation Mapping

Analysis Database Tables. District DistrictStats SubDistrict Third Party Street LastDate PressItem Incident 1 1 * 1 * 1 *

CREATE TABLE tCrime( id_crime INT(8) NOT NULL AUTO_INCREMENT, title VARCHAR(100) NOT NULL, url VARCHAR(200), cdate TIMESTAMP NOT NULL, full_address VARCHAR(200) NOT NULL, ctype VARCHAR(64) NOT NULL, content VARCHAR(2000), id_subdistrict INT(8) NOT NULL, clat VARCHAR(64) NOT NULL, clong VARCHAR(64) NOT NULL, PRIMARY KEY (id_crime), FOREIGN KEY (id_subdistrict) REFERENCES tSubDistrict(id_subdistrict) ON DELETE CASCADE) Analysis Data Table. Crime.

Visualization and Interface

Visualization and Interface Objectives 1. Implementation of Spatial Turn 2. Visualization of Individual Incidents 3. Identification of Crime Hot Spots 4. Large Data Sets / Clustering 5. User Interaction, Selection, and Detail 6. Development over Time 7. Implementation of Filter Algorithms

 

Visualization and Interface Evolution of Icons and Meanings. Data Layers/Clustering Standard Pin Arson Theft Drugs Violence Murder Arrest Vandalism Diverse Different Cluster Sizes Scale and Clustering / More than one Incident

Visualization and Interface Ranges, Filter, and Selection Type of Crime Borough Date and Period On/Off

Social Networks Evolution of Icons and Meadnings. Data Layers. 5. Crime Ticker Crime Markers Zoom Information 3. Charts Map Control 10. Statistical Overview 11. Map Type 7. Date Range Filter District Filter Crime Type Filter

5. Crime Ticker

Crime Markers

Zoom

Information

3. Charts

Map Control

7. Date Range Filter

District Filter

Crime Type Filter

Application and Screenshots

 

 

 

 

 

 

Conclusion

Conclusion Limitations and Implications 1. Data Sources 2. Crimes Reported/Not Reported 3. Press as Target Audience 4. Inherent Bias Workload/Filter 5. Structural and Political Implications 6. Statistical Mismatch 7. Population Crime Relationship 8. Lack of Standards and Metrics 9. Gap btw. Data and Understanding

Conclusion Outlook and Future Developments 1. Generic Framework (Local/ National/ Global) 2. Additional Sources (Emergencies, Fire, Construction, Environment, Education, Social and Culture, Quality of Life) 3. Add News, Wikipedia and Panoramio 4. Add Prevention and Advisories 5. Associations and Relationships 6. Tag Cloud, Semantics, Ontology, and NLP 7. Personalization, Participation / User Experience / Satisfaction 8. Game and Playing

1. Crimeblips. Prototype. http://crimeblips.informatik.fh-kl.de/ 2. Crimeblips. Sourceforge. Code and CVS. http://ccrime.sourceforge.net/ 3. Berlin Police Department http://www.berlin.de/polizei/presse-fahndung/index.html 4. Luisenstädtischer Bildungsverein e.V. http://www.luise-berlin.de/Strassen/indexstr.htm 5. Google Maps http://maps.google.com/ Conclusion References. Berlin Prototype.

1. State Police. Landespolizei Oberösterreich. http://www.bundespolizei.gv.at/ooe/ 2. Federal Ministry of the Interior (Austria) . Annual Crime Report. http://www.bmi.gv.at/kriminalstatistik/ 3. Federal Ministry of the Interior (Austria) . Crime Prevention. http://www.bmi.gv.at/praevention/ 4. Linz City Archive. Street Names. http://www.linz.at/strassennamen/ 5. Google Maps. http://maps.google.com/ Conclusion References. Linz.

End

Thank you for your attention. I will gladly answer your questions. Prof. Hendrik Speck contact (at) hendrikspeck [dot] com University of Applied Sciences Kaiserslautern Information Architecture Lab Amerikastrasse 1 66482 Zweibrücken Tel: +49 6332 914 360 Skype: hendrikspeck Conclusion Contact Information

License Information. You are free to share (to copy, distribute and transmit the work) and to remix (to adapt the work) under the following conditions: Attribution. (You must attribute the work in the manner specified by the author or licensor but not in any way that suggests that they endorse you or your use of the work) Share Alike. (If you alter, transform, or build upon this work, you may distribute the resulting work only under the same, similar or a compatible license.) Conclusion Attribution-ShareAlike 3.0 Unported. License Information.

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