290096 PS Crime GIS (2020W)
Continuous assessment of course work
Labels
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from Tu 06.10.2020 12:00 to Sa 31.10.2020 23:59
- Deregistration possible until Th 12.11.2020 23:59
Details
max. 20 participants
Language: German
Lecturers
Classes
INFO (01.11.2020): die LV findet ab November ausschließlich digital statt
DO, 12.11.2020, 14:00-16:00 Uhr, DigitalMO, 16.11.2020, 14:00-16:00 Uhr, GIS-Lab Computerkartographie Geographie NIG 1.OG C109
DO, 19.11.2020, 14:00-16:00 Uhr, Digital
MO, 23.11.2020, 14:00-16:00 Uhr, GIS-Lab Computerkartographie Geographie NIG 1.OG C109
DO, 26.11.2020, 14:00-16:00 Uhr, Digital
MO, 30.11.2020, 14:00-16:00 Uhr, Digital
DO, 03.12.2020, 14:00-16:00 Uhr, Digital
MO, 07.12.2020, 14:00-16:00 Uhr, GIS-Lab Computerkartographie Geographie NIG 1.OG C109
DO, 10.12.2020, 14:00-16:00 Uhr, Digital
MO, 14.12.2020, 14:00-16:00 Uhr, GIS-Lab Computerkartographie Geographie NIG 1.OG C109
DO, 17.12.2020, 14:00-16:00 Uhr, Digital
Information
Aims, contents and method of the course
Assessment and permitted materials
Four group exercises on (1) crime hot spot mapping, (2) criminal geographic profiling, (3) criminal predictive analytics, and (4) geospatial privacy; presentations of results of all group exercises; regular attendance and class participation.
Minimum requirements and assessment criteria
The course grade comprises of four group exercises and presentations of the results from each group exercise. All four exercises and presentations count for 80% (20% per exercise). Regular attendance and class discussions count for the remaining 20% of the grade. Final grades will be assigned using the following scale: Very good (1) = 90 - 100; Good (2) = 80 - 89.99; Satisfactory (3) = 70 - 79.99; Sufficient (4) = 60 - 69.99; Not sufficient (5) = below 60;
Examination topics
Reading list
The following list of research articles will be made available and discussed in class.Andresen, M. A. and W. Tong (2012) The Impact of the 2010 Winter Olympic Games on Crime in Vancouver. Canadian Journal of Criminology and Criminal Justice, 54 (3), 333-361.
Anselin, L. (1995) Local Indicators of Spatial Association—LISA. Geographical Analysis, 27, 93–115.
Armstrong, M., Rushton, G., and D. L. Zimmerman (1999) Geographically Masking Health data to Preserve Confidentiality. Statistics in Medicine, 18, 497-525.
Brantingham, P. J. and P. L. Brantingham (eds.) (1981) Environmental Criminology. Beverly Hills, US and London, UK: Sage Publications, 264 pages.
Caplan, J. M., Kennedy, L. W., and E. L. Piza (2013) Risk Terrain Modeling Diagnostics Utility User Manual (Version 1.0). Newark, NJ: Rutgers Center on Public Security, 41 pages.
Eck, J., Chainey, S.P., Cameron, J., Leitner, M., and R. Wilson (2005) Mapping Crime: Understanding Hotspots. Washington DC: National Institute of Justice.
Kent, J., Leitner, M. and A. Curtis (2006) Evaluating the Usefulness of Functional Distance Measures When Calibrating Journey-To-Crime Distance Decay Algorithms. Computers, Environment and Urban Systems, 30 (2), 181-200.
LeBeau, J. L. and M. Leitner (2011) Progress in Research on the Geography of Crime. The Professional Geographer, 63 (2), 161-173.
Leitner, M., Kent, J., Oldfield, I., and E. Swoope (2007) Geoforensic Analysis Revisited - The Application of Newton’s Geographic Profiling Method to Serial Burglaries in London, UK. Police Practice and Research: An International Journal, 8 (4), 359-370.
Levine, N. (2015) CrimeStat: A spatial statistics program for the analysis of crime incident locations (v 4.02). Ned Levine & Associates, Houston, TX & the National Institute of Justice, Washington, DC.
Perry, W. L., McInnis, B., Price, C. C., Smith, S. C., and J. S. Hollywood (2013) Predictive Policing. The Role of Crime Forecasting in Law Enforcement Operations. Santa Monica, CA: RAND Corporation, 186 pages.
Strelnikova, D., Schneider, T, and M. Leitner (2018) Utilizing Spatial Video to Analyse Roadside Advertisements in Villach, Austria. GI_Forum 2018, 1, 34-46.
Anselin, L. (1995) Local Indicators of Spatial Association—LISA. Geographical Analysis, 27, 93–115.
Armstrong, M., Rushton, G., and D. L. Zimmerman (1999) Geographically Masking Health data to Preserve Confidentiality. Statistics in Medicine, 18, 497-525.
Brantingham, P. J. and P. L. Brantingham (eds.) (1981) Environmental Criminology. Beverly Hills, US and London, UK: Sage Publications, 264 pages.
Caplan, J. M., Kennedy, L. W., and E. L. Piza (2013) Risk Terrain Modeling Diagnostics Utility User Manual (Version 1.0). Newark, NJ: Rutgers Center on Public Security, 41 pages.
Eck, J., Chainey, S.P., Cameron, J., Leitner, M., and R. Wilson (2005) Mapping Crime: Understanding Hotspots. Washington DC: National Institute of Justice.
Kent, J., Leitner, M. and A. Curtis (2006) Evaluating the Usefulness of Functional Distance Measures When Calibrating Journey-To-Crime Distance Decay Algorithms. Computers, Environment and Urban Systems, 30 (2), 181-200.
LeBeau, J. L. and M. Leitner (2011) Progress in Research on the Geography of Crime. The Professional Geographer, 63 (2), 161-173.
Leitner, M., Kent, J., Oldfield, I., and E. Swoope (2007) Geoforensic Analysis Revisited - The Application of Newton’s Geographic Profiling Method to Serial Burglaries in London, UK. Police Practice and Research: An International Journal, 8 (4), 359-370.
Levine, N. (2015) CrimeStat: A spatial statistics program for the analysis of crime incident locations (v 4.02). Ned Levine & Associates, Houston, TX & the National Institute of Justice, Washington, DC.
Perry, W. L., McInnis, B., Price, C. C., Smith, S. C., and J. S. Hollywood (2013) Predictive Policing. The Role of Crime Forecasting in Law Enforcement Operations. Santa Monica, CA: RAND Corporation, 186 pages.
Strelnikova, D., Schneider, T, and M. Leitner (2018) Utilizing Spatial Video to Analyse Roadside Advertisements in Villach, Austria. GI_Forum 2018, 1, 34-46.
Association in the course directory
(MK2-c-PI)
Last modified: Th 07.01.2021 12:10
The overall goal of this class is to teach students the main GIS research and application areas in the geography of crime utilizing GIS. This includes the discussion of the integration, management, and the common utilization of spatial databases from different public agencies; the development and application of analytical and modeling methods for the identification of spatial, temporal, and spatiotemporal crime hot spots; investigations into the spatial relationship between crime locations and socio-economic characteristics of the population and physical and structural attributes of a city; predicting future trends of spatiotemporal crime locations; and possible reasons for the spatial displacement of crime locations due to natural disasters or construction activities in the urban environment.
Most recent research questions deal with the continued development and evaluation of criminal geographic profiling models with the goal to better predict the anchor point of a serial offender. Additional ongoing research questions deal with the legal aspects of using and publishing personal and confidential data that can be represented as points on maps (geospatial privacy), such as the street address of crime victims, which are collected in the process of an investigation; or the consequences of laws which determine that sex offenders cannot live or stay within certain distances of public places or buildings, including, for example, kindergartens, playgrounds, and schools. In some US cities, these laws have led to sex offenders’ inability to find a legal home or to cluster in a small area of such cities.
This class will foremost discuss theoretical, conceptual, and methodological topics relevant to spatial crime analysis. Afterwards, methods will be tested with available crime data in a practical context using different GIS and spatial statistical software packages. Prior knowledge of GIS and spatial statistics is helpful but not necessary.