- UCAS course code
- UCAS institution code
BASS Politics and Criminology
Year of entry: 2022
- View tabs
- View full page
Course unit details:
Crime Mapping: an introduction to GIS and spatial analysis
|Unit level||Level 3|
|Teaching period(s)||Semester 2|
|Offered by||School of Social Sciences|
|Available as a free choice unit?||No|
The course provides a theoretically-contextualised and practically-oriented introduction to the use of geographic information systems for crime analysis and research using R and R Studio. It combines the study of a subject area (crime and place) with the development of spatial visualisation and analysis skills. The course will be of interest to students with a particular interest in learning GIS for the study of a variety of social or public health phenomena. The course responds to current calls from ESRC and the British Academy to improve the quantitative skills of social science graduates and fits within the Q-Step Manchester initiative.
Indicative content: (1) Introduction to the course; (2) Producing your first crime map; (3) Working with spatial data; (4) Thematic maps; (5) Mapping crime ‘hot spots’; (6) Hot spots in context; (7) Global/local spatial autocorrelation; (8) Regression & challenges of autocorrelation; (9) Basic spatial regression models; (10) Course review.
The course assumes the student has already taken an introductory data analysis course using appropriate software such as SPSS, STATA or R such as Modelling Criminological Data, Making Sense of Criminological Data, or Data Analysis with R and R Studio, or the equivalent in other Departments across SOSS. In case of doubt about whether you meet this criteria do not hesitate to contact the Course Unit Director.
The unit aims to (1) Enhance students' understanding of criminological theory in context with particular forms of violence; (2) Develop students' awareness of the links between approaches to research, theory construction and policy surrounding violence; (3) Explore the complex relationships between power, inequality and violence, drawing upon examples such as ethnicity and gender; (4) Examine knowledge and understanding of various forms of violence through critical discussion of empirical research and theory.
On completion of the course, the student will be able to (1) Identify main research traditions in the study of crime and place; (2) Recognise key concepts on spatial data visualisation and analysis; (3) Produce maps of crime and other social features in a professional manner; (4) Carry out exploratory spatial data analysis of both points and area data; (5) Produce hot spots maps using various approaches; (6) Model spatial area data using regression.
Teaching and learning methods
Teaching in academic year 21/22 will be flexible and allow us to adapt to changing conditions, however, the common intention across units is to provide a blended offer of the best in online and on-campus teaching that includes: (1) media, activities and other learning material that should be engaged with before scheduled teaching; (2) a practical computing lab; (3) a weekly discussion session; (4) weekly opportunity for 1:1 support.
Further details: In the Discussion Sessions, we will introduce the most substantive part of the course. These sessions will try to provide the context for what we are doing (the research and theory on the geography of crime) but also try to reinforce some of the methodological concepts that you will have the chance to apply in the labs. In the Practical Lab Sessions you will work interactively with a PC or your own laptop and carry out a set of designated exercises to consolidate your understanding of GIS and spatial analysis.
- (i) analyse, critique and (re-)formulate a problem or issue; (ii) rapidly and thoroughly review/rate argument and evidence from targeted bibliographic searches; (iii) plan, structure and present arguments in a variety of written formats and to a strict word limit, (iv) express ideas verbally and organise work effectively in small teams for a variety of written and oral tasks; (v) obtain, manipulate and (re-)present different forms of data; (vi) manage time effectively; (vii) reflect on and improve performance through feedback.
The course is assessed by means of homework (20%) and a learning portfolio (80%). You will submit 8 pieces of homework. Mostly the homework activities will ask you to submit the maps that you will typically have the time to complete during the lab sessions. We mark timely submission rather than quality of the output. Then you will need to submit a learning portfolio (3000 words). As part of it, you will have to attach a selection of maps and analysis.
Formative feedback (both individual and collective) will be given on tasks and contribution in class. Summative feedback will be given on both assessed components via Blackboard (Grademark).
We won’t require you to purchase a textbook for this course unit. Instead we will rely on reading material that is available for free or that can be obtained from the library in digital format. The main required text we will be following is: ‘Crime Mapping and Analysis using R: a Practical Introduction’ by Reka Solymosi and Juanjo Medina, available online.
|Scheduled activity hours|
|Independent study hours|
|Reka Solymosi||Unit coordinator|
Across their course units each semester, full-time students are expected to devote a ‘working week’ of 35-40 hours to study. Accordingly each course unit demands 12-13 hours of study per week consisting of (i) timetabled teacher-led hours, (ii) preparation, required and further reading.
Students who have studied Modelling Criminological Data, Making Sense of Criminological Data, or Data Analysis with R and R Studio, or the equivalent in other Departments across SOSS such as The Survey Method in Social Research SoST20012. In case of doubt about whether you meet this criteria do not hesitate to contact the course leader.
Resticted to: Final year students University wide who have met the pre-requisites.
This course is available to incoming study abroad students if they are able to demonstrate sufficient quantitative training ideally R software to engage successfully with the course.