Crime Mapping: an introduction to GIS and spatial analysis

CRIM31152 Crime Mapping: an introduction to GIS and spatial analysis 20 Semester 2 Undergraduate Level 3 Reka Solymosi Unit coordinator 1 FHEQ level (Framework for Higher Education Qualifications) ' Last part of a Bachelors ' European Credit Transfer & Accumulation System Rating : 10.0 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: <ol><li>Introduction to the course; </li><li>Producing your first crime map; </li><li>Working with spatial data; </li><li>Thematic maps; </li><li>Mapping crime 'hot spots'; </li><li>Hot spots in context; </li><li>Global/local spatial autocorrelation; </li><li>Regression & challenges of autocorrelation; </li><li>Basic spatial regression models; </li><li>Course review. </li></ol> 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: <ol><li>Introduction to the course; </li><li>Producing your first crime map; </li><li>Working with spatial data; </li><li>Thematic maps; </li><li>Mapping crime 'hot spots'; </li><li>Hot spots in context; </li><li>Global/local spatial autocorrelation; </li><li>Regression & challenges of autocorrelation; </li><li>Basic spatial regression models; </li><li>Course review. </li></ol> The unit aims to:<ol><li style="margin-left:5.7pt;">Enhance students' understanding of criminological theory in context with particular forms of violence; </li><li style="margin-left:5.7pt;">Develop students' awareness of the links between approaches to research, theory construction and policy surrounding violence; </li><li style="margin-left:5.7pt;">Explore the complex relationships between power, inequality and violence, drawing upon examples such as ethnicity and gender; </li><li style="margin-left:5.7pt;">Examine knowledge and understanding of various forms of violence through critical discussion of empirical research and theory. </li></ol> On completion of the course, the student will be able to: <ol><li style="margin-left:5.7pt;">Identify main research traditions in the study of crime and place; </li><li style="margin-left:5.7pt;">Recognise key concepts on spatial data visualisation and analysis; </li><li style="margin-left:5.7pt;">Produce maps of crime and other social features in a professional manner; </li><li style="margin-left:5.7pt;">Carry out exploratory spatial data analysis of both points and area data; </li><li style="margin-left:5.7pt;">Produce hot spots maps using various approaches; </li><li style="margin-left:5.7pt;">Model spatial area data using regression. </li></ol> Other Spatial data analysis skills: - Produce professional, data-driven visualisations and maps that effectively communicate spatial insights; - Interpret and model spatial patterns, including crime hot spots and area data trends, to inform strategic decision-making; - Integrate statistical methods with spatial analytics to solve complex real-world problems; - "Manage and manipulate large spatial datasets to deliver actionable, evidence-based insights.", Teaching and learning across course units consists of:  <ol><li>Preparatory work to be completed prior to teaching sessions, including readings, pre-recorded subject material and online activities; </li><li>A weekly whole-class lecture or workshop; </li><li>Tutorials;</li><li>One-to-one support via subject specific office hours.   </li></ol> 0 Other 20% 5 Portfolio 80% The course is assessed by means of homework (20%) and a learning portfolio (80%). You will have weekly homework tasks, which will be assessed by online quizzes designed to consolidate your practical and theoretical learning.  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). 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 the School of Social Sciences. In case of doubt about whether you meet this criteria do not hesitate to contact the Course Unit Director. N 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. Lectures 20 Tutorials 10 0 70 Across their course units each semester, full-time students are expected to devote a 'working week' of around 30-35 hours to study. Accordingly each course unit demands around 10-11 hours of study per week consisting of (i) 3 timetabled teacher-led hours, (ii) 7-8 independent study hours devoted to preparation, required and further reading, and note taking.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.Restricted 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.