16238 Property Data Visualisation and Analytics
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Subject handbook information prior to 2025 is available in the Archives.
Credit points: 6 cp
Subject level:
Undergraduate
Result type: Grade and marksThere are course requisites for this subject. See access conditions.
Description
This is an elective subject open to all interested students who have completed the necessary prerequisites, and is of particular use for students in their second year of the Bachelor of Property Economics (C10310). The subject covers core property data visualisation and visual interaction tools that are used to support property market research and property visual analytics. It also provides an essential understanding of the different visualisation techniques that may be used when dealing with different forms of property data. Students find that both data visualisation and data presentation skills are highly useful in their future studies and careers, particularly in the research and decision-making process. Students also benefit from practical projects in both individual and group settings.
Subject learning objectives (SLOs)
On successful completion of this subject, students should be able to:
| 1. | Demonstrate understanding of property data visualization and visual analytic technologies |
|---|---|
| 2. | Use data visualization methods to represent and navigate large property information spaces |
| 3. | Critique and evaluate different data visualization approaches |
| 4. | Apply multi-dimensional visualization techniques throughout the data analytical process |
| 5. | Design and implement efficient visual interaction and visual analytic technologies |
Contribution to the development of graduate attributes
The term CAPRI is used for the five Design, Architecture and Building faculty graduate attribute categories where:
C = communication and groupwork
A = attitudes and values
P = practical and professional
R = reseach and critique
I = innovation and creativity
Course intended learning outcomes (CILOs) are linked to these categories using codes (e.g. C-!, A-3, P-4, etc).
Teaching and learning strategies
This subject combines a traditional teaching approach with an interactive project-based learning approach. Lectures will present the theoretical aspects of data visualization and visual analytics, including topics, that set out in the 'Content' section of the Subject Outline. The core learning component of the subject is through hands-on experience in real data projects. Throughout the subject, students will be asked to create and develop visualizaion analytics tools and programs in order to make the property project viable and successful. By implementing data visualization techniques in several popular visualization tools, students will also enhance their analytical capabilities and presentation skills through the assignments. These practical assignments are spaced throughout the semester with the necessary software to complete. The bulk of the students’ interactive learning experiences will come from attending lectures, class workshops, assignment-based projects, group discussions, and self-directed reading.
The lecturer provides feedback during the lectures, in the class or through the UTS Canvas notice board or email. It is the student's responsibility to record any feedback given during the classes. Students are provided with individual feedback on Assignment 1, 2 and 3, marks are posted Canvas, and overall results are communicated to students. Assignment 1 and 2 will be reviewed in detail to provide students with the opportunity to reflect on their own work and to ask questions. Students provide feedback to each other on their group assessments.
Content (topics)
- Introduction to data visual analytics and Big Data visualization
- Visual representations of data, information and knowledge
- Introduction to property data: source, form, collection, characteristics
- Textual data visualization: applications in property
- Low-dimensional data visualization: applications in property
- Real-time data visualization: applications in property
- High-dimensional data visualization: applications in property
- Visual analytics and data mining
- Behavior-driven data visualization: applications in property
- Combining visualization and visual analytics techniques
Assessment
Assessment task 1: Data Collection and Visual Analytics
| Intent: | This assignment is an individual assignment. Students are required to collect and analyze property data for a particular suburb or city. | ||||||||||||||||||||||||
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| Objective(s): | This task addresses the following subject learning objectives: 1, 2, 3 and 5 This task also addresses the following course intended learning outcomes that are linked with a code to indicate one of the five CAPRI graduate attribute categories (e.g. C.1, A.3, P.4, etc.): .1, .3 and .7 | ||||||||||||||||||||||||
| Type: | Report | ||||||||||||||||||||||||
| Groupwork: | Individual | ||||||||||||||||||||||||
| Weight: | 30% | ||||||||||||||||||||||||
| Criteria: | Students are required to select an appropriate property data source and data type, and then collect the necessary dataset. Students should select an appropriate data source for their chosen suburb/city, and then collect any relevant economic and property data for that suburb/city. Note that although there is no limitation on the data source and data type that can be collected, the data must be related to the suburb/city’s property economics and built environment contexts. Please note that data from the chosen suburb/city will also be relevant for the group assignment as outlined below. Lectures will be recorded and students will be able to choose from a shortlist of potential suburbs/cities. Students are also required to implement a range of data visualisation techniques that they have learnt throughout the course to summarize and analyse the data they have collected. In this step, each student must select the particular visualisation technique most relevant to their dataset, based on its data type, the attributes and characteristics of the dataset, and the desired form of visual representation. Students should be aware that the viewing scheme and interaction scheme are key concepts in this step. | ||||||||||||||||||||||||
| Criteria linkages: |
SLOs: subject learning objectives CILOs: course intended learning outcomes |
Assessment task 2: Sample Data Visualisation
| Intent: | This assignment aims to enable students' ability to analyse and visualise the sample data provided in the question, which has been selected from the Australian Bureau of Statistics. | ||||||||||||||||
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| Objective(s): | This task addresses the following subject learning objectives: 1, 3 and 5 This task also addresses the following course intended learning outcomes that are linked with a code to indicate one of the five CAPRI graduate attribute categories (e.g. C.1, A.3, P.4, etc.): .7 | ||||||||||||||||
| Type: | Exercises | ||||||||||||||||
| Groupwork: | Individual | ||||||||||||||||
| Weight: | 40% | ||||||||||||||||
| Criteria: | This is an individual assessment. Each student should be able to summarize key concepts and trends by deploying essential visualisation techniques that they have learnt throughout the course so far. Students are also expected to provide commentary on the real-world causes and implications of these trends. Students must also describe the advantages and weaknesses of the visualisation approaches they have used, with focus on property data-specific advantages and disadvantages. Students must cite all data sources and references used. | ||||||||||||||||
| Criteria linkages: |
SLOs: subject learning objectives CILOs: course intended learning outcomes |
Assessment task 3: Data Visualization and Knowledge Discovery
| Intent: | This is a group assignment for groups of between FIVE and SIX students. The group assignment is designed to combine and further develop Assignment One by introducing mixed data type and multiple data sources into the visual analytics project. The groups should be formed so that students are able to collect data from Assignment One that spans at least five different suburbs/cities (as denoted by name and postcode). To clarify, the group’s combined data from assignment one must include at least five different suburb/cities, as denoted by their name and postcode. It is the responsibility of the entire team to ensure that the criteria are satisfied. No leeway will be given to groups who are unable to achieve these criteria. This group assignment is in three steps. The first step is for team members to combine their data from Assignment One. As noted above, the combined data must span at least five different suburbs/cities. Students must then implement a range of different visualization and interaction techniques that they have learned throughout the course to summarize and analyze the data they have collected. In particular, visual analytics, pattern comparison, and knowledge discovery will be highly useful in this step in order to highlight and explore new concepts for comparing and measuring property markets across different suburbs/cities. Often, targeted and intelligent data analytics and visualization techniques will be able to uncover hidden trends and insights that may not be apparent through normal data analytics. The second step requires students to write a less 5,000-word report outlining their insights into the property market in their chosen suburbs/cities. Although groups are encouraged to explore as many ideas and aspects as possible in detail, the following points are particularly relevant. The third step will involve a group presentation in the last week of trimester. | ||||||||||||||||
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| Objective(s): | This task addresses the following subject learning objectives: 2, 4 and 5 This task also addresses the following course intended learning outcomes that are linked with a code to indicate one of the five CAPRI graduate attribute categories (e.g. C.1, A.3, P.4, etc.): .2, .3 and .7 | ||||||||||||||||
| Type: | Report | ||||||||||||||||
| Groupwork: | Group, group assessed | ||||||||||||||||
| Weight: | 30% | ||||||||||||||||
| Criteria linkages: |
SLOs: subject learning objectives CILOs: course intended learning outcomes |
Minimum requirements
The DAB attendance policy requires students to attend no less than 80% of formal teaching sessions (lectures and tutorials) for each class they are enrolled in to remain eligible for assessment.
Recommended texts
1. Sue, Valerie M (2016), Data visualization & presentation with Microsoft Office
Thousand Oaks, California: SAGE Publications, Inc., UTS library code number: 005.5 SUEV
2. Simon, Phil (2014), The visual organization: data visualization, big data, and the quest for better decisions Hoboken, New Jersey : John Wiley and Sons, Inc., UTS library code number: 658.4038 SIMO
3. Australian Bureau of Statistics
Other resources
Students should regularly consult UTS Canvas under this subject for additional references and readings.