Supervision

Bachelor & Master thesis topics

1.     Perceived versus actual unsafe areas and the link to people’s activity spaces.
2.     Social Media and Location Privacy: what can tweets reveal?
3.     Data protection of health geodata and its impact on spatial analysis.
4.     k-Voronoi Masking: a method to protect confidential discrete spatial data.
5.     Short Vs Long term spatial crime forecasting using autoregressive models.
6.     Exploring Space-Time Autoregressive models for the prediction of future crime hotspots.
7.     Introducing spatial dependence in machine learning models.

1.Perceived versus actual unsafe areas and the link to people’s activity spaces.

Objective: To examine whether spatial patterns of daily routes are associated with the gap between perceived and actual unsafe areas.

Keywords: Spatial statistics, crime analysis, perception gaps, population at risk

Acquire, develop, and apply knowledge on:

·       Spatial Groupings
·       Point Pattern Analysis
·       Logistic Regression
·       Statistical Testing

2. Social Media and Location Privacy: what can tweets reveal?

Objective: To develop an algorithm that assesses inference attacks from semantic place-based signatures compared to inference attacks from spatial signatures; both derived from LBSN data. The motivation and goal of this topic is to raise awareness of the hidden risks to individual privacy by the extensive use of LBSNs.  

Keywords: inference attacks, location privacy, semantic signatures, location-based social networks

Acquire, develop, and apply knowledge on:

·       Python or R programming
·       Processing of LBSN data
·       Location inference algorithms
·       Geo-localisation algorithms
·       Data quality

3. Data protection of health geodata and its impact on spatial analysis.

Objective: To investigate different strategies for privacy protection of health geodata and asses their effects when performing spatial and statistical analysis.

Keywords: data protection, location privacy, health geography, spatial analysis, spatial error

Acquire, develop, and apply knowledge on:

·       Data protection strategies
·       Literature on health geography
·       Anonymization techniques
·       Spatial statistical analysis

4. k-Voronoi Masking: a method to protect confidential discrete spatial data.

Objective: To develop a spatial algorithm that anonymizes confidential discrete spatial data by mitigating the re-identification risk and the confidence to identify.

Keywords: location privacy, geoprivacy, geographical masking, k-anonymity, spatial error

Acquire, develop, and apply knowledge on:

·       Python and ArcPy package
·       K-anonymity algorithms
·       Spatial statistical analysis

5. Short Vs Long term spatial crime forecasting using autoregressive models.

Objective: To evaluate the accuracy of short-term versus long-term forecasting of crime in space when applying autoregressive models.

Keywords: predictive policing, forecasting, spatio-temporal statistics, autoregressive models

Acquire, develop, and apply knowledge on:

·       Predictive policing
·       Spatial regressive models
·       Autoregressive models
·       Space-time regressive models
·       R Programming

6. Exploring Space-Time Autoregressive models for the prediction of future crime hotspots.

Objective: To test and evaluate the ability of space-time autoregressive models to forecast crime hotspots.

Keywords: predictive policing, forecasting, spatio-temporal statistics, autoregressive models

Acquire, develop, and apply knowledge on:

·       Predictive policing
·       Spatial regressive models
·       Autoregressive models
·       Space-time regressive models
·       R Programming

7. Introducing spatial dependence in machine learning models.

Objective: To develop a geographical modelling approach that accounts for spatial autocorrelation by introducing spatial features to traditional machine learning models.

Keywords: geographical modelling, machine learning, predictive analytics, spatial autocorrelation, spatial dependence.

Acquire, develop, and apply knowledge on:

·       Spatial Statistics
·       Supervised learning
·       Validation and performance evaluation
·       Python programming