A brief communication in Nature on our indigenous cultural heritage and engineering engagement

After an intense half a year, all the background activity led by my colleague Lawrence Molloy finally led to hands-on cultural heritage documentation on Country for the Gunditjmarra traditional owners of Budj Bim in March this year. These three days were of data collection were followed by months of data processing, primarily by our fantastic students that processed the UAV (drone) and UGV (robot) data. Finally, we were able to interpret the landscape quantitatively and support the UNESCO bid with visualizations.

We also managed to put a short communication about the start of this great collaboration to Nature ( https://www.nature.com/articles/d41586-019-02315-y ).

And some eye candy here:

Quick summary of Semester 1, 2019 (apologies for slow updates)

The intense first semester is now over for me. I had a great time, interacting with amazing masters students of Spatial Databases and of Spatial Information Programming, and introducing a new generation of students to GIS in the Applications of GIS course. It was a very rewarding period, and I am sure that many of these students will continue in a spatial career.

I have also engaged in cultural heritage documentation, trying a highly unusual deployment of our Husky robot in complex conditions. While the first data collection trip may not have been as optimal as we hoped, we have learned a lot. This will inform a lot of our current research.

And of course, there was intense research activity going on, with a crop of papers out the door. Ehsan and Haonan are making a great progress exploring place-related question answering with conference papers at AGILE and SEMEVAL’19 [1,2]. Ivan’s paper on association rule mining for automated error detection in spatial data has appeared in JOSIS [6](part of the ARC DP Self-Healing Maps). Rob, Elham, Nick and I have finally managed to publish our investigation of using spatial mixing models to improve metropolitan scale influenza forecasting (that was a looong review period)[4]. Yet, not as long as the one for [3], which is now showing on the journal website, yet is still not online (pre-print is of course here).

Stephan and I have also engaged with Mike Batty’s commentary in EPB [5], and this has triggered a nice discussion.

I hope you have a fun read!

  1. Hamzei, E., Li, H., Vasardani, M., Baldwin, T., Winter, S. and Tomko, M. (in press, to appear 2020). Place questions and human-generated answers: A data analysis approach. Geospatial Technologies for Local and Regional Development. Proceedings of the 22nd AGILE Conference on Geographic Information Science. Kyriakidis, P., Hadjimitsis, D., Skarlatos, D. and Mansourian, A., Springer Heidelberg (link, pre-print)
  2. Li, H., Wang, M., Vasardani, M., Tomko, M., & Baldwin, T. (2019). UniMelb at SemEval-2018 Task 12:  Multi-model combination for toponym resolution. Paper presented at the SemEval-2019: 13th International Workshop on Semantic Evaluation, Minneapolis, Minnesota, USA.(link , paper)
  3. Winter, S., Tomko, M., Vasardani, M., Richter, K.-F., Khoshelham, K., & Kalantari, M. (accepted, 2019). Infrastructure-Independent Indoor Localization and Navigation. ACM Computing Surveys. (pre-print)
  4. Moss, R., Naghizade, E., Tomko, M., & Geard, N. (2019). What can urban mobility data reveal about the spatial distribution of infection in a single city? BMC Public Health, 19(656). doi:10.1186/s12889-019-6968-x (link , paper)
  5. Winter, S., & Tomko, M. (2019). Beyond Digital Twins — A Commentary. Environment and Planning B: Urban Analytics and City Science, 46(2), 395-398. doi:10.1177/2399808318816992 (link , paper)
  6. Majic, I., Naghizade, E., Winter, S., & Tomko, M. (2019). Discovery of topological constraints on spatial object classes using a refined topological model. Journal of Spatial Information Science. doi:10.5311/JOSIS.2019.18.459 (link , paper)

Come and help us breathe life into the future of Mapping!

We have acquired an exciting new piece of equipment – a Clearpath Robotics Husky Autonomous Ground Vehicle, fully kitted with a variety of sensors, including automotive-standard Velodyne LIDAR. This will be a great boost to our research on autonomous mapping. Get in touch, for all from sensing ( in collaboration with colleagues) to data processing and storage, to coverage planning.

Desired skills: ROS/Python/C++/Matlab.


New paper: Street network studies: from networks to models and their representations.

This week our paper (with Stephen Marshall [UCL], Jorge Gil [Chalmers], Karl Kropf [Oxford Brookes University] and Lucas Figueiredo [UFPB), Brazil]): “Street network studies: from networks to models and their representations.” appeared as Open Access in Networks and Spatial Economics.

This is our contribution to the debate on modeling street networks, trying to disentangle issues of modeling, representation, and analysis. It should also be a useful paper for all considering aspects of reproducibility of street analysis research. What nice outcome from a few fantastic workshops, in particular, the Urban Networks workshop in Ghent!

(cite as: Marshall, S., Gil, J., Kropf, K., Tomko, M. and Figueiredo, L. (2018). “Street network studies: from networks to models and their representations.” Networks and Spatial Economics: 15. link: https://doi.org/10.1007/s11067-018-9427-9 )

TRIIBE paper “Understanding the predictability of user demographics from cyber-physical-social behaviours in indoor retail spaces” published in EPJ Data Science (OA)

Understanding the association between customer demographics and behaviour is critical for operators of indoor retail spaces. In our new study published in EPj Data Science, we explore associations based on a combined understanding of customer Cyber (online), Physical (Where?), and Social behaviour. We combine the results of a traditional questionnaire with large-scale WiFi access logs which capture customer cyber and physical behaviour. We investigate the predictability of user demographics based on CPS behaviors captured from both sources and provide strong support for demographic studies based on large-scale logs data capture.

This is the most recent publication from the fruitful ARC LP TRIIBE collaboration, with colleagues Yongli Ren, Flora Salim, Jeff Chan and Mark Sanderson (all RMIT).

Cite as:

Ren, Y., M. Tomko, F. D. Salim, J. Chan and M. Sanderson (2018). “Understanding the predictability of user demographics from cyber-physical-social behaviours in indoor retail spaces.” EPJ Data Science 7(1): 1-21. DOI: 10.1140/epjds/s13688-017-0128-2

The paper can be freely accessed here: http://rdcu.be/DZkO

Ivan’s first paper “Finding equivalent keys in OpenStreetMap” accepted to GeoAI’17

It is a special feeling when your PhD student publishes their first paper! Congratulations to Ivan Majic (that’s Magic with a “J“) for the first workshop paper – a great start for the PhD, keep on the good work!

Reference: Majic, I., Winter, S., & Tomko, M. (2017). Finding equivalent keys in OpenStreetMap: semantic similarity computation based on extensional definitions. In GeoAl’17: 1st ACM SIGSPATIAL Workshop on Artificial Intelligence and Deep Learning for Geographic Knowledge Discovery, . Los Angeles Area, CA, USA: ACM. (pre-print)

TRIIBE paper “A Location-Query-Browse Graph for Contextual Recommendation” accepted to TKDE

I am excited that one of our main papers from TRIIBE (lead author Yongli Ren, RMIT) has been accepted to TKDE.”

Working with large-scale WiFi sensing data is never easy, and with the bulk the amount of noise increases as well. Here we present a method that clearly demonstrates the interplay, and the benefits of combining the physical location, as well as the search and browsing histories of indoor visitors (in an aggregate, privacy aware way) to improve contextual recommendations in indoor spaces.

Ren, Y., M. Tomko, F. Salim, J. Chan, C. L. A. Clarke and M. Sanderson (accepted, 2017). “A Location-Query-Browse Graph for Contextual Recommendation.” IEEE Transactions on Knowledge and Data Engineering. DOI:10.1109/TKDE.2017.2766059 (pre-print, publisher link)


P.S.> apologies for the huge delay in posting. Many more news are overdue.

PHD scholarships: Making Computers Understand Place

Your PhD research will contribute to a project at the forefront of fundamental AI and spatial information research: Making computers understand common language about place. This project, funded by the Australian Research Council (2017-2019), will deliver the computational methods to capture, model, process, and interact on human place knowledge, which conceptualizes places and their relations, instead of using coordinates and maps. The outcomes will enable human-machine interaction that is capable of responding to human intuition.

The project engages in research in artificial intelligence, language technologies, and spatial information science – you will work in an interdisciplinary environment. The project is also geographically spread between The University of Melbourne, the Australian National University in Canberra, and the University of California, Santa Barbara. The scholarships, however, are allocated to the two Australian universities, which are Australia’s leading research universities.

We seek applicants covering one of the following tasks:

1. Capture (Melbourne: led by Tim Baldwin). This task addresses the extraction of place knowledge in conversational context from large-scale text corpora such as social media and web sources, or crowd-sourced from mobile apps.

2. Modelling (Melbourne: led by Stephan Winter). This task addresses the representation of extracted place knowledge in place graphs, especially the representation of context with places and their relationships.

3. Reasoning (Canberra: led by Jochen Renz). This task addresses context-sensitive spatial reasoning beyond maintaining a locally consistent database. We expect to make progress on this long avoided challenge by considering the spatial context derived from the integration of place knowledge and maps.

4. User Interaction (Melbourne: led by Martin Tomko). This task addresses the user interaction with a place graph, enabling users to query or modify stored data by developing methods answering user queries taking into account the context of the querying user.

Selection Criteria

* Masters or Honours Degree in Computer Science, Geodesy/Geomatics/Geoinformatics, or another relevant discipline;

* A weighted average mark at least satisfying the entry requirements of the PhD programs at the respective universities;

* Demonstrated ability to perform independent research (e.g., by a research component in the master degree);

* Excellent written and verbal communication skills to technical and non-technical audiences (e.g., demonstrated in the written application and interview);

* Software development skills;

* Ability to work in (at least one of) spatial data handling and analysis, database systems, machine learning, natural language understanding, and artificial intelligence;

* Ability to work co-operatively in a multi-disciplinary environment and team.


Scholarships are for three years, with the expectation to complete a PhD in this time-frame. The universities have excellent environments and frameworks to support PhD research. Also, successful candidates will be provided a tuition fee waiver. Scholarships are sufficient to cover living costs in Melbourne or Canberra. The project also provides funds for conference travels commensurate with opportunity.

How to apply

Send your letter of application (addressing the selection criteria, and identifying the particular task of interest), a CV, and a transcript of your last degree per email to the project leader, Prof Stephan Winter (winter@unimelb.edu.au).

The application deadline is 20 March 2017, for a start later in 2017. Selection is pending admission at the respective university and visa processes.

Vacancy: Research Fellow in Autonomous Spatial Data Integration

All #AI #ML #DB talent out there!

A Research Fellow position is available within a project funded by the Australian Research Council, “Self-Healing Maps”. The project aims to expand our ability to automatically integrate real-time data in map databases of high integrity, enabling adaptive and autonomous protection of maps from the insertions of erroneous or malicious data, and the detection and correction of inconsistencies.

You will work within under the guidance of Dr Martin Tomko and other chief investigators across all aspects of Geomatics and spatial information. You will be in charge of conducting research and coordinating the efforts leading to a new generation of mechanisms supporting spatial data integration. You will devise novel methods enabling the autonomous and evolving discovery and rectification of errors, discrepancies and inconsistencies in spatial databases. You will apply your outstanding computational skills to demonstrate the validity of your theoretical contributions through the implementation of proof-of-concept software.

You will conduct collaborative and independent research, leading to the preparation and publication of research outcomes in conferences and journals. You will be located in the Department of Infrastructure Engineering in the Melbourne School of Engineering.

Advertisement: http://jobs.unimelb.edu.au/caw/en/job/890060/research-fellow-in-spatial-data-integration

For more information, contact Dr Martin Tomko (tomkom@unimelb.edu.au)

“Ripe for the Picking? Dataset Maturity Assessment based on Temporal Dynamics of Feature Definitions”

Our paper that has been recently accepted for publication in the International Journal of Geographical Information Science investigates a thus-far unexplored aspect of spatial data of particular relevance to VGI usability (OSM as a case) – the differences between (geometric) feature definitions within a feature class. I am particularly pleased by this paper as it is the outcome of a short, but intense and very satisfying collaboration with Stephen Maguire, a Masters of IT (Spatial) student here at the University of Melbourne. Congratulations Stephen!


From the abstract:

Map databases traditionally capture snapshot representations of the world following strict data collection and representation guidelines. The content of these map databases is often assessed using data quality metrics focusing on accuracy, completeness and consistency. The success of volunteered geographic information, supporting evolving representations of the world based on fluid guidelines, has rendered these measures insufficient. In this paper, we address the need to capture the variability in quality of a map database. We propose a new spatial data quality measure — dataset maturity — enabling assessment of the database based on temporal trends in feature definitions, specifically geometry type definitions. The proposed measure can be (1) efficiently used to identify feature definition patterns reflecting community consensus that could be formalised in community guidelines; and (2) deployed to identify regions that would benefit from increased editorial activity to achieve greater map homogeneity. We demonstrate the measure based on the content of the OpenStreetMap database in four regions of the world, and show how the proposed dataset maturity measure captures a distinct quality of the datasets, distinct to data completeness and consistency.

Read the full thing in pre-print, supplementary materials here.

Maguire, S., & Tomko, M. (accepted 2017). Ripe for the Picking? Dataset Maturity Assessment based on Temporal Dynamics of Feature Definitions. International Journal of Geographical Information Science. doi:10.1080/13658816.2017.1287370