Geo Data Mining and Visual Analytics

Transcription

1 Geo Data Mining and Visual Analytics Beyond Limits Developments in Cadastral Domain Workshop, Zürich 19 March 2015 Susanne Bleisch Institute of Geomatics Engineering School of Architecture, Civil Engineering and Geomatics HABG FHNW University of Applied Sciences and Arts Northwestern Switzerland

2 Define - Exemplify - Use

3 Data Science Statistics - Data mining - Machine Learning - Artificial Intelligence Figure: Mamatha Upadhyaya. Capgemini.

4 (Geographic) Data Mining u u u u Data- and computation-poor to data- and computation-rich environments Traditional (spatial) analytical methods developed for small and homogenous data sets From confirmatory (a priori hypotheses) to exploratory (gain insight) Find hidden information in the data: novel, valid, useful and understandable Miller, H. J., & Han, J. (Eds.). (2009). Geographic Data Mining and Knowledge Discovery (2nd ed., p. 458). Parkway: CRC Press.

5 Clustering Outlier analysis Typical data mining tasks Classification Modelling/Prediction A A A A A? B B B Association analysis Sequence analysis {wine, cheese} -> {bread} C > A > B

6 Visual Analytics Humans in the loop Visual Analytics is the science of analytical reasoning supported by interactive visual interfaces.... detect the expected and discover the unexpected... Thomas, J.J. & Cook, K.A., Illuminating the Path: The Research and Development Agenda for Visual Analytics, IEEE Computer Society. Figure:

7 Visual Analytics Andrienko, G. & Andrienko, N., Visual Analytics.

8 u Textual data: documents, news, , tweets,... u Databases and collections u Image data: from satellite to user generated u Sensor data u Video data u Networks, linked data u Structured, semi-structured, unstructured u... Data Challenges: u Heterogeneous sources and types u massive amounts but incomplete, inconsistent, time-varying and potentially inaccessible,...

9 Spatial is special u u u u u GI is uncertain, imprecise or vague. GI is correlated, i.e. non-random. GI is dynamic, it represents an ever-changing world. GI is scale dependent. GI is heterogeneous.

10 Define - Exemplify - Use

11 recaptcha

12 Information from small pieces of data Rudder, C. (2014). Dataclysm: Who We Are (When We Think No One s Looking). London, UK: Crown/Archetype.

13 Information from small pieces of data dont-be-ugly-by-accident/

14 Geographic information retrieval Midlands Wales Arampatzis, A., van Kreveld, M., Reinbacher, I., Jones, C. B., Vaid, S., Clough, P., Hideo, J. & Sanderson, M. (2006). Web-based delineation of imprecise regions. Computers, Environment and Urban Systems, 30,

15 Geographic information retrieval Hollenstein, Livia; Purves, Ross S (2010). Exploring place through user-generated content: Using Flickr to describe city cores. Journal of Spatial Information Science, 1(1):21-48.

16 Descriptions > maps We re sitting in Baretto s in the Alan Gilbert Building, across Grattan street is one of the medical buildings. Down the hill along Grattan street the new building being constructed is the Peter Doherty Institute and diagonally across the road (Royal Parade) is Melbourne Hospital. In the other direction... Vasardani, M., Timpf, S., Winter, S. and Tomko, M. (2013) From Descriptions to Depictions: A conceptual Framework. COSIT 2013, Sept. 2th - 6th, Scarborough, UK. In Lecture Notes in Computer Science (LNCS), , Springer.

17 a b c d e f g h i j k l m n o p q r s t u v w

18 Event causes Event Full moon causes Start of fish migration State allows Event High river flow allows Start of fish migration Bleisch, S., Duckham, M., Galton, A., Laube, P., & Lyon, J., Mining candidate causal relationships in movement patterns. Int. J. of Geographical Information Science, 28(2),

19 Data Mining Sequence analysis Fish migration Fish migration upstream downstream Moon phases expected observed Bleisch, S., Duckham, M., Galton, A., Laube, P., & Lyon, J., Mining candidate causal relationships in movement patterns. Int. J. of Geographical Information Science, 28(2),

20 Data Mining Assocation analysis (river flow) Fish migration Fish migration upstream downstream expected observed Bleisch, S., Duckham, M., Galton, A., Laube, P., & Lyon, J., Mining candidate causal relationships in movement patterns. Int. J. of Geographical Information Science, 28(2),

21 Event causes Event -> event sequences Subsequence Support Count 1 (1) (7) (7)-(1) (5) (5)-(1) (1)-(1) (5)-(5) (10) (5)-(5)-(1) (10)-(1) (2) (2)-(1) (5)-(7) (7)-(5) (5)-(7)-(1) (7)-(7) (7)-(5)-(1) (1)-(5) (1)-(7) (7)-(7)-(1) (1)-(7)-(1) (5)-(7)-(5) (1)-(5)-(1) (5)-(7)-(5)-(1) (7)-(1)-(1) (5)-(5)-(5) (7)-(7)-(7)-(1) (7)-(1)-(5)-(1) (2)-(7) (7)-(5)-(7) (2)-(5)-(1) (5)-(1)-(7) (5)-(2) (1)-(5)-(7)-(5)-(1) (4) (10)-(5)-(5) (2)-(7)-(1) (5)-(5)-(10)-(1) (5)-(2)-(1) (7)-(7)-(1)-(1) (7)-(5)-(7)-(1) (4)-(1) (5)-(1)-(5)-(5)-(1) (5)-(1)-(7)-(1) (9)-(7) (2)-(1)-(1) (5)-(7)-(7)-(5) (10)-(5)-(5)-(1) (1)-(3) (5)-(5)-(1)-(5)-(1) (1)-(3)-(1) (7)-(1)-(10)

22 Define - Exemplify - Use

23 Thank you! Beyond Limits Developments in Cadastral Domain Workshop, Zürich 19 March 2015 Susanne Bleisch