1 OLAP for heterogeneous socio-economic data – the challenge of integration, analysis and crime prevention: a Czech case study. Jiří HORÁK, Igor IVAN, Bronislava HORÁKOVÁ VSB-Technical University of Ostrava Intergraph CS Ltd. Czech Republic

2 European Forum for Geography and Statistics 2015 Conference Vienna, Austria, 10 – 12 November 2015 Big Spatial Data Features: – Volume beyond the limit of usual geo-processing, – Velocity higher than available by usual processes, – Variety, combining more diverse geodata sources than usual. traditional methods of geodata collection, storing, processing, controlling, analysing, modelling, validating and visualizing fail to provide effective solutions how to exploit the big spatial data?

3 European Forum for Geography and Statistics 2015 Conference Vienna, Austria, 10 – 12 November 2015 part of Business intelligence On-line analytical processing - provide an effective and intuitive access to consolidated data (harmonized and aggregated) stored in multidimensional data structures. OLAP operations: – Drill-down (success in hierarchy down, towards more details), – Roll - Up (success in hierarchy up, obtaining more aggregated data) – Drill-Across (link several fact tables with the same granularity) – Slice-and-Dice (splitting data) – Pivot (exchange of dimension in designed view) multidimensional database as a Data Warehouse: subject-oriented, integrated, time-variant and non-volatile collection of data Multidimensional database and OLAP

4 European Forum for Geography and Statistics 2015 Conference Vienna, Austria, 10 – 12 November 2015 dimensional modelling elementary items in fact tables contain aggregated data (counts, sums etc.) organised according to dimensions (features) dimensions usually contain hierarchical structure Granularity – the level of detail for facts Additivity - possibility to summarize data according to dimensions Fact tables and dimensions

5 European Forum for Geography and Statistics 2015 Conference Vienna, Austria, 10 – 12 November 2015 Data sources: population data – grid 1km, 100 m Census 2011(CZSO), municipal IS reg. of land identif., addresses and properties - buildings (NMCA) central crime register (Police CZ) - events offence register (city police) – local, central is planned register of schools (Min. of Education, Youth and Sports) - contact register of health service providers (Min. of Health) – contact, beds register of unemployed (Labour office) register of gambling machines (Min. of Finance) register of companies (CZSO, or others) DWH & OLAP for social environment (crime, human factors)

6 European Forum for Geography and Statistics 2015 Conference Vienna, Austria, 10 – 12 November 2015 ETL processes: Data differs in quality, formats, accesses, legal and ethical aspects (license policy, sensitivity), and maintenance control procedures - integrity constrains, check validity of time range, geographical range, referential integrity etc. harmonisation – referential time of event from time interval, harmonisation of addresses, classification of facilities, buildings etc. Geocoding for missing or bad coordinates aggregation – according to multidimensional modelling data anonymization – filtering, scramble, rounding, projection ETL processes for DWH & OLAP for social environment

7 Fact tables: CRIME POPULATION UNEMPLOYED HEALTH BUILDING FACILITIES Dimensional tables: DATE SQUARE ADMIN_UNITS AGE SEX and more Structure

8 European Forum for Geography and Statistics 2015 Conference Vienna, Austria, 10 – 12 November 2015 Grid – 100 x 100 m (4 th level of the scale system for communes and urban districts, Bacler), 500 m, 1 km, 5 km Administrative units - part of municipality, municipality, MEA, LAU1, NUTS3 temporal dimension - one day unit, week, month, year day-cycle hours – hour unit, morning time, rush hours age - 5-years basic categories, 10-years, 20-years, “30 and more”. crime (& offences) - standard 3-level classification system facilities - purpose and the hierarchical structure Dimensions and hierarchy

9 European Forum for Geography and Statistics 2015 Conference Vienna, Austria, 10 – 12 November 2015 Pivoting Place of commitment X Resid. of offenders OLAP pivoting, selections, relationships Scatter plot, regres.a. Gambling machines X Population

10 European Forum for Geography and Statistics 2015 Conference Vienna, Austria, 10 – 12 November 2015 Data grid view

11 European Forum for Geography and Statistics 2015 Conference Vienna, Austria, 10 – 12 November 2015 Number of burglaries per 100 flats (2014)

12 European Forum for Geography and Statistics 2015 Conference Vienna, Austria, 10 – 12 November 2015 # burglaries to dwellings, # residential buildings (2014) 3 towns: CB Ceske Budejovice KO Kolin OV Ostrava Differences: density of buildings density of burglaries dependencies

13 European Forum for Geography and Statistics 2015 Conference Vienna, Austria, 10 – 12 November 2015 Number of gambling machines per 1km 2

14 European Forum for Geography and Statistics 2015 Conference Vienna, Austria, 10 – 12 November 2015 Number of gambling clubs per 100 inhabitants

15 European Forum for Geography and Statistics 2015 Conference Vienna, Austria, 10 – 12 November 2015 # sprayer crimes per 1 school (2014)

16 European Forum for Geography and Statistics 2015 Conference Vienna, Austria, 10 – 12 November 2015 Classification tree for sprayer crimes Dependency – second.schools + regions; no second.schools + gambling m. + districts No dependency – population, buildings, basic schools, property offences

Thank you for your attention! 17 Data is provided by the courtesy of the Czech Statistical Office, Police of the Czech Republic, Czech Office for Surveying, Mapping and Cadaster, Czech Ministry of Finance, Labour offices, Czech Ministry of Health and Municipal Police departments in Ostrava, Kolín and České Budějovice. The research is supported by the research of the Czech Ministry of Interior, project “Geoinformatics as a tool to support integrated activities of safety and emergency units”, No. MV /VZ-2012.