1 OLAP for heterogeneous socio-economic data – the challenge of integration, analysis and crime...

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OLAP for heterogeneous socio-economic data – the challenge of integration, analysis and crime

prevention: a Czech case study.

Jiří HORÁK, Igor IVAN, Bronislava HORÁKOVÁVSB-Technical University of Ostrava

Intergraph CS Ltd.Czech Republic

2European Forum for Geography and Statistics 2015 Conference Vienna, Austria, 10 – 12 November 2015

Big Spatial Data

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?


• 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


• 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

http://www.code-magazine.com/focus/Article.aspx?QuickID=1103091


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)


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

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Fact tables:• CRIME• POPULATION• UNEMPLOYED• HEALTH• BUILDING• FACILITIES

Dimensional tables:• DATE• SQUARE• ADMIN_UNITS• AGE• SEX• and more

Structure


• Grid – 100 x 100 m (4th 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


Pivoting

Place of commitment

XResid. of offenders

OLAP pivoting, selections, relationships

Scatter plot, regres.a.

Gambling machinesX

Population


Data grid view


Number of burglaries per 100 flats (2014)


# burglaries to dwellings, # residential buildings (2014)

3 towns:• CB Ceske Budejovice• KO Kolin• OV Ostrava

Differences:• density of buildings• density of burglaries • dependencies


Number of gambling machines per 1km2


Number of gambling clubs per 100 inhabitants


# sprayer crimes per 1 school (2014)


Classification tree for sprayer crimes

Dependency – second.schools + regions; no second.schools + gambling m. + districtsNo dependency – population, buildings, basic schools, property offences

Thank you for your attention!

[email protected]

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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-32046-58/VZ-2012.

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1 OLAP for heterogeneous socio-economic data – the challenge of integration, analysis and crime...

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