Public Safety Mashups to Support Policy Makers || Choennie

EGOVIS – Sept 2010

Public Safety Mashups to Support Policy Makers

Sunil ChoenniRotterdam University/ WODC

Content

• Introduction• Measuring Safety• Architecture Design & Implementation• Creating Mashups• Conclusions & further Research

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Introduction

• Policy makers have a need for statistical insight into public safety at different levels, such as regional and national

• Data wrt public safety are collected by different organisations and published on different websites.

• Integrating these data may increase the insight in public safety

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Introduction

Goal: provide policy makers a tool such that they may create mashups, i.e., able to combine data from different sources and create their own content.

requirement: avoid undesired effects• violation of privacy• misinterpretation of statistics• disclosure of the identity of a group of individuals

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Approach

• How to Measure Safety - broad and subjective notion

- searched for variables that are useful to make safety operational

• To find out the Information Need of Policy Makers

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Measuring Safety

Phenomena and variables related to public safety• Have exploited literature on criminology and public safety

• Have exploited domain knowledge and databases

Phenomena related to Public Safety (about 1500 variables)

Crime – registered crime, victims, preventive measures

Enforcement – police contacts, suspects, solved crime

Sanction – fines, imprisonments, judicary cases

Police & justice resources - prison capacity, police officers

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Information Need

By means of two workshops: about 30 people participated ranging from junior policy makers to directors

Some individual meetings after the workshops

Results- Three types of questions- Contextual data is required as well- Requirements to the tool

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Three types of questions

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Simple queries. For example,how many people in a region within a time period responded in a specific way to a specific survey question?

Context of a quantifier. For example, how does the growth or decline of a specific figure in a geographical region relate to another figure? For example, a growth in bicycle thefts in a neighbourhood can turn into a relative decline when local population growth exceeds.

Similarity queries, i.e. looking for regions that share in some respect the same context. After querying for a specific data set in which some numbers stand out in some way, the user can query for other regions that show similar numbers or trends.

Requirements

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• rules & regulations wrt privacy should be respected (in agreement with our requirement)

• Help required in interpreting statistics/result• Interaction with the tool• Possibility to add new data and questions

Architecture design focussed on- Extensibility and flexibility- User friendly interfaces

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mashedup data

defined mashup

store/retrievedata

source

data

Data Warehouse

Interface Layer

Presentation module

Mashup module

ETL processset

queries

queryresults

translator1

translator2

translatorn

mash

up_to

_sQL

Data

acce

ss layer

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Architecture

Data is stored aggregated at police region level in DW

Each region is distinguished by a regionid in DWMashup module contains click and drag facilities

and menus to define a mashupPresentation Module has the capability to present

the output as tables, graphs, figures, …For interpretation purposes: how a result is

obtained? E.g. is the result based on survey or register data, the meaning of a unit in which a number is expressed

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Architecture: to prevent violation of privacy

- only attributes are stored in the system that are in line with Dutch Personal Data Protection Act, i.e., no data wrt someone religion or life conviction, political conviction, health, sexual orientation

- only aggregated data are stored- Mashups that contains results that may violate the

privacy are not shown by the presentation module ( e.g. if there are only 2 convicted persons for a crime

type X, this is not shown. Also if there are 90 % of the people in a region involved in crime, this is not shown as well

- ( An extensive explanation module to facilitate interpretation)

Creating Mashups

• User selects indicator from a tree,

• looks at meta data,• selects a period,• selects a region level,• and selects a

presentation form

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Conclusion & further research

To meet the practical need of policy makers we implemented a tool that facilitates to create mashups. Currently the tool is used at our department.

• avoid undesired effects

• Extensible

• rich set of presentation capabilities

Further research

• evaluation

• Scalability, google like engine

• (adding more resources - information overload?)

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Provide citizens to create their own mashups i.e., combine different data sources focussed towards Rotterdam to create their own content.

However,

• More data (locally focussed)

• Wide variety of structured data such as sensor data

(tid, pid, objectid)

• Added value different types of data, such as semi-structured data and unstructured data ( social media)

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Other applications: Rotterdam Open Data