Public Safety Mashups to Support Policy Makers || Choennie
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EGOVIS is a tool that allows policymakers to create "mashups" by combining data from different sources to gain insights into public safety. It addresses the information needs of policymakers by providing contextual data and the ability to perform simple, contextual, and similarity queries. The system's architecture stores aggregated data in a data warehouse and includes modules for mashup creation, data presentation, and translation to facilitate interpretation while avoiding privacy violations or misinterpretation of statistics. Evaluation of the tool's scalability and ability to incorporate additional data sources is suggested for further research.
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Public Safety Mashups to Support Policy Makers || Choennie
- 1. EGOVIS – Sept 2010 Public Safety Mashups to Support Policy Makers Sunil Choenni Rotterdam University/ WODC
- 10. mashed up data defined mashup store/ retrieve data source data Data Warehouse Interface Layer Presentation module Mashup module ETL process set queries query results translator 1 translator 2 translator n mashup_to_sQL Data access layer
- 14. Vul titel presentatie in | Vul datum in
Editor's Notes
- 26-06-09
- 26-06-09 If it appears that some parts are becoming safer than others, resources can be moved from one to the other. Statistical data provides insights into why areas are becoming safer while others are not. There is a lot of data on safety, primarily coming from surveys and operational databases. Problem: maintained by many different organisations (Statistics Neth, Just, Internal Affairs (Police) Policy makers have to put in a lot of effort to get the data they need. They have to phone, email people, who have to make time to find what they need. They might not be there, wait for people to return calls. Often weeks go by. We developed a tool that does all this. It collects the data they need in their day-to-day work to that they have it ready when they need it. It is extensible and easily updatable. It also contains what we call “contextual data”, data that is not about safety itself, but still related in some way. Like demographics.
- 26-06-09 If it appears that some parts are becoming safer than others, resources can be moved from one to the other. Statistical data provides insights into why areas are becoming safer while others are not. There is a lot of data on safety, primarily coming from surveys and operational databases. Problem: maintained by many different organisations (Statistics Neth, Just, Internal Affairs (Police) Policy makers have to put in a lot of effort to get the data they need. They have to phone, email people, who have to make time to find what they need. They might not be there, wait for people to return calls. Often weeks go by. We developed a tool that does all this. It collects the data they need in their day-to-day work to that they have it ready when they need it. It is extensible and easily updatable. It also contains what we call “contextual data”, data that is not about safety itself, but still related in some way. Like demographics.
- 26-06-09 If it appears that some parts are becoming safer than others, resources can be moved from one to the other. Statistical data provides insights into why areas are becoming safer while others are not. There is a lot of data on safety, primarily coming from surveys and operational databases. Problem: maintained by many different organisations (Statistics Neth, Just, Internal Affairs (Police) Policy makers have to put in a lot of effort to get the data they need. They have to phone, email people, who have to make time to find what they need. They might not be there, wait for people to return calls. Often weeks go by. We developed a tool that does all this. It collects the data they need in their day-to-day work to that they have it ready when they need it. It is extensible and easily updatable. It also contains what we call “contextual data”, data that is not about safety itself, but still related in some way. Like demographics.
- 26-06-09 When we started out, we did a number of workshops with our intended users, where we demoed prototypes of the interface and asked for feedback. Some 800 indicators from some thirty sources from a dozen organisations. Privacy. – small numbers – personal accounts, logged: mandatory by law. But we also came up with our own list of requirements. We have to maintain this, so it had to be extensible and easy to update. That means that we needed to be able to connect every imaginable source, whatever its specs. Also: meta data. Exensive information about what numbers mean, where they come from, what methods used, etc.
- 26-06-09 When we started out, we did a number of workshops with our intended users, where we demoed prototypes of the interface and asked for feedback. Some 800 indicators from some thirty sources from a dozen organisations. Privacy. – small numbers – personal accounts, logged: mandatory by law. But we also came up with our own list of requirements. We have to maintain this, so it had to be extensible and easy to update. That means that we needed to be able to connect every imaginable source, whatever its specs. Also: meta data. Exensive information about what numbers mean, where they come from, what methods used, etc.
- 26-06-09 When we started out, we did a number of workshops with our intended users, where we demoed prototypes of the interface and asked for feedback. Some 800 indicators from some thirty sources from a dozen organisations. Privacy. – small numbers – personal accounts, logged: mandatory by law. But we also came up with our own list of requirements. We have to maintain this, so it had to be extensible and easy to update. That means that we needed to be able to connect every imaginable source, whatever its specs. Also: meta data. Exensive information about what numbers mean, where they come from, what methods used, etc.
- 26-06-09 When we started out, we did a number of workshops with our intended users, where we demoed prototypes of the interface and asked for feedback. Some 800 indicators from some thirty sources from a dozen organisations. Privacy. – small numbers – personal accounts, logged: mandatory by law. But we also came up with our own list of requirements. We have to maintain this, so it had to be extensible and easy to update. That means that we needed to be able to connect every imaginable source, whatever its specs. Also: meta data. Exensive information about what numbers mean, where they come from, what methods used, etc.
- 26-06-09 When we started out, we did a number of workshops with our intended users, where we demoed prototypes of the interface and asked for feedback. Some 800 indicators from some thirty sources from a dozen organisations. Privacy. – small numbers – personal accounts, logged: mandatory by law. But we also came up with our own list of requirements. We have to maintain this, so it had to be extensible and easy to update. That means that we needed to be able to connect every imaginable source, whatever its specs. Also: meta data. Exensive information about what numbers mean, where they come from, what methods used, etc.
- 26-06-09 Leg plaatje uit Transformation layer: consistency checks Sources: no PKs, only connectable by period/region Meta data
- 26-06-09 To make things a little less abstract, I will show some screens from our interface., in which the user finds out something about a hot topic at this moment, loitering youth.
- 26-06-09
- 26-06-09