l The Virtuous Circle l

◼︎

The Connected City


The Virtuous Circle


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Value Proposition:


Types of data collected


Raw Data

 

Primary data directly collected from a source. Raw data has not been subjected to processing, “cleaning” by researchers to remove outliers, obvious instrument reading errors or data entry errors or any analysis.


Processed Data


Data that has been processed from raw data where processing may include cleaning, aggregation, conversion to a different format, etc.


Combined Data


Data that is the result of bringing together multiple data sources, e.g., pedestrian count and weather data.


Data Sold to Customers


Data packaged as a data set that the customers can download or packaged as a service the customers can query via an API. In both cases (data set and API), the data is contingent on a fee and the customer also expects a service level agreement in terms of quality, freshness, etc.


Data Given-in-Kind


Same data as Data Sold to Customer with no contractual obligations in terms of quality, freshness, etc. The data is often given “as is”.


Usage Data


Data about how users consume the data. In the context of web data, the data is information about the Web (pages, content of pages, etc.) and usage data is the popularity of pages based on queries.

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Creation of city-scale data sets


▪︎  Pedestrian analytics


▪︎  Street-level still images


▪︎  Fixed-street asset locations


▪︎  Vehicle movements


▪︎  Commercial space usage



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Data sets are then used for


▪︎  Situational analysis


▪︎  Cause-and-effect analysis


▪︎  Prediction


▪︎  Impact and value assessment


▪︎  Ecosystem support


▪︎  Future Mobility



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Public Sector uses of data


▪︎  Improving and maintaining infrastructure


▪︎  Promoting environmental safety


▪︎  Fostering community wellness


▪︎  Increasing access to affordable housing


▪︎  Stimulating wage growth and employment    

     creation



◼︎

Private Sector uses of data


▪︎  Increasing revenue and market share


▪︎  Optimizing existing processes


▪︎  Big data driven decision making


▪︎  Future Mobility technology development



◼︎

The Connected City


The Virtuous Circle


◼︎

Value Proposition:


Types of data collected


Raw Data

 

Primary data directly collected from a source. Raw data has not been subjected to processing, “cleaning” by researchers to remove outliers, obvious instrument reading errors or data entry errors or any analysis.

Data Sold to Customers


Data packaged as a data set that the customers can download or packaged as a service the customers can query via an API. In both cases (data set and API), the data is contingent on a fee and the customer also expects a service level agreement in terms of quality, freshness, etc.

Processed Data


Data that has been processed from raw data where processing may include cleaning, aggregation, conversion to a different format, etc.

Data Given-in-Kind


Same data as Data Sold to Customer with no contractual obligations in terms of quality, freshness, etc. The data is often given “as is”.

Combined Data


Data that is the result of bringing together multiple data sources, e.g., pedestrian count and weather data.

Usage Data


Data about how users consume the data. In the context of web data, the data is information about the Web (pages, content of pages, etc.) and usage data is the popularity of pages based on queries.

◼︎

Creation of city-scale data sets


▪︎  Pedestrian analytics


▪︎  Street-level still images


▪︎  Fixed-street asset locations


▪︎  Vehicle movements


▪︎  Commercial space usage


◼︎

Data sets are then used for


▪︎  Situational analysis


▪︎  Cause-and-effect analysis


▪︎  Prediction


▪︎  Impact and value assessment


▪︎  Ecosystem support


▪︎  Future Mobility


◼︎

Public Sector uses of data


▪︎  Improving and maintaining infrastructure


▪︎  Promoting environmental safety


▪︎  Fostering community wellness


▪︎  Increasing access to affordable housing


▪︎  Stimulating wage growth and employment creation


◼︎

Private Sector uses of data


▪︎  Increasing revenue and market share


▪︎  Optimizing existing processes


▪︎  Big data driven decision making


▪︎  Future Mobility technology development