Big Data in public sector

Big data for the state

Most the Russian departments already saved up enough data bulks and now can use their potential for quality improvement of the made decisions. In recent years for increase in security in Russia a number of infrastructure projects which are connected, first of all, with installation of surveillance cameras is implemented. However the data arriving from cameras are only one channel of data. High-quality increased security requires transition to the pro-vigorous activity allowing to predict crime and in advance to plan resource allocation for its prevention. It is possible in the analysis of historical data on precedents for creation of profiles of risk – conditions under which this or that event is reproduced or a crime is committed. Creation of such profiles is possible using modeling of dependence between a set of the characteristics describing an object and the studied phenomenon^[1].

For example, in London the fire service uses social and demographic data for assessment and prevention of fire risks. Such indicators of inhabitants as age, education, income, an employment type, type of housing and others allow to construct the predictive model increasing quality of risk assessments of the fire on districts of the city. When Michael Bloomberg became the mayor of New York, approach to fire-fighting was "rebooted" too: for object definition of fire-prevention inspections began to use the profiles of risk of buildings developed on the basis of data of the general plan, construction, financial and fire-prevention departments.

Other task in the field of fire safety – geographically optimal placement of resources for liquidation of already come fire. On the saved-up data characterizing departures of fire crew to the place of the fire in the past, information on traffic jams and availability of thoroughfares modeling of dependence between geographical remoteness of the fire station and time which is required to crew to reach to the place of the fire is performed. Then the return problem is solved: where should the station that target indicators of time were observed be located?

One more example – use of Big Data for crime prevention. On the basis of the facts of the committed crimes the diagram of patrol of the area is developed. Timely existence of the patrol car can help to prevent crime. Data on a crime amount, their place, time, type and number of victims allow to define how many it is required machines where and in what time and then to optimize a route taking into account this information.

Also on the basis of social data it is possible to develop profiles of risk of the children subject to ill treatment. These profiles allow to identify families concerning which carrying out scheduled maintenance is required.

Analytics on service of city security

Application of analytics in the field of city security allows to increase efficiency of already existing processes – inspections, patrol and others. Management process is improved directly in the organization which exercises control. The solution on a surface: data already are, they need to be added to the analyzed array only. For this purpose additional investments in infrastructure are not required.

But equipment of city infrastructure sensors and its technology upgrade allow to bring quality of functioning of a system to new level. Water supply, the power supply network, transport and other systems equip with sensors which transfer data on work of specific section of a system. The sensor does not interact with other sensors as it occurs in the concept of M2M at which all logic of processing of a signal and algorithms of corrective action "are sewn up" in directly in the device.

Main differences of the principle of work of M2M and Internet of Things (SAS)

Sensors of new type, to be exact counters (smart meters), in general does not interact with anything to which except the central system they transfer data. Stream technical data from all counters are analyzed in the mode of "the sliding window" that in the online mode to select important information for operational corrective action for a system. Usually it is assumed by accomplishment of simple feature set, such how search of extrema for a time window (maximum temperature of water in a system), aggregation (the number of the released cubic meters), incremental updating of metrics with receipt of new data (whether there are no abnormal indicators for "window"?). The greatest positive effect is reached at return of these results to the data warehouse and their further profound analysis in offline mode.

The combination of two types of analytics for data processing of sensors allows to understand what happens to a city system what operational solutions are necessary now how to react to the arisen situations. Such concept is called Internet of Things in city infrastructure allowing the organizations to increase profit due to improvement of operational efficiency, and to consumers to save due to consumption optimization.

IDC: Housing and public utilities in the USA at the very beginning of Big data use

The IDC Energy Insights company published in the spring of 2014 the report devoted to readiness of municipal services of the USA for work with Big Data technologies. Within the research IDC studied work of 760 American organizations, including 59 companies of housing sector with income more than $500 million. The report purpose – to help the companies to estimate the degree of availability for service with Big Data technologies.

Experts of IDC selected key criteria by which it is possible to estimate capability of the company to work with Big Data technologies. The report also contains recommendations about improvement of a situation with technologies of Big Data in this industry in short-term and long-term perspectives.

Authors of the report consider that readiness for use of Big Data technology consists of five components: desire, the saved-up data, adaptation of technologies, smoothly running processes and personnel. Success of the company in the field of Big Data equally depends on a maturity of the company in all these areas.

According to IDC, the utility sector is on initial stages of mastering of Big Data technologies. So, readiness of two thirds of the companies for work with the Big Data IDC estimates as "average". "Nizkaya" assessment of a maturity was received by four times more companies, than "high".

Today Big Data and analytics are applied in the field of utilities to the solution of a number of tasks. These technologies help to optimize power generation, operational efficiency and work with clients. The analytics allows to prepare beforehand for shutdowns and also to estimate the energy market, to predict demand and to conduct calculation of financial performance.

2019: The Moscow authorities buy data on movements of citizens from operators

On March 4, 2019 it became known that the Moscow authorities buy several years data on movement of citizens from mobile operators. On the basis of this information the mayor's office changes transport and infrastructure in the city. Read more here.

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