Smart city under big data

First, data collection

For example, focus on the collection and integration of geographic location data. When collecting urban data, Chicago must collect the corresponding geographical location information at the same time, thus forming a platform that can basically meet the dynamic real-time monitoring of the entire city. Similarly, New York and Singapore have also integrated demographic data and business data into geographic information repositories to inject basic nutrients into smart cities.

Another example is the implementation of automatic data collection and manual acquisition as a means of data collection. The core of Singapore’s “Smart Country 2025” program is “understanding” based on “connection” and “collection”. Real-time data is obtained through sensors all over the country, and the data is anonymized and then shared and analyzed to realize urban intelligent operation. Of course, this accurate, efficient, real-time automatic acquisition method is not suitable for all scenarios, and manual acquisition is necessary to assist if necessary.

For example, in the use of government-shared data to predict and troubleshoot illegal housing projects, New York requires inspectors to review complaint information, check whether the house is “lack of safe exits” or “exposed boilers”, and manually enter these data into the system. , manually upload the headquarters by mail. At the same time, New York also stipulated that inspectors should report data on the same day to ensure the speed of data update.

Data collection is like the sensory neurons responsible for information input. What data is collected and how it is collected is the logical starting point of a smart city. It is expected that Shanghai will accelerate the integration of spatial geodatabases and other urban databases, build an Internet of Things system covering the whole city, improve the enthusiasm, initiative, responsibility and data processing capabilities of data collectors, and improve the level of urban intelligence from the source.

Second, data sharing

If "zhimin" is the goal pursued by smart cities, then "access" is the precondition for "smart". This requires a data sharing system that functions as a contact neuron. The experience of global cities is that complete organizational mechanisms and legal guarantees are key elements in achieving data sharing.

In 2013, New York issued Executive Order No. 306, requiring all government agencies to cooperate with the government's chief data analyst to collect all city data to the city's data exchange platform. The prerequisite for any department to obtain data from other departments is to share their entire data set first.

In order to complete data sharing with minimal resistance, New York also retained the existing systems and networks in each department to the maximum extent, and data bridges and data element exchange projects were born. The data bridge has the functions of data sharing, management and statistical analysis, and can provide services for analysts of government agencies throughout the city. The data element exchange project is like a spider web, which connects all departments to realize real-time automatic data exchange.

Data sharing promotes the formation of internal government synergies and is the basis for the effective operation of smart cities. "Shanghai has comprehensively promoted the requirements of the "One Network General Office" to accelerate the construction of a smart government work program, and built a city data hub to realize data collection and sharing and sharing applications. This marks that Shanghai is planning and designing big data management and applications from the whole city level.

The establishment of the Municipal Big Data Center is a new initiative of the system construction, and is an important starting point for realizing data sharing in the city. It is recommended that big data centers can send CIOs to various departments to help achieve data sharing through organizational mechanism innovation. At the same time, it is necessary to further expand the coverage of government external networks, enhance the elastic carrying capacity of the “government cloud”, study data fusion and automatic standardization solutions, develop basic tools for decision analysis, and provide technical support for data sharing and scientific decision-making of various departments.

Third, data is open

Experience has shown that the data products formed by the public using open data will be partially integrated into the construction of smart cities, which will spark the spark of social co-governance. It can be said that data opening is the soul lever that incites the operation of smart cities.

New York enacted the Open Data Act in 2012, requiring all governments and their affiliates to open all government data except security and privacy by the end of 2018. “Smart Seoul 2015” proposes to build an open data plaza plan. For Shanghai, the implementation of the open approach to public data management will guide the full process management of data openness, and it is expected to further ensure the fairness, effectiveness and security of data openness.

Fourth, data utilization

Hong Kong, China Established the Innovation and Technology Steering Committee chaired by the Chief Executive to promote big data research and innovation, and set up a smart city office to coordinate smart city projects in various government departments and public and private institutions.

Chicago hired IBM's senior data experts as the city's chief analyst, and the Innovation and Technology Department also formed a data operations team consisting of data science, software development, and information security based on the functional needs of smart cities.

In general, global cities are basically demand-oriented to develop big data applications. Whether it is the illegal dumping of edible oil detection systems in New York's sewers, the illegal alteration of housing projects, or the large-scale monitoring system and space maps in Singapore, they are deeply developed from the perspective of public demand.

Under the conditions of the existing system and mechanism, Shanghai should further strengthen the construction of the talent team, fully tap the existing big data talents in various departments, and form a professional big data analysis team that is both technically savvy and knowledgeable in various industries.

Fifth, data security

For example, the usage bar "Exceptions" encourages government use of big data. Singapore enacted the Personal Data Protection Act in 2012. Although it is a law designed to regulate the use of personal information by private enterprises and institutions, it clearly states that the object of restriction is “organization”, and the annexes detail the exceptions for collecting, using and disclosing personal information without consent. The government used big data to pave the way for social governance.

Another example is the use of laws to regulate data regulation and relief behavior. In accordance with the Information and Communication Media Development Authority Act and the Government Technology Bureau Act, Singapore has reorganized and created the Information and Communications Media Development Authority's Technical Bureau to separate data utilization and data governance to ensure the neutrality of data governance.