Three characteristics of big data

With the continuous development of technologies such as cloud computing and the Internet of Things, the role of Big Data in smart cities is also increasingly important. However, in fact, the data is originally a very important basis for urban governance, especially related to urban security. For example, demographic statistics, crime rates, and traffic flow data are all collected. The government’s governance unit will collect and analyze the data at regular intervals. If you can't understand the difference between big data and traditional data first, you can grasp the characteristics of big data analysis and tools. Even if you have big data, you may only be able to convert data into value. Contribution to urban security.

Understanding Big Data to Use Effectively Compared to traditional data, big data has at least three very different characteristics. The first is the volume of data. If it is converted into digital data units, the basic units are usually TB and PB. Not only must the cost of collection and storage be considered, but how to transfer such huge data quickly is also a key point for big data applications.

Second is the timeliness (Velocity). Even with such a large amount of data, it is still necessary to generate analysis results in the shortest possible time. For example, the traditional annual report statistics are often collected last year's data, but only published every other year. Wasted results often distort data analysis results.

The final and greatest difference is the variety of data (Variety). Traditional data is usually structured and has fewer options, such as age, gender, level, etc. However, big data may have various forms, including text. , video, images, web pages, etc., not only have no obvious structure, but big data often appear in the form of staggered phenomenon, such as Youtube's video in addition to the number of clicks, while there are message discussions.

It can be seen that traditional data collection methods can no longer meet the needs of urban security for big data. Fortunately, under the development of technologies such as Internet of Things (IoT), cloud computing, and 4G wireless broadband, it is necessary to obtain The data on the interconnection of objects, objects, and people and people is not a problem in technology. However, it is necessary to rapidly construct the infrastructure for collecting, transferring, and storing big data before it is possible to establish a comprehensive awareness capability and become a city security guard. The best backing for decision making.

But just getting information from the sensory layer is not enough, because if you want to do a big data in-depth analysis, you must be able to find answers to complex and open questions, and through visual analysis tools, through continuous screening With abstraction, you can gain insight into important information. However, the large number of semi-structured/unstructured data features of big data often cause the bottlenecks of the traditional relational database management system (RDBMS). It is necessary to introduce new big data analysis tools in order to be able to truly use the large data. data.

In addition, since the value of big data far exceeds that of traditional data, the truth, safety, and stability of big data must be valued. In particular, current web applications are ubiquitous. Airports, banks, MRT stations, railway stations, and hydropower supply and oil supply mechanisms are all potentially exploited by hackers. In addition, the government must be transparent in order to make the acquired data more valuable. The data use mechanism, when the more open the data of public utilities, the higher the chance of being invaded, so if you want to use big data to solve the problem of urban security, you must first do a good job of protecting big data. The introduction of technology and the configuration of professionals must not be overlooked.

The help of big data in urban public health and public security Many European and American cities have begun to collect and analyze large amounts of data and anticipate possible crises, which will then serve as a reference for urban security. For example, the research team of Christopher E.Mason, assistant professor of computational and systemic biomedical sciences at Weill Cornell Medical College in New York City, spent 18 months in carriages and stair railings at more than 400 subway stations in New York City. Samples were collected in seats, lamp posts, trash cans, etc. A total of 15,152 microorganisms were found, of which only 0.2% were from humans. Nearly half of the samples were unknown to humans, 27% were active and had antibiotic resistance. The bacteria, fortunately, only 12% of them can make people sick.

The PhthoMap research project also provides interactive maps through the Wall Street Journal website, allowing users to view the results of research at specific stations, such as the source of samples collected, the ratio of microbial sources, and the types and descriptions of bacteria. Using the type of bacteria to search for, and understanding the presence of these bacteria at those stations, it also demonstrates the open use of public health data.

Interestingly, in the course of the research, it was also found that the DNA found in some subway stations was in line with the population conditions around them. These are information that have never been thought of in the past. If they can be classified in the future and studied in depth, Urban public health protection work will be of great help.

The Los Angeles Police Department is introducing PredPol, a software used to predict locations where crime may occur. According to the PredPol (predictive monitoring Predictive Policing) team, the company first collected statistics on public crimes over the past 10 years and collected crimes from a large amount of news. The predictable criminal behavior was not only suicide but also crimes. , Including *kill, skeletonization, burglary, car theft, etc. According to the criminal behavior pattern in the aforementioned data, a unique computing system was developed, and the area with high probability of crime or even the next possible crime was displayed on the map. A 500-square-foot block is marked for reference, which is a typical example of applying conventional data to big data.

In fact, the security services in many cities already have a criminal record data file that has accumulated for decades, and they have even stepped up patrols in areas or places where the possibility of a crime is high. However, PredPol uses big data analysis technology to calculate 10 to 10 points from places where crimes are easy to nourish (such as bars where bully incidents occurred), multiple affected areas (such as communities that are frequently burglars), and neighboring areas of affected areas. The 20 most likely crime sites. PredPol claims that as long as the area is clearly marked, it only takes 5% to 15% of the past patrol time to stop more criminal activities.

At present, there are nearly 60 police stations in the United States that use Predpol, the largest of which is the Los Angeles Police Department and the Atlanta Police Department. The burglary in the Santa Cruz hollow gate in California dropped 11% in the first year of the system construction and the robbery even reduced 27%. After introducing PredPol in the Foothill area of ​​Los Angeles in 2011, the criminal rate dropped by 13% after 4 months. On the other hand, the area where PredPol was not introduced also increased slightly by 0.4%.

In 2012, a study of nearly 200 police stations in the United States pointed out that 70% of police stations plan to start or increase the use of PredPol-like predictive policing technologies in the next 2 to 5 years, including IBM, Palantir, and Motorola. Began to get involved in related fields.

Although the application of big data analysis technology in crime security is not yet 100% accurate, experienced ** may not necessarily need predictive policing techniques, but for the new police officers, the predictive policing technology It can help them get into the situation early, especially when the city's budget is tight, and the relative lack of manpower, the use of big data can obviously improve the efficiency of urban security.

More Data Linked to Generate More Value Since the construction of urban security, the importance of image surveillance has also increased. However, how large image data is analyzed has become a major problem for urban administrators. Fortunately, big data technology can analyze non-structured data such as video, allowing video surveillance data to be used effectively.

Big data can be said to be the basis for the operation of smart cities. In addition to urban security, other applications such as smart transportation and smart medical services also need to be based on big data, and these different types of data have more connections and naturally need to be more relevant. In-depth data analysis capabilities, such as the combination of smart transportation and smart security, can guide police and people to the scene of the accident in the shortest time, and can also see the potential of big data in urban security applications.