Big Data Relieving Citizens’ Small Grievances for a Public Administration Innovation
Information System Division, Seoul Metropolitan Government

A. Problem Analysis

 1. What was the problem before the implementation of the initiative?
1. Growing City Problems That Defy Conventional Policy Approaches. The City of Seoul, whose population is over 10 million, has faced a host of problems found common in large metropolises, for many of the citizens have conflicting interests that are very difficult to mediate. For example, selecting late-night bus service routes that are most pertinent for intended citizens or reducing traffic accidents using limited resources is a task too complex to tackle through use of conventional, one dimensional method, for such tasks could affect many people of conflicting interests and agendas. 2. Lack of decision-making tools that are objective, rational and reliable. In the past solutions to such complex and controversial tasks were dependent on the opinions of experts or the discretion of civil officials in charge, and such practice often led to irrational decisions and thus failed to gain public support. 3. Paying little attention to big data . As information technology advances at an increasing rate and the realm of man’s activity continues to expand, big data has become an invisible part of our daily life. The city of Seoul alone produces over 100 Byte of data every day; such a vast amount of data is scattered around 500 some internal information systems, and most of the data were left unused or erased after a certain period. If data from municipal offices, city-funded institutions, and SNS were to be included, the amount of data erased everyday without any use would be enormous. 4. Lack of a data share base among different departments. Most of the City’s administration data remained unused and then were erased due entirely to lack of means to collect and compile data its different departments produced in a vast quantity everyday; and because of a lack of the means, all the municipal departments and institutions could not access each other’s data even if they wanted to; in fact, they had very little idea as to what data each other had and finding out about the types of data available at other departments was next to impossible.

B. Strategic Approach

 2. What was the solution?
1. The significance of the “Administration Innovation Through Big Data” Project. Big data is a term for any collection of data sets too large and complex to process through use of traditional data processing applications. With the advent of the digital age, the explosion of data is taking place in just about every aspect of human activity. It was not long before the city of Seoul started to use big data towards improving the lives of its citizens. The City already has developed a late-night bus service based on public transportation data and has plans for preventing or minimizing the damage caused by typhoons, floods, or other natural disasters through use of climate data. 2. The Big Data Project spearheaded by the current mayor of Seoul. Mayor Park Won-soon first brought out the possibility of big data as a source of valuable information and knowledge that can be applied towards developing proactive responses to emerging problems or making accurate predictions of changes to come. Accordingly, shortly after the inauguration as the mayor, he appointed a big data expert as the CIO in charge of the City’s ICT business, and launched “Administration Innovation Through Big Data”, a new project to make its policy decision making process more scientific and rational through use of big data and thus enhance the effectiveness and public confidence of its municipal services and programs. 3. Organization and work process realignment. As part of the Big Data Project, the City has created new units, reshuffled old ones, and realigned its work process towards effective implementation of the Project within the city government. First, two teams were created, “Data Planning” for planning project implementation and “Operation Support” for developing tasks and a share base infrastructure; then all the team members were undergone intensive training to strengthen their job competencies. In addition, the City developed a strategic big data process to its business and organization characteristics. Comprising five phases—task analysis, data preparation, model developing, model verification, and implementation—the process has proved instrumental to formulation of strategic and systematic solutions to common city problems it faced. 4. Laying a data share base. Next, the City has constructed a data share platform, named “Seoul-style Big Data Share Base”, through which to collect and share data many of its different departments and affiliated institutions produce on a daily basis as well as data it obtains from private businesses and institutions. The main intention behind the construction of the data collection and share system was to implement “Taxi Matchmaking” and “Public Transportation Card Data” programs. As a part of the City’s efforts to make the daily life of its citizens convenient through collection and use of its data scattered around its organization, the “Taxi Matchmaking” service seeks to help both citizens and taxi drivers find each other more easily through analysis of data accumulated from the taxi sensor that keep track of where, when passengers were picked up in conjunction of weather data. 5. Fostering “Big Data Curators”. Aware of a lack of qualified personnel to manage the explosive growth of data and apply its big data to development of municipal policies, the City carried out a project of training into “Big Data Curators” college graduates or young adults in need of employment. Trained to formulate big data strategies and lead system construction, they focus on identifying tasks in the realm of transportation, public welfare, economy, culture, and other that can be dealt with more effectively through use of big data and supporting the municipal department and institutions in developing more rational and effective policies. 6. Pursuing “Open Government 3.0” in step with “Government 3.0” . “Government 3.0” is a new national policy the central government of Korea pursues to create more jobs and develop public services to the needs of the people through opening and sharing of public information and elimination of the boundaries between agencies and departments. As one of the autonomous regional governments, the City also has conducted its municipal business in sync with the central government’s policy direction through “Open Government 3.0”. To open and share public information, the City has constructed “Seoul Open Data Plaza”, a service releasing via the Internet all administration information produced from administration activities. The Big Data Project is an extension of the City’s effort to share not only its public information but also the data produced by private businesses as well as its affiliated institutions that can be utilized towards improving the quality of the lives of its citizens.

 3. How did the initiative solve the problem and improve people’s lives?
1. Selecting late-night bus service routes based on citizens’ needs and opinions. There had been general needs among Seoul citizens for late-night public bus services for some time; but knowing which routes to serve and how often each selected route should be served had been potentially too controversial issues for the City to solve with conventional public decision making methods until the availability of big data. To solve the issues, city official and private experts in the public transportation field got together many times, discussing what data to use, where to get them, and how to use them, and finally settling on the best routes on which to run a limited number of buses available. Identifying the patterns of citizens moving from places to places by hour was the key to the selection of optimum routes. 2. Offering the optimum service at reasonable costs thanks to big data. Of course the City had used data for formulating its municipal policies before, but the data used were more or less of statistical nature. By bringing a new approach to using data into the arena of governing, the City now makes more scientific and rational decisions in its policy implementation. In comparison with similar services in London, Paris, and other metropolises, the City’s late-night bus service serves nine routes statistically verified as most relevant, thereby making the most out of limited resources.

C. Execution and Implementation

 4. In which ways is the initiative creative and innovative?
1. Discussing project tasks between Big Data Team and Transportation Policy Department (March 2013). The first big data-based program was the late-night bus service as it was deemed to have immediacy, especially with regard to working women and the handicapped. Big Data Team and Transportation Policy Department held many meetings through which both agreed that the routes for late-night bus service the department has been considering needed to be revised based on the actual numbers of the citizens who would benefit most from the routes once selected. 2. Signing an MOU with KT (April 2013). Finding the big data useful for selecting the most pertinent routes was not easy. Knowing that the data showing the movements of citizens in late hours would be ideal, the two decided on the idea of using data created by people using their phones at or near bus stations, which led to taxi GPS data containing such information as when and where passengers got on and off and phone data as to where, when and how many people used their phones. To get such phone data, the City signed an MOU with KT, a leading telecommunication company, and received over 3 billion phone logs for free. 3. Forming cooperative ties with the central government on big data. The City and the Ministry of Science, ICT, and Future Planning of the central government agreed on selection of late-night bus service routes as a trial task to promote use of big data and pushed forward the task in unison. Under the basic administration principle of “Government 3.0”, the City and the central government cooperated with each other on technical and strategic aspects related to how to use big data in selecting the routes. 4. Laying a big data share base unique to Seoul City (Sept. 2013). Aiming to adopt the practice of big data analysis throughout its municipal organization by 2014, the City has constructed a big data share/use platform and plans to carry out follow-on tasks such as fusing social big data with location information and developing and analyzing new policy tasks.

 5. Who implemented the initiative and what is the size of the population affected by this initiative?
1. Cooperation between IT and administration units. In solving city problems through use of big data, cooperation between the IT department capable of processing and analyzing data and other departments in search of solutions to city problems of their own has proved to be the most important factor in the Big Data Project. If these departments had not shared among themselves the need to use big data and the willingness to work together towards common goals, the Project would have been impossible. Because of these, the IT and Transportation Administration departments were able to agree on the feasibility of using call trend big data and, after meeting online and off-line over 50 times, find the nine most pertinent routes. 2. Private partners. Formulating policies that are effective in addressing city problems takes not only public data but also a great deal of data that are scattered around various private sectors. For example, in the late-night bus service project in which big data was used for the first time, data from KT played a crucial role. In addressing issues related to public welfare, economy, culture and other areas, the City has found out that forming cooperative and strategic ties with private businesses early on is very important. 3. Working with groups of experts in various areas. The City also has realized early on that involvement of experts was necessary in program development, particularly for securing public confidence in the reliability of programs to be implemented. By forming an independent council comprising experts from many different fields for each program, the City was able to ensure objectivity in the decision making process and thus enhance public confidence in the programs it sought to implement. In the case of selecting locations for senior welfare centers, for example, a researcher from a reputable and independent social welfare research center participated and offered his expertise on how to use social welfare-related big data.
 6. How was the strategy implemented and what resources were mobilized?
1. Funding: Minimizing costs through MOUs with private businesses. In developing the late-night bus service, the City spent about KRW 20 million all together, most of which went into outsourcing for construction of a computerized system for the service; as for data and data analysis, the City signed an MOU with a telecommunication company and received them for free. According to the budget estimate report from its IT department, the system construction would have cost the City over KRW 10 billion if it had to pay for the data and the data analysis service. 2. Data sources: Securing data through cooperation with public and private institutions. As big data in diverse areas are the most important resource for the Big Data Project, the City has sought and succeeded in securing necessary data for free through formation of cooperative ties with the holders of the data: call data records from KT for the late-night bus service program; taxi operation data from Korea Smart Card for the “Taxi Matchmaking” program, and traffic accidents data from KoRoad for reducing traffic accidents. 3. Human and technical resources: through partnerships with non-profit organizations. Through its partnerships with non-profit institutions the City also has secured human and technical resources necessary to its big data-based programs. Big Data Professional Association advises the City on the use of big data for solving city problems, and research institutions at Seoul National University and KAIST provide their research facilities and manpower.

 7. Who were the stakeholders involved in the design of the initiative and in its implementation?
1. The scientific selection of late-night bus service routes. Anyone who knows what is like to catch a taxi after regular public transportation services end would appreciate the idea of a bus service that runs all night. This seemingly simple idea, however, has one sticky problem to be solved: knowing which routes would be most pertinent to people who regularly return to their homes late at night. Any public service would be considered a waste of limited resources if it fails to serve its intended target group. Thus began the “Late-night Bus Route Optimization” program which focused on identifying street sections with most foot traffic at night and grouping them into several bus routes. In the past, such routes would likely have been selected based on existing bus operation data and/or the discretion of city employees in charge. With the explosion of big data following the advances of information technology and a bit of open-mindedness towards unconventional policy approaches, however, the City has selected the routes most frequented by late-night commuters by using data gleaned from vast amounts of call data records and data containing use of smart cards by taxi passengers. 2. Selecting the best location for senior welfare service facilities. In response to an increasing population of citizens aged 65 and over, the City operates a number of facilities throughout its municipality offering diverse welfare programs. But for a lack of established policy guidelines on the provision of welfare service, the supply-and-demand gap between the City and its senior citizens had been widening. To reduce the gap, the City also has turned to the use of big data: By analyzing the census records of its citizens aged 65 and over in conjunction with other types of data by administration section showing their income levels, the availability of existing welfare programs, and the proximity of public transportation services with respect to their places of residence, the City has developed an accurate distribution chart of senior people in each of its municipal districts and gained reliable information about welfare services needed by location and service type. And by applying such information, the City was able to fine-tune the availability of existing programs and better serve the needs of its senior citizens by building welfare facilities in locations deemed most accessible to them. 3. Posting PR materials in the most effective places. The City produces and posts a great amount of PR materials to keep citizens informed of its business activity; while much of the materials are for the general public, some are for certain segments of citizens or citizens in certain age brackets. Prior to the use of big data, the City used to produce PR materials in numbers deemed appropriate and post them in any available places. But with the launch of the Big Data Project, the City now produces and posts PR materials in numbers and places derived based on big data analysis. For example, materials about youth job training are posted in areas most frequented by youths and young adults who are mostly likely to be interested in employment opportunities; materials about no-collateral, low-interest loans now are posted in low-income areas; and information materials concerning the safety of women returning home late at night are placed in areas with a high concentration of single working women. As such, through the use of big data, the City has been able to reach more of its target groups of citizens with a fewer number of PR materials. 4. An efficient big data application system established through construction of a “Big Data Task Development & Verification” process. Based on the experience and achievements it has gained from such big data-based programs as the late night bus service and the selection of locations for welfare facilities, the City has established “Big Data Task Development & Verification”, a process through which to verify if a certain city problem can be solved through use of big data. The process centers on analysis by experts of city problems facing each municipal department in terms of the applicability of such data sets as census, traffic, income, social data and application to policy formulation of the data derived in cooperation with the IT department. With the adoption of the process, the City was able to establish a big data application system focusing on solving city problems from the citizen perspective.

 8. What were the most successful outputs and why was the initiative effective?
1. Evaluating the late-night bus service. In order to see if appropriate routes have been selected for the late-night bus service, the City has used “T-test”, a testing method for finding out if the average of differences between sub groups is statistically meaningful. Through the use of “T-test”, the City has found out that, on average, the number of users of regular bus services drops 17% on rainy days, increases 20% on weekends and holidays, and drops 19% a day after weekends and holidays. These numbers also are found to be identical to the figures derived from the case of the late-night bus service, which proves that bid data have contributed to the scientific and rational selection of the bus routes. 2. Setting up a reliable monitoring system with the help of experts. The City constantly monitors the strategy development of all of its big data-based programs and evaluates their progress once implemented through use of the governing system it has formed with groups of industry experts. Through this system, for example, the City listens to experts’ opinions on the development of its strategies for improving existing taxi services and reducing traffic and supplements deficiencies if necessary. 3. Monitoring the use of the big data share base. As for optimum system usage, the City has established a system for monitoring the process of collecting, storing, processing, analyzing data within its big data share base. By monitoring the process, the City has been able to keep data omissions or errors to minimum and thus enable accurate data analyses and forecasts.

 9. What were the main obstacles encountered and how were they overcome?
1. Data securement and process: construction of a big data share base. One of the major problems in the use of big data was a lack of the technology necessary for processing and analyzing big data. To solve this problem, the City has constructed a big data share base that allows its various departments to share their own data with one another and effectively analyze shared data using the built-in analysis tool. Through use of this system many of its departments have been able to analyze and process data of their interest. 2. Selection of tasks and difficulty of solving: fostering of big data experts. Selecting tasks suitable for the Big Data Project among various city problems turned out to be a major challenge. The City thus launched a program of training people into “Big Data Curators” to analyze the suitability of target tasks and gather necessary data for selected tasks. By recruiting able bodies, mostly college graduates and corporate employees in search of better job opportunities, and training them in the areas of identifying and solving city problems through application of big data, the City has selected city problems and developed ways to address them in a satisfactory manner. 3. Tepid attitude among departments: Consensus build-up and cooperation system. The nonchalant attitude of many of the City’s departments towards the Big Data Project also proved to be another huddle to overcome. In the early stage of the late-night bus service program, for example, relevant departments had trouble seeing the necessity of using big data eye-to-eye and thus were less than cooperative in their efforts to push forward the program. To deal with a lack of enthusiasm and cooperation among departments, the City informed its employees of the utility of big data and the importance of synergetic cooperation among departments through a series of education sessions and seminars, thereby developing a workable consensus on big data throughout its organization.

D. Impact and Sustainability

 10. What were the key benefits resulting from this initiative?
1. Improving the quality of citizens’ lives through adoption of rational policies. The Big Data Project has so far proven effective in improving the quality of the lives of Seoul citizens, which can be attributed to its emphasis on the adoption of rational policies. The late-night bus service has been found to have served 5~10% more passengers than bus services running comparable routes; the increase can be translated into a significant amount of savings for each passenger who otherwise would have to take taxi home, not to mention an unquantifiable benefit as a result of safe returns home late at night. Encouraged by positive results from the bus service and other big data-based programs, the City plans to introduce a series of programs aimed at solving city problems in the near future, many of which will be unprecedented, including traffic forecast and futuristic road safety services. First, a traffic forecast service will be rolled out that provides traffic forecasts of tomorrow as in weather forecasts. By statistically analyzing big data containing the information of road traffic flows the City has accumulated over 10 years, the service will be providing future traffic updates by the hour and day. As such, by making rational policy decisions and offering information based on big data, the City is directly improving the quality of its citizens’ lives. 2. Offering citizens-centered administration services through formation of private and public cooperation. To successfully implement the late-night bus routes optimization program, the City has signed an MOU with KT, a telecommunication company, and thus established a channel through with it acquired statistical data on foot traffic. KT, which had been unable to utilize the data for fear of violating personal privacy, was able to heighten the utility value of the data by analyzing it for the benefit of the public and supplying to the City resultant statistical data containing no personal information. In short, a citizens-oriented service was made possible through the cooperation of the City and a private enterprise knowing exactly what each needed.

 11. Did the initiative improve integrity and/or accountability in public service? (If applicable)
1. The City’s big data-based programs being benchmarked by local and overseas governments. The number of governments local and overseas is steadily increasing benchmarking the City as a model information-oriented city and its big data-based programs. NESTA, an innovation charity organization based in the U.K., has visited the City for a tour of the late-night bus service system. Moreover, the City has visited the World Bank and the municipal governments of Taipei, Taiwan, and Jakarta of Indonesia, and made presentations on its cases of applying big data to municipal administration. 2. Sharing the “Seoul-style Big Data Process”. Innovating the administration of municipal business requires both technical and procedural factors. As for hardware and software supplies for analyzing big data, they are readily available, as global IT businesses offer them in various types. The administrative process necessary for the utilization of big data is another matter, however: as it involves dealing with private citizens with diverse, often conflicting, interest, it is difficult to develop the “right” process and apply it trial-free. The City was able to develop an optimum administrative process through the development and implementation of diverse big data-based programs, including the late-night bus service and the selection of ideal locations for welfare facilities. This so-called “Seoul-style Big Data Process” comprises five phases, 14 activities, and 41 tasks. Those nations and governments interested in innovating their decision-making approaches can take advantage of the Seoul-style Big Data Process and thus implement data-based rational policy decisions with a minimum of trials and errors.

 12. Were special measures put in place to ensure that the initiative benefits women and girls and improves the situation of the poorest and most vulnerable? (If applicable)
1. Problem solving through cooperation with stakeholders. One lesson the City has learned from its implementation of big data-based programs is that close cooperation between the IT department and other departments with relevant data is crucial to the success of the programs; by developing consensus and close ties among them through dozens of educational sessions and workshops, the City was able to develop quality programs and implement them to desired effects. Two other contributing factors were selection of tasks appropriate for big data-based programs together with the big data curators it has fostered through training and the formation of cooperative networks through which the City discussed and sought expert opinions on various issues with a number of reputable private and public institutions. 2. Communicating with citizens through big data. The core value of administration the City pursues with the promulgation by the central government of “Government 3.0” is communication with citizens: knowing what citizens want through use of data created by them and communicating with them about the findings. The main reason big data was first applied to development of the late-night bus and traffic accidents prevention programs is that the City has discovered, through the analysis of over 600,000 complaints its citizens have registered, that they were most concerned about the state of public transportation services available in the city. 3. Big data, indispensable for a better future. Big data has played a crucial role in helping the City develop public services that improve the quality of the lives of its citizens. In creating a safer and more convenient Seoul through prevention of traffic accidents, street floods, building collapses and through selection of optimum locations for parking lots, bus stops, and other public facilities, it has become clear that big data-based administration innovation is not an option but a must.

Contact Information

Institution Name:   Information System Division, Seoul Metropolitan Government
Institution Type:   Government Department  
Contact Person:   Kyung Gun Ma
Title:   Manager  
Telephone/ Fax:   82-2-2133-2966
Institution's / Project's Website:  
Address:   110 Sejongdaero, Jung-gu
Postal Code:   100744
City:   SEOUL

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