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A Study on an Urban Safety Index Model Using Public Big Data and SNS Data
- 1. A Study on an Urban Safety Index Model Using Public Big Data and SNS Data Young-Won Seo, Hyun-Suk Hwang, Palash Sontakke, Shao Xiaorui, Tea-Gun Jeon, Tea-Yeon Kim, Chang-Soo Kim ICONI 2017
- 2. ICONI 2017 02 03 04 1. Background 2. Approach & Goal 3. Related Work 4. Safety Index Model 1) Data Preprocessing 2) Model 3) System Design 5. Conclusion & Future Work 01 05 Index A Study on an Urban Safety Index Model Using Public Big Data and SNS Data
- 3. Background Recent Disaster and Accident Issues in Korea Avian influenza Traffic accident Regional Safety Index Deprecated Not reflect characteristics of regional feature 02 03 04 01 05 Heavy rain and earthquake Release once a year ICONI 2017A Study on an Urban Safety Index Model Using Public Big Data and SNS Data
- 4. Goal & Approach Analysis public data set Include feature of locality Release as file and API(OpenAPI) System design of disaster monitoring system for public data and SNS Data Interactive disaster monitor system for real-time state Push real-time to user using notification 02 03 04 01 05 open data set of Korea government ICONI 2017A Study on an Urban Safety Index Model Using Public Big Data and SNS Data
- 5. Related Works Regional Safety Index Measure of the level for safety at regional levels Social Big Board Classification by disaster type Work with Smart Big Board Seven fields and 5 level ( level1 – weakest ) Real-time SNS Collect and filtering Private System 02 03 04 01 05 Regional Safety Index ICONI 2017A Study on an Urban Safety Index Model Using Public Big Data and SNS Data
- 6. Workflow Workflow of an integrated model 02 03 04 01 05 ICONI 2017A Study on an Urban Safety Index Model Using Public Big Data and SNS Data
- 7. Urban Safety Index – Data Preprocessing Public Data Set Type Organization Attribute Weather Korea Meteorological Administration temperature(max,min,avg), Precipitation, Wind speed(max,avg), Humidity, Measuring point Air environment Korea Environment Corporation SO2, CO, O3, NO2, Find dust(PM10, PM25), Measuring point Traffic accident Traffic Accident Analysis System Datetime(year,month,day,hour), Address, Lat, lng, Count(Dead, Injury) Infection Korea Centers for Disease Control and Prevention Address, Count(enterohemorrhagic, heterogeneity, typhoid, paratyphoid, cholera, hepatitis, …) Fire National Fire Data System Address, Property Damage, Count(Injury, Fire, Caualities) Crime National Police Agency Address, Count(violence, intelligence, theft, transporation, …) … … … 02 04 01 05 03 ICONI 2017A Study on an Urban Safety Index Model Using Public Big Data and SNS Data
- 8. Urban Safety Index – Data Preprocessing 02 04 01 05 Merge as location and time 1. Standardize Coordinate system 2. Calculate data by format/period 3. Merge locality code - Area , address -> WGS84(lat,lng) - UTMK -> WGS84(lat,lng) 4. Merge by nearest point (latitude, longitude) - Weather measuring point: ASOS, Disaster - Air pollution measuring point - Average of Year, month, day - Summation of year, month - Korea Legal locality code - For matching with layout of map 03 ICONI 2017A Study on an Urban Safety Index Model Using Public Big Data and SNS Data
- 9. Urban Safety Index - Model Model evaluation – Statistic public data with weather and air environment 02 04 01 05 Type Related Attribute Logistic regression Decision Tree SVM Neural Network Traffic accident temperature, find dust, humidity, wind direction, wind speed 34% 34% 45% 40% Fire temperature, wind direction, humidity, wind direction 37% 38% 42% 33% Suicide maximum temperature, find dust, wind speed, humidity 35% 43% 40% 29% Infection minimum/maximum temperature, wind speed, find dust, humidity 31% 36% 39% 29% Safety accident temperature, precipitation, find dust, humidity, wind speed 30% 23% 36% 28% 03 ICONI 2017A Study on an Urban Safety Index Model Using Public Big Data and SNS Data
- 10. Urban Safety Index - Model Model evaluation – Detailed traffic accident data with weather and air environment 02 04 01 05 Algorithm Accuracy Logistic regression 45% Decision Tree 42% SVM 40% Neural Network 53% 03 ICONI 2017A Study on an Urban Safety Index Model Using Public Big Data and SNS Data
- 11. Urban Satefy Index - SNS Data Collect and preprocessing 02 04 01 05 1. Collect Tweet using Twitter stream API 3. Send to client (Browser/Mobile App) 4. Store to database - Filtering by keyword of disaster ex) fire, traffic accident - Include lat,lng - Using CNN for sentence classification - Problem of Korean language for NLP - Using test data and just count - Send to view using web socket - Store in json format 2. Classification disaster type/positive or negative 03 ICONI 2017A Study on an Urban Safety Index Model Using Public Big Data and SNS Data
- 12. ReactJS (Docks/Redux) System Design - Architecture RESTful (OAuth) Client ncigmap … Middleware Tweet Collector Twitter Stream API Twitter Analyzer(NLP) echart Server Google map Router Logger Thunk MySQ L Monolitic, MVC Database SNS Public Data Weather Transportation Fire MongoDB / Redis Public Data Collector Wrapper API Auth Favorite Location View Static User Admin 02 03 04 01 05 Location … ICONI 2017A Study on an Urban Safety Index Model Using Public Big Data and SNS Data
- 13. System Design - App 02 03 01 05 04 Main Safety Index Menu ICONI 2017A Study on an Urban Safety Index Model Using Public Big Data and SNS Data
- 14. System Design - App 02 03 01 05 04 Safety Index Traffic Accident Fire Tweet ICONI 2017A Study on an Urban Safety Index Model Using Public Big Data and SNS Data
- 15. Conclusion & Future Work Conclusion Future Work Improving classification performance with granular data Calculated the correlation between the weather and various disaster data of public data set. 02 03 04 01 05 Implements disaster monitoring system using public data set. Collect public data and preprocessing for analysis. Processing SNS Data using NLP Using crawling data on the portal or government system with API ICONI 2017A Study on an Urban Safety Index Model Using Public Big Data and SNS Data
- 16. 02 03 04 01 05 Thank you ICONI 2017A Study on an Urban Safety Index Model Using Public Big Data and SNS Data
Editor's Notes
- #2: Hi, My name is youngwon, seo, I work at nc infotech in korea, busan. I am glad to meet you. today, I’m going to present about Urban safety index model using public data and building disaster monitoring system that title is “A study on an urban safety index model using public big data and sns data.
- #3: Looking at the table of contents, I’m going to talk the background, approach and goal, related work, safety index model, and finally conclusion and future work.
- #4: In recent years, there have been various issues of disasters in Korea. Typically, natural disasters such as floods and earthquakes have caused unpredictable and unexpectable, and it has the potential to cause human casualties. also, infection such as avian influenza have become an issue whenever they happen, but they tend to be less responsive or lack attention if they are not. Therefore, the South Korean government created a safety index model in 2014, using Statistical data of disasters and accident. and it is called regional safety index. It has been ranked according to the types of disasters occurring in the region, so that each local government can identify and manage state of safe within the there region. However, there is also some evidence that the recent issue of the recent issue of the disaster is difficult to deal with. The reason is that regional safety indices are designated as accident statistics based on the basis of the accident statistics, and therefore, they do not reflect the reflection and reflection of the sudden disaster. For example, it was based on statistical data of accident, Except for the classification between rural and urban areas, The dangers of traffic accidents and fires have resulted in safer rural villages than cities.
- #5: so, in this paper, presents a prototype safety model based on data that include regional characteristics, and a visualization system for this model. This study uses public data set that contains regional charactuerstics. Public data has been disclosed for the purpose of ensuring access to public data by data provided by each governmental agency, and for the creation of public data through private utilization. After this, it presents a system design for model visualization. It provide interactive visualization and real-time notification,
- #6: As related studies, there are regional safety index and social big board. The regional safety index are quantified to the level of safety of local governments, and it use related statistical data. Risk indicators, weak indications, mitigation indicators, and calculated according to the given formula. Areas of natural disaster use local safety diagnosis results. The index is divided into five categories and provides a safety index for seven areas including fire, traffic, and crime. However, we have been released once a year and have been unable to reflect on the reflection and reflection of real time. The Social Big Board is a monitoring system that leverages the data from the government's social networks to cope with real-time situations rather than just a single year index. This is a system that enables real-time data processing, such as collecting disaster related data from SNS and categorizing disaster types. It is associated with a traditional disaster monitoring system, a traditional disaster monitoring system, and can only be used by public institutions.
- #7: The entire process of this thesis and future research is as follows : Data collection and pre-processing, second model generation, third system development and model application stages exist.
- #8: First, data preprosesing step. We have searched various data to create a model. We collected data on weather, traffic accidents, fire, crime, and population. For develop the prototype model, I used the following data.
- #9: After collecting the data, proceed to merge the related data pairs. First, unify the coordinate format. Actual data has various coordinate formats, and coordinates may not exist, so we converted it to wgs84 latitude and longitude using software or location api. Second, proceed with calculations as appropriate for the unit and period. It is a task to calculate the number of days data or to match the timestamp of the data. Third, match the Korean code for visualization because the code for the Korean map is made up of the corresponding code. Fourth, the merge of the data, that is, the distinction to the data generated at the closest proximity and nearest location.
- #10: Next, we apply the following algorithms to evaluate the preprocessed data. The following is an evaluation of the year’s data, and It it divided by gu layer in city. I found and evaluated the associated properties, but I could not expect high performance. The reason is that it is only preprocessing, and the accuracy and the number of the data are few and it has NA.
- #11: The following is a model for evaluating the relationship between weather and detailed traffic accident data. This data has more detailed information than other data, but it also has 50% accuracy because of the small amount of data.
- #12: In this study, SNS was used for real-time model. Use the Twitter streaming API to get relevant tweets and visualize them. To get a tweet, we filter through disaster-related keywords and analyze the disaster type or positive denial. All of this depends almost entirely on natural language processing, which is not discussed in detail here. I tried to visualize the map using only test data.
- #13: It is System design for visualization. It focuses on scalability and is largely divided into clients and servers. The client used react and the server was express and spring based. It acts asynchronously by creating a restapi for accessing the data to interactively load the data. To increase the data and scalability of the system, nosql, which is easy to shard, was used.
- #14: The screen for the results of the system development. The client for the prototypes was developed as a mobile version. You can monitor the information about the layers and public data for each local region of the region.
- #15: It is View of Safety Index, Traffic Accident, Fire and Tweet.
- #16: Here's the final conclusion. In this study, public data were first collected and pre-processed for the visualization of safety indexes and safety index models. Preprocessing has merged data into the correct location and time and processed the abnormal values. Later, several algorithms were created to generate and evaluate models and develop a visualization system. The most important thing in this study is the quality and quantity of data. I tried to build a prototype of the relationship between the weather and transportation, but it didn't perform well. In further studies, the system development is intended to address the performance improvement of models and SNS through the collection of better quality data.
- #17: Here's the final conclusion. In this study, public data were first collected and pre-processed for the visualization of safety indexes and safety index models. Preprocessing has merged data into the correct location and time and processed the abnormal values. Later, several algorithms were created to generate and evaluate models and develop a visualization system. The most important thing in this study is the quality and quantity of data. I tried to build a prototype of the relationship between the weather and transportation, but it didn't perform well. In further studies, the system development is intended to address the performance improvement of models and SNS through the collection of better quality data.