The Construction of Open Data Portal using DKAN for Integrate to Multiple Japanese Local Government Open Data

In recent years, the Code for Japan, a civic tech community in Japan, has focused on the context of the FOSS4G. Consequently, the Japanese have published open data in more than 150 local governments, but these data are almost simply provided as a file on their website. And also CKAN portal are used less than 20 cities. In this study, we built open data platform that uses DKAN for integrated open data distribution of Japanese local governments

Cross-comparative analysis of evacuation behavior after earthquakes using mobile phone data

Despite the importance of predicting evacuation mobility dynamics after large scale disasters for effective first response and disaster relief, our general understanding of evacuation behavior remains limited because of the lack of empirical evidence on the evacuation movement of individuals across multiple disaster instances. Here we investigate the GPS trajectories of a total of more than 1 million anonymized mobile phone users whose positions are tracked for a period of 2 months before and after four of the major earthquakes that occurred in Japan. Through a cross comparative analysis between the four disaster instances, we find that in contrast with the assumed complexity of evacuation decision making mechanisms in crisis situations, the individuals' evacuation probability is strongly dependent on the seismic intensity that they experience. In fact, we show that the evacuation probabilities in all earthquakes collapse into a similar pattern, with a critical threshold at around seismic intensity 5.5. This indicates that despite the diversity in the earthquakes profiles and urban characteristics, evacuation behavior is similarly dependent on seismic intensity. Moreover, we found that probability density functions of the distances that individuals evacuate are not dependent on seismic intensities that individuals experience. These insights from empirical analysis on evacuation from multiple earthquake instances using large scale mobility data contributes to a deeper understanding of how people react to earthquakes, and can potentially assist decision makers to simulate and predict the number of evacuees in urban areas with little computational time and cost, by using population density information and seismic intensity which can be observed instantaneously after the shock

A Study of the Development and Distribution of Open Geospatial Data in Japanese Local Governments

Since the end of the last decade, the use of open data (secondary use and machine-readable formats) has emerged as a political and cultural movement for the realization of citizen participation. Open government, citizen participation, transparency in government affairs, and cooperation of public and private entities were established as goals by the Obama administration in the U.S. in 2009. In the “G8 Open Data Charter,” which was declared at the G8 Lough Erne Summit in June 2013, geospatial information data was recognized as an area of high value. In addition to open data policy, data flow is a necessity; for example, the CKAN platform with data catalogs have been developed as open source with the provision for the flow of information. Various policies and government strategies on open data have been enforced since 2012 in Japan including the introduction of various guidelines and standard government terms and conditions Visualization being an important aspect of geographic information, the use of various tools, such as FOSS4G, is required. On the other hand, since the formats of open data currently vary, a cross-evaluation is necessary to determine the usability of the available data, especially in the case of geographical information comprising of latitudes and longitudes, as well as readable mechanical data. The format in which governments use or distribute data—in addition to desktop GIS and web GIS—is particularly important, as general-purpose tools are also important requirements for using open data. Based on Japanese trends on open data policies/datasets/activities in recent years, the purpose of this study is: first, to compare the extent and circumstances surrounding the openness of geospatial information in Japan, and; second, to analyze especially open source platforms and applications for using open data. The classified data categories of Japanese open data are, population and statistical data account for more than 20% of total open data, while the next most common category, public relations, accounts for about 16% of government information. The geographic data format is primarily distributed for disaster prevention, education, and tourism sectors, as much of the original data and urban planning diagrams contain positional information regarding facilities. In recent years, the Code for Japan, a civic tech community in Japan, has focused on the context of the FOSS4G. Consequently, the Japanese have published open data in more than 100 local governments; this data is simply provided as a file on the website of the local governments. However, the staff in the technology department of Japanese local governments introducing platforms such as CKAN and the information policy issues is insufficient. The other hands, CKAN and NetCommons (Japanese open source CMS) have been readily adopted in some local governments, such as Fukuoka City and Shizuoka Prefecture. In addition, some local governments provide open geospatial data using the OSM platform. Therefore, an increase in programs that combine enhancements (more provide and use case) and platforms that offer easy access to open geospatial data is necessary

Human Mobility Patterns for Different Regions in Myanmar Based on CDRs Data

Sustainable urban and transportation planning depends greatly on understanding human mobility patterns in urban area. Myanmar is one of the developing countries in ASEAN. It develops more rapidly as compare past years due to its international trade policy change and faces serious traffic problem in major cities. To solve these problem, human mobility pattern need to know for improvement. Therefore, this paper focuses to analyze different human mobility patterns for the different regions in Myanmar by using Call Detail Records (CDRs) Data. Such studies could be useful for creating transport model of mobility pattern. The numbers of trip generated are obtained by using CDRs over seven days period. CDRs of each region can be used to generate trip numbers of townships within certain time frame and time windows. In this study, average distance travelled, preferred days of long distance users and human mobility patterns at different times of weekdays and weekends in Yangon and Mandalay were analyzed. People living in Yangon area are generally more travelled than Mandalay on weekdays and weekends. The results indicated the similarities and differences in mobility patterns for both cities. This information is very useful for transport planning and future transportation developments

Predicting Evacuation Decisions using Representations of Individuals' Pre-Disaster Web Search Behavior

Predicting the evacuation decisions of individuals before the disaster strikes is crucial for planning first response strategies. In addition to the studies on post-disaster analysis of evacuation behavior, there are various works that attempt to predict the evacuation decisions beforehand. Most of these predictive methods, however, require real time location data for calibration, which are becoming much harder to obtain due to the rising privacy concerns. Meanwhile, web search queries of anonymous users have been collected by web companies. Although such data raise less privacy concerns, they have been under-utilized for various applications. In this study, we investigate whether web search data observed prior to the disaster can be used to predict the evacuation decisions. More specifically, we utilize a "session-based query encoder" that learns the representations of each user's web search behavior prior to evacuation. Our proposed approach is empirically tested using web search data collected from users affected by a major flood in Japan. Results are validated using location data collected from mobile phones of the same set of users as ground truth. We show that evacuation decisions can be accurately predicted (84%) using only the users' pre-disaster web search data as input. This study proposes an alternative method for evacuation prediction that does not require highly sensitive location data, which can assist local governments to prepare effective first response strategies.Comment: Accepted in ACM KDD 201

City2City: Translating Place Representations across Cities

Large mobility datasets collected from various sources have allowed us to observe, analyze, predict and solve a wide range of important urban challenges. In particular, studies have generated place representations (or embeddings) from mobility patterns in a similar manner to word embeddings to better understand the functionality of different places within a city. However, studies have been limited to generating such representations of cities in an individual manner and has lacked an inter-city perspective, which has made it difficult to transfer the insights gained from the place representations across different cities. In this study, we attempt to bridge this research gap by treating \textit{cities} and \textit{languages} analogously. We apply methods developed for unsupervised machine language translation tasks to translate place representations across different cities. Real world mobility data collected from mobile phone users in 2 cities in Japan are used to test our place representation translation methods. Translated place representations are validated using landuse data, and results show that our methods were able to accurately translate place representations from one city to another.Comment: A short 4-page version of this work was accepted in ACM SIGSPATIAL Conference 2019. This is the full version with details. In Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. AC

RDD2022: A multi-national image dataset for automatic Road Damage Detection

The data article describes the Road Damage Dataset, RDD2022, which comprises 47,420 road images from six countries, Japan, India, the Czech Republic, Norway, the United States, and China. The images have been annotated with more than 55,000 instances of road damage. Four types of road damage, namely longitudinal cracks, transverse cracks, alligator cracks, and potholes, are captured in the dataset. The annotated dataset is envisioned for developing deep learning-based methods to detect and classify road damage automatically. The dataset has been released as a part of the Crowd sensing-based Road Damage Detection Challenge (CRDDC2022). The challenge CRDDC2022 invites researchers from across the globe to propose solutions for automatic road damage detection in multiple countries. The municipalities and road agencies may utilize the RDD2022 dataset, and the models trained using RDD2022 for low-cost automatic monitoring of road conditions. Further, computer vision and machine learning researchers may use the dataset to benchmark the performance of different algorithms for other image-based applications of the same type (classification, object detection, etc.).Comment: 16 pages, 20 figures, IEEE BigData Cup - Crowdsensing-based Road damage detection challenge (CRDDC'2022