In this paper, we did not generate the noise filter in advance, and we focused on the noise reduction method which automatically generates and uses the filter using only the noise reduction targeted tweets. Specifically, in each targeted tweet of noise reduction, a morpheme n-gram analysis of only nouns was performed, those with a high combination frequency of nouns were generated as noise filters. The results suggest that there is a certain degree of effects of noise reduction method under the condition of morpheme 4-gram of noun and frequency threshold 3 or 5 in the case where the number of tweets to be processed is at least about 110 or more.

In developing countries, it is difficult to grasp the living condition of people because there is no detailed data on residential status. Especially, it is difficult to grasp the condition of poor people because some of them live in illegally occupied areas. In this research, therefore, the deep learning model to grasp the residence of poor people at the building level from satellite image and household survey data was developed. This model can classify buildings into three levels: poor, middle and rich. Three methods for creating labeled training data were considered and the influence of building area, land use and elevation data on estimation accuracy was also considered. The accuracy of the method with the highest estimation accuracy was 81.8%. The result can be visualized by using GIS and it helps people to understand where many poor or rich people live.

In recent years, Japan has developed long-distance cycle routes as part of tourism promotion policy. Cycling tourists often take these routes for inter-destination movement. However, it is difficult to gather data concerning such types of movement with existing investigation methods such as questionnaires. This study examines the effectiveness of volunteered geographic information (VGI) for analyzing inter-destination movement of cycle tourists and regional factors affecting the movement. Hokkaido was selected as a suitable targeted area. As the result, comparing the behavioral indicators derived from VGI and existing questionnaire survey, we could show that VGI were effective in capturing the actual condition of cycle tourist movement. In addition, analyzing the regional factors affecting cycle tourist movement based on Spatial Autoregression Model allowed us to detect important regional factors, including coastline, trail station and accommodation.

Recent achievement of the Internet of Things (IoT) have brought a great opportunity for GIS to be integrated with physical space as Cyber-Physical Systems (CPS). This article reviews the concepts of IoT and CPS from the perspective of geospatial spaces and clarifies the complementary relationshp of the two technologies. Two forms of CPS were discussed: Pseudo-CPS (PCPS) which projects spatial data on a display without interaction among data sources and True-CPS (TCPS) with handles data sources deeply and semantically. While PCPS is widely industrialized TCPS is in its infant, lack of common schemes and platforms for data sharing, security and privacy etc. Dissolving the barriers requires collaborations cross sectors and disciplines. A transformation of our mindset on GIS from ‘location + attributes’ to ‘location×attributes’ is recommended for creating value-added GI services.

The 5D World Map System, the Mathematical Model of Meaning technique (MMM) and their applications create new analytical system environments with the SPA concept (Sensing, Processing and Actuation) for the "Global Knowledge Sharing, Analysis and Visualization". 5D World Map System is a knowledge sharing, analysis and visualization system for realizing Global-Environmental Analysis with the integration of various environmental phenomena. We have proposed the 5D World Map System to collect and facilitate various social and natural-environmental information resources, which are characteristics of infrastructure, disaster, traffic, water and energy supply. The collaborative research activities are promising for approaching to the global architecture of a multi-visualized knowledge analysis and sharing system "5D World Map System," applied to "environmental data analysis and visualization. The basic dimensional space of this system consists of a temporal (1st dimension), spatial (2nd, 3rd and 4th dimensions) and semantic dimensions (5th dimension, representing a large-scale and multiple-dimensional semantic space that is based on our semantic associative computing model (MMM: The Mathematical Model of Meaning)). This system is effective to make a progress of "global-environmental analysis" with cyber-physical system integration for detecting and analyzing natural and social environmental phenomena. The 5D World Map System is globally utilized as a Global Environmental Semantic Computing System for SDGs 9, 11, 14, United-Nations-ESCAP.

In addition to big data and artificial intelligence, Internet of Things (IoT) is a new technology which is gaining much attention from wide range of industries in recent years. Since the data collected by the IoT sensor is related to the position information, it can be said that IoT and GIS are strongly related.

GIS combines spatial and sensor data based on position information, and extracts new information. As diverse spatial data plays an important role to extract various information, spatial data infrastructure is required. Spatial data infrastructure is the fundamental data developed by various organizations, and provided through electronic media or networks.

In this paper, IoT applications used in various fields and spatial data used therein are cited, and the utilization situation, tendency of spatial data and its infrastructure are discussed.

This paper aims to discuss a society that assumes digital technologies such as IoT, AI, drones and autonomous driving are penetrated. In such kind of society, these technologies are used for solving social problem by integrating cyber-physical space (SCPS). The authors focus on (1) digital technology as a tool to solve social problems like as lack of labor, (2) elements of geospatial technologies and (3) diversifying management agencies and services. High precision three-dimension map and positioning system, indoor positioning system, SfM by cloud service and next generation mobile communication system are expected to be necessary tools to develop future society. Especially, examples on elemental and integrated technologies related to next generation mobility by drone and autonomous driving, agriculture and emergency responses are introduced. Finally, issues to be considered are raised as followings; (1) regulatory sandbox, (2) dual-use and (3) development of new market.

Civil infrastructure which supports our lives exists in every field of physical space. In order to continue to use this enormous civil infrastructure, it is necessary to plan and process civil infrastructure management in which finding damaged parts and making appropriate repairs. However, human and budget resources have been limited to realize sufficient management in the current situation. As a breakthrough, the realization of the smart infrastructure management by fusion of IoT which can monitors and analyzes everything in the physical space with GIS which can link physical space and cyber one is expected. In this paper, we discuss the possibility of civil infrastructure management by making full use of IoT in various physical spaces, and the needs of GIS.

In recent years, IoT (Internet of Things) is leading the trend of industry. Those systems gather various data from sensors and surveillance equipment, accumulate data in the cloud, make full use of large-scale database with the support of spatial analysis. On the other hand, connecting sensors and surveillance equipment via the Internet increases the risk of cyber-attacks and security damage to social infrastructure because of the big differences in Operation systems (OT) and Information systems (IT). This paper reviews the background of IoT in industry, reports the current status of security in IoT×GIS by cases of security damages, and discuss the challenges to future GIS for the management of social infrastructure.