Optimizing taxi parking spaces in urban environments is crucial for reducing customer wait times and enhancing operational efficiency. Traditional approaches, based on static statistical methods and relying on Points of Interest (PoIs), often fail to address dynamic demand patterns, particularly with the rise of app-based ride-hailing services. This paper introduces a data-driven framework combining automatic segmentation and association rule mining techniques to dynamically align taxi parking spaces with demand zones. Using DBSCAN for parking cluster identification and the Apriori algorithm for correlating these clusters with high-demand areas, our methodology demonstrates a practical solution. Experimental results reveal positive alignment between identified parking spaces and manually validated demand areas, highlighting the reliability of our approach in modern urban transportation systems.

Accurate demand prediction in target areas is critical for an optimal taxi placement systems. This accuracy relies heavily on the correctness of the spatial index because predictions are directly runned on this spatial representation. However, conventional spatial index methods often fail to fully capture the dynamic patterns of urban demand, leading to inefficiencies in fleet management and service delivery. In this paper, we propose a novel methodology that integrates demand hotspots into an adjusted spatial grid. We demonstrate the implementation of this approach in a simulated environment and evaluate it against conventional spatial index. The results show that our proposed spatial index has more accurate representation of demand while having acceptable performance, leading to better taxi placement. This strategy is scalable and adaptable to various urban settings in the field of modern transportation.

Cross-cultural religious tourism is computational to promote cross-cultural communication and understanding according to impression distance. Our motivation to implement semantic search with an emotion-oriented context into the proposed system is to realize global tourism recommendations expressed in different cultures. The objectives of this paper are (1) to find the religious places by using the tourist’s emotional distance, (2) to find similar religious places not only in the same culture but also in the different cultures with the tourist’s emotional distance calculations. Experimental results demonstrate the feasibility and applicability of this method.

The significant computation in global environmental analysis is "context-oriented semantic computing" to interpret the meanings of natural phenomena occurring in the nature. Our semantic computing method realizes the semantic interpretation of natural phenomena and analyzes the changes of various environmental situations. It is important to realize global environmental computing methodology for analyzing difference and diversity of nature and livings in a context dependent way with a large amount of information resources in global environments. Semantic computations contribute to make "appropriate and urgent solutions" to the changes of environmental situations. It is also significant to memorize those situations and compute environment changes in various aspects and contexts, in order to discover what are happening in the nature of our planet. We have various (almost infinite) aspects and contexts in environmental changes, and it is essential to realize a new analyzer for computing the meanings of those situations and making solutions for discovering actual aspects and contexts. We propose a new method for semantic computing in our Multi-dimensional World map. We utilize a multi-dimensional computing model, the Mathematical Model of Meaning (MMM) [1–3], and a multi-dimensional space with an adaptive axis adjustment mechanism. In semantic computing for environmental changes in multi-aspects and contexts, we present important functional pillars for analyzing natural environment situations. We also present a method to analyze and visualize the highlighted pillars using our Multi-dimensional World Map (5-Dimensional World Map) System. We introduce the concept of "SPA (Sensing, Processing and Analytical Actuation Functions)" for realizing a global environmental system, to apply it to Multi-dimensional World Map System. This concept is essential to design environmental systems with Physical-Cyber integration to detect environmental phenomena in a physical-space (real space), map them to cyber-space to make analytical and semantic computing, and actuate the analytically computed results to the real space with visualization for expressing environmental phenomena, causalities and influences. This system currently realizes the integration and semantic-analysis for KEIO-MDBL-UN-ESCAP Joint system for global ocean-water analysis with image databases. We have implemented an actual space integration system for accessing environmental information resources and image analysis.

The detection of deforestation by remote sensing technologies has been one of the most important research issues in forest monitoring over the last decades. However, only identifying the area of change is usually not sufficient to understand how critical the effects are on the environment including increased CO2 emissions, loss of biodiversity, and soil degradation. To interpret the causes of the detected forest loss and the full impacts upon an ecosystem, additional expert knowledge is required. Traditionally the environmental standard classifies the measurement value, as called parameter value, from the environmental sensor into several condition categories to presenting meaningful quantitative measures of environmental results and establishing whether or not the problem of environmental exists. There are several traditional calculations to measure the interpretation of environmental phenomena such as numerical approach as represented, e.g., by pattern matching that is supported by classical Boolean logic rule. However, in the Boolean logic rule, the truth interpretation values of parameters may only be the truth values, true and false in a category. This paper demonstrates the type of logical approach that has huge potential to assign the interpretation of environmental phenomena in where the truth value may fall in the range between completely true and completely false.

This paper presents a 5D World Map System's application for disaster-resilience monitoring as "Environmental AI System" of each player's implementation of United Nation's SDG 9 and DGS 11 from global-level to regional-level, country-level, sub-regional-level and city-level. In Asia-Pacific, disaster risk is outpacing disaster resilience. The gap between risk and resilience-building is growing in those countries with the least capacity to prepare for and respond to disasters. Using the Sensing-Processing-Actuation (SPA) functions of 5D World Map System, a disaster risk analysis can be conducted in multiple contexts, including regional, national, and sub-national. At the regional, national and subnational levels, the analysis will focus on identifying disaster risk hotspots through incorporating existing multi-hazard disaster risk and socio-economic risk information. The system will further be used to assess future risks through integration of global climate scenarios downscaled to the region as well as countries. This paper presents the design of two new actuation functions of 5D World Map System: (1) Short-term warning with prediction and push alert and (2) Long-term warning with context-dependent multidimensional visualization, and examines the applicability of these functions by indicating that (1) will support both resident and those who are working at the operational level by being customized to disaster risk analysis for each target region/country/area, and (2) assist both policy-makers and sectoral ministries in target countries to use the analysis for evidence-based policy formulation, planning and investment towards building disaster-resilient society.

Deforestation is a major problem in ecosystem degradation and one of the main sources of carbon emission to the atmosphere. The use of multi-temporal satellite remote sensing has proven to be effective means to monitor forest conditions on global scale. The effectiveness of the utilization of remotely sensed images will depend on the analysis model and parameter selection procedure to provide information that meets the requirements of deforestation monitoring. Here we demonstrate the ability of semantic computing for analyzing satellite images that applied to interpreting tropical deforestation. The typical semantic computing works for interpreting numerical or textual data. It still remains challenging for utilizing remote sensing images for environmental monitoring in semantic computing since the data is presented in interval value. Therefore, we proposed interval matrix decomposition for automatically generate semantic projection that address uncertain value in environmental parameters. In this study, independent dimensions of Landsat Thematic Mapper imagery (Landsat-8) and The Phased Array Type L-band SAR-2 (PALSAR-2) were combined to create an integrated interpretation of environmental condition for a study area in the deforestation zone of global tropical forest. We proposed essential semantic interval-dimensions derived from heterogonous satellite images, combination of L-Band SAR and optical, namely: HV gamma-naught, red, green, blue, NIR, SWIR channel; and combinational features: soil temperature, soil moisture, temporal change density, temporal change velocity, shape and texture. Afterward, semantic computing is employed as analysis model to explain significant knowledge of deforestation activity. The experimental result shows that integrated independent dimensions from both the optical and SAR domains, has potential for presenting for aspect-based deforestation assessment and to enable the design of robust forest monitoring systems.

Deforestation is still a major nature phenomenon in our society. For assessing deforestation effect, satellites remote sensing provides a fundamental data for observation. While new remote-sensing technologies are able to represent high-resolution forest mapping, the application is still limited only for detecting and mapping the deforestation area. In this paper, we proposed a new method for retrieve the information contained on Satellite Multispectral images in order to interpreting deforestation effect in the context of soil degradation. We proposed an idea to interpret reflected “substances (material)” of bare soil in deforested area in spectrum domain into human language. The objectives of this paper are to (1) recognize the deforestation activity automatically. (2) Identify deforestation causes and examines the deforestation effect based on deforestation causes. (3) Scrutinize deforestation effects on soil degradation. (4) Representing nature knowledge of deforestation effect by performing calculation for semantic retrieval, to bring the clear comprehensible knowledge even for people who are not familiar with forestry. Semantic retrieval formed by understanding queries and showing queries result based on semantic calculation. As for experimental study, Riau Tropical Forest has been selected as the study area, where the multispectral data was acquired by using Landsat 8 Satellite between 2013 and 2014; Where forest fire and logging activities are reported, and detected.