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This paper introduces Virtual Urbanism (VU), a multimodal AI-driven analytical framework for quantifying urban identity through the medium of synthetic urban replicas. The framework aims to advance computationally tractable urban identity metrics. To demonstrate feasibility, the pilot study Virtual Urbanism and Tokyo Microcosms is presented. A pipeline integrating Stable Diffusion and LoRA models was used to produce synthetic replicas of nine Tokyo areas rendered as dynamic synthetic urban sequences, excluding existing orientation markers to elicit core identity-forming elements. Human-evaluation experiments (I) assessed perceptual legitimacy of replicas; (II) quantified area-level identity; (III) derived core identity-forming elements. Results showed a mean identification accuracy of ~81%, confirming the validity of the replicas. Urban Identity Level (UIL) metric enabled assessment of identity levels across areas, while semantic analysis revealed culturally embedded typologies as core identity-forming elements, positioning VU as a viable framework for AI-augmented urban analysis, outlining a path toward automated, multi-parameter identity metrics.

This study examines the impact of the 1923 Great Kanto Earthquake on population distribution within Tokyo City. The earthquake triggered massive fires that devastated nearly half of the city, including much of its urban core. To investigate its consequences, I digitized systematic census statistics and conducted regression analyses using variation in fire damage across areas. The results show that land readjustment implemented as part of the reconstruction project reduced residential land area within the burned area, leading to higher unit rents. Although the total residential floor area eventually recovered through the construction of multi-story dwellings, the population of the burned area remained below its pre-earthquake level throughout the period examined. In addition, the zoning system established before the earthquake had little effect on population redistribution. These findings suggest that post-disaster population distribution was shaped primarily by market-based price adjustments rather than institutional regulations. The analysis further shows that rising rents reduced the number of kinship households while increasing incentives for workers to rent rooms as lodgers. Th

In this paper, we introduce a dataset of multilingual news articles covering the 2021 Tokyo Olympics. A total of 10,940 news articles were gathered from 1,918 different publishers, covering 1,350 sub-events of the 2021 Olympics, and published between July 1, 2021, and August 14, 2021. These articles are written in nine languages from different language families and in different scripts. To create the dataset, the raw news articles were first retrieved via a service that collects and analyzes news articles. Then, the articles were grouped using an online clustering algorithm, with each group containing articles reporting on the same sub-event. Finally, the groups were manually annotated and evaluated. The development of this dataset aims to provide a resource for evaluating the performance of multilingual news clustering algorithms, for which limited datasets are available. It can also be used to analyze the dynamics and events of the 2021 Tokyo Olympics from different perspectives. The dataset is available in CSV format and can be accessed from the CLARIN.SI repository.

Recent advances in data collection and technology enable a deeper understanding of complex urban commuting, yet few studies have rigorously analyzed the temporal stability and Origin-Destination (OD) heterogeneity of route choice. To address this, we analyze one year of smartphone position data from over one million users in the Tokyo metropolitan area to extract high-resolution commuting trajectories. Our methodology is twofold: First, we develop algorithms to process raw position data, accurately extracting the commuting trajectory, transportation mode, and transfer stations. Second, by reinterpreting the Multinomial Logit (MNL) model through the canonical ensemble framework of statistical physics, we model route choice rationality as a temperature-dependent system. Our approach uniquely measures behavioral consistency in terms of rationality and preference stability over time, and distinguishes systematic from random heterogeneity. Our results reveal temporal stability in aggregate route choice behavior across the entire urban region throughout 2023. Also, we found heterogeneity dependent on the origin and destination (OD) pair. This variation is reflected as a bimodal split in

Understanding spatial openness is vital for improving residential quality and design; however, studies often treat its influencing factors separately. This study developed a quantitative framework to evaluate the spatial openness in housing from two- (2D) and three- (3D) dimensional perspectives. Using data from 4,004 rental units in Tokyo's 23 wards, we examined the temporal and spatial variations in openness and its relationship with rent and housing attributes. 2D openness was computed via planar visibility using visibility graph analysis (VGA) from floor plans, whereas 3D openness was derived from interior images analysed using Mask2Former, a semantic segmentation model that identifies walls, ceilings, floors, and windows. The results showed an increase in living room visibility and a 1990s peak in overall openness. Spatial analyses revealed partial correlations among openness, rent, and building characteristics, reflecting urban redevelopment trends. Although the 2D and 3D openness indicators were not directly correlated, higher openness tended to correspond to higher rent. The impression scores predicted by the existing models were only weakly related to openness, suggesting

Considering the purpose of the session relating early engineering developments in site response and soil-structure interaction, this paper focuses on the development of studies regarding site-city interaction following the striking site response observations obtained in Mexico City during the 1985 Guerrero-Michoacan event, The first part presents an overview of the investigations on multiple structure-soil-structure interaction, starting with Mexico-city like environments with dense urbanization on soft soils, which later evolved with the concept of metamaterials. Up to now, such investigations have been largely relying on numerical simulations in 2D and 3D media, coupling soft surface soil layers and simplified building models, including also some theoretical developments using various mechanical concepts. They also relied on a number of laboratory experiments on reduced-scale mock-ups with diverse vibratory sources (shaking table, acoustic devices). The latest studies coupled full-scale experiments on mechanical analogs such as forests or wind turbine farms involving sets of resonators with similar frequencies, and numerical simulation to investigate their impact on the propagati

Analyzing the SARS-CoV-2 pandemic outbreak based on actual data while reflecting the characteristics of the real city provides beneficial information for taking reasonable infection control measures in the future. We demonstrate agent-based modeling for Tokyo based on GPS information and official national statistics and perform a spatiotemporal analysis of the infection situation in Tokyo. As a result of the simulation during the first wave of SARS-CoV-2 in Tokyo using real GPS data, the infection occurred in the service industry, such as restaurants, in the city center, and then the infected people brought back the virus to the residential area; the infection spread in each area in Tokyo. This phenomenon clarifies that the spread of infection can be curbed by suppressing going out or strengthening infection prevention measures in service facilities. It was shown that pandemic measures in Tokyo could be achieved not only by strong control, such as the lockdown of cities, but also by thorough infection prevention measures in service facilities, which explains the curb phenomena in real Tokyo.

Universal power laws have been scrutinised in physics and beyond, and a long-standing debate exists in econophysics regarding the strict universality of the nonlinear price impact, commonly referred to as the square-root law (SRL). The SRL posits that the average price impact $I$ follows a power law with respect to transaction volume $Q$, such that $I(Q) \propto Q^δ$ with $δ\approx 1/2$. Some researchers argue that the exponent $δ$ should be system-specific, without universality. Conversely, others contend that $δ$ should be exactly $1/2$ for all stocks across all countries, implying universality. However, resolving this debate requires high-precision measurements of $δ$ with errors of around $0.1$ across hundreds of stocks, which has been extremely challenging due to the scarcity of large microscopic datasets -- those that enable tracking the trading behaviour of all individual accounts. Here we conclusively support the universality hypothesis of the SRL by a complete survey of all trading accounts for all liquid stocks on the Tokyo Stock Exchange (TSE) over eight years. Using this comprehensive microscopic dataset, we show that the exponent $δ$ is equal to $1/2$ within statistica

This paper evaluated the effects of the Tokyo 2020 Olympic and Paralympic Games on traffic demand on the Metropolitan expressway. We constructed panel data for both passenger and freight vehicles' demand using longitudinal disaggregated trip records from the Metropolitan expressway. Subsequently, we established a demand function and used a difference-in-differences method to individually estimate the impacts of toll surcharges and other Olympics-related factors by leveraging the fact that the toll surcharges were not applied to freight vehicles. The results indicate that toll surcharges resulted in a decrease of 25.0 % for weekdays and 36.8 % for weekends/holidays in passenger vehicle demand on the Metropolitan expressway. The estimated toll elasticities are 0.345 for weekdays and 0.615 for weekends/holidays, respectively. Notably, analysis of the Olympics-related factor demonstrated that travel demand management (TDM) strategies effectively curbed demand on weekends/holidays with a reduction of 2.9 % in traffic demand. However, on weekdays, induced demand surpassed the reduction of demand by other TDM strategies than tolling, resulting in a 4.6 % increase in traffic demand. Additi

I analyze the risk-coping strategies of factory-worker households in early twentieth-century Tokyo. I digitized a unique daily longitudinal household budget survey conducted in Tsukishima, a representative manufacturing area, to examine how consumption was affected by idiosyncratic shocks. I find that although the households were vulnerable and their consumption levels were impacted by these shocks, the estimated income elasticity of indispensable consumption was relatively low in the short run. The results of the mechanism analysis suggest that credit purchases from local retailers helped smooth short-run consumption, highlighting the role of informal credit institutions in mitigating vulnerability among urban worker households.

This study is the first to investigate whether pawnshops, financial institutions for low-income populations, have contributed to the decline in mortality in the early twentieth century. Using ward-level panel data from Tokyo City, this study revealed that the popularity of public pawnshops was associated with a 4% and 5% decrease in infant mortality and fetal death rates, respectively, during 1927-1935. The historical context implies that the potential channels of the relationships were improving nutrition and hygiene and covering childbirth costs. Moreover, a cost-effectiveness calculation highlighted that the establishment of public pawnshops was a cost-effective public investment for better public health. Contrarily, for-profit private pawnshops showed no significant association with health improvements.

Individuals undertake both solo and joint activities as part of their overall activity-travel patterns. Compared to work and maintenance activities, social and leisure activities differ in that they exhibit high levels of temporal and spatial flexibility. In this study we used data from an ego-centric social networks survey in the Greater Tokyo Area and follow-up group activity survey to estimate a joint eating-out destination choice model explicitly incorporating group-level impedance. Consistent with the literature, travel time has a large impact on destination choice as measured by its elasticity; however, the elasticities of group-level maximum, average and median travel times are larger than individual-level travel times. Furthermore, we show that incorporating group-level impedance increases model performance up to 49% against the ego-level impedance model, a substantial increase that underscores the need to incorporate group-level characteristics in travel behavior models.

In order to understand the chemical properties and environmental impacts of low-solubility Cs-rich microparticles (CsMPs) derived from the FDNPP, the CsMPs collected from Tokyo were investigated at the atomic scale using high-resolution transmission electron microscopy (HRTEM) and dissolution experiments were performed on the air filters. Remarkably, CsMPs 0.58-2.0 micrometer in size constituted 80%-89% of the total Cs radioactivity during the initial fallout events on 15th March, 2011. The CsMPs from Tokyo and Fukushima exhibit the same texture at the nanoscale: aggregates of Zn-Fe-oxide nanoparticles embedded in amorphous SiO2 glass. The Cs is associated with Zn-Fe-oxide nanoparticles or in the form of nanoscale inclusions of intrinsic Cs species,rather than dissolved in the SiO2 matrix. The Cs concentration in CsMPs from Tokyo (0.55-10.9 wt%) is generally less than that in particles from Fukushima (8.5-12.9 wt%).The radioactivity per unit mass of CsMPs from Tokyo is still as high as 1E11 Bq/g, which is extremely high for particles originating from nuclear accidents. Thus, inhalation of the low-solubility CsMPs would result in a high localized energy deposition by beta (0.51-12)*

The rapid growth of the e-commerce market creates new dynamics in the logistics landscape, which has been evolving for decades in cities around the world. It is a challenge for businesses and planners to meet the high demand for logistics facilities for e-commerce order fulfillment and goods handling. In the Tokyo Metropolitan Area, mega-scale multi-tenant logistics facilities have been developed in both the port area near the urban center and the periphery of the city, while delivery service providers locate many last-mile delivery stations, varying in number depending on the urban density. We analyze the spatial distribution and location factors of both mega-scale multi-tenant facilities and last-mile delivery facilities. We found that, due to the scarcity of land, newly developed multi-tenant facilities are more likely to be in less accessible places that have high-level development restrictions. The result also indicates the heterogeneity of the distribution of delivery service providers' facilities, reflecting the heterogeneity in business strategies.

Amid growing environmental concerns, interactive displays of data constitute an important tool for exploring and understanding the impact of climate change on the planet's ecosystemic integrity. This paper presents Tokyo kion-on, a query-based sonification model of Tokyo's air temperature from 1876 to 2021. The system uses a recurrent neural network architecture known as LSTM with attention trained on a small dataset of Japanese melodies and conditioned upon said atmospheric data. After describing the model's implementation, a brief comparative illustration of the musical results is presented, along with a discussion on how the exposed hyper-parameters can promote active and non-linear exploration of the data.

This study investigates the influence of infection cases of COVID-19 and two non-compulsory lockdowns on human mobility within the Tokyo metropolitan area. Using the data of hourly staying population in each 500m$\times$500m cell and their city-level residency, we show that long-distance trips or trips to crowded places decrease significantly when infection cases increase. The same result holds for the two lockdowns, although the second lockdown was less effective. Hence, Japanese non-compulsory lockdowns influence mobility in a similar way to the increase in infection cases. This means that they are accepted as alarm triggers for people who are at risk of contracting COVID-19.

The 6th wave of COVID-19 in Tokyo continued for the longest period of infection (about 190 days from late Nov. 2021), and the 7th wave, which occurred in mid-May 2022, was the largest wave ever (cumulative 1.7 million people). In order to elucidate their factors, the infection wave was analyzed by using the Avrami equation. The main component of the 6th wave was formed by the coupling of increased human interaction due to the New Year holidays and the invasion of the new virus variant Omicron BA.1. After that, side waves were formed by the coupling of the invasion of the new virus variant Omicron BA.2 and the human interaction in the consecutive holidays in February, March, and May. These side waves caused the 6th wave not to converge for a long time. The outbreak of the main component of the 7th wave occurred by the coupling of the invasion of the new virus variant Omicron BA.5 and multiple social factors, followed by human interaction during the July holidays. Based on the results that the domain growth rate $K$ and the infection rise time $t_\mathrm{on}$ were almost independent of the initial susceptible $D_\mathrm{s}$, dense nucleation followed by a near growth model was deduce

The purpose of this study was to simulate all COVID-19 infection waves in Tokyo, the capital of Japan, by phase transformation dynamics theory, and to quantitatively analyze the detailed structure of the waveform for estimating the cause. The whole infection wave in Tokyo was basically expressed by the superposition of the 5 major waves as in Japan as a whole. Among these waves, the detailed structure was seen in the 3rd and the 5th waves, where the number of infections increased remarkably due to the holidays. It was characterized as "New Year holiday effect" for New Year holidays, "Tokyo Olympics holiday effect" for Olympics holidays, and "Delta variant effect" for the replacement of the virus with Delta variant. Since this method had high simulation accuracy for the cumulative number of infections, it was effective in estimating the cause, the number of infections in the near future and the vaccination effect by quantitative analysis of the detailed structure of the waveform.