dhCM: Dynamic and Hierarchical Event Categorization and Discovery for Social Media Stream

The online event discovery in social media based documents is useful, such as for disaster recognition and intervention. However, the diverse events incrementally identified from social media streams remain accumulated, ad hoc, and unstructured. They cannot assist users in digesting the tremendous amount of information and finding their interested events. Further, most of the existing work is challenged by jointly identifying incremental events and dynamically organizing them in an adaptive hierarchy. To address these problems, this article proposes d ynamic and h ierarchical C ategorization M odeling (dhCM) for social media stream. Instead of manually dividing the timeframe, a multimodal event miner exploits a density estimation technique to continuously capture the temporal influence between documents and incrementally identify online events in textual, temporal, and spatial spaces. At the same time, an adaptive categorization hierarchy is formed to automatically organize the documents into proper categories at multiple levels of granularities. In a nonparametric manner, dhCM accommodates the increasing complexity of data streams with automatically growing the categorization hierarchy over adaptive growth. A sequential Monte Carlo algorithm is used for the online inference of the dhCM parameters. Extensive experiments show that dhCM outperforms the state-of-the-art models in terms of term coherence, category abstraction and specialization, hierarchical affinity, and event categorization and discovery accuracy.

Spatial and Temporal Influence of Sea Level on Inland Stress based on Seismic Velocity Monitoring

Earth’s crust responds to perturbations from various environmental factors. To evaluate this response, seismic velocity changes offer an indirect diagnostic, especially where velocity can be monitored on an ongoing basis from ambient seismic noise. Investigating the connection between the seismic velocity changes and external perturbations could be useful for characterizing dynamic activities in the crust. The seismic velocity is known to be sensitive to variations in meteorological signals such as temperature, snow, and precipitation as well as changes in sea level. Among these perturbations, the impact of variations in sea level on velocity changes inferred from seismic interferometry of ambient noise is not well known. This study investigates the influence of the ocean in a 3-year record of ambient noise seismic velocity monitoring in the Chugoku and Shikoku regions of southwest Japan. First, we applied a bandpass filter to determine the optimal period band for discriminating among different influences on seismic velocity. Then, we applied a regression analysis between the proximity of seismic station pairs to the coast and the ocean influence, as indicated by the correlation of sea level to seismic velocity changes between pairs of stations. Our study suggests that for periods between 0.0036 to 0.01 cycle/day (100–274 days), the ocean’s influence on seismic velocity decreases with increasing distance of station pairs from the coast. The increasing sea level deforms the ocean floor, affecting the stress in the adjacent coast. The stress change induced by the ocean loading may extend at least dozens of kilometers from the coast. The correlation between sea level and inland seismic velocity changes are negative or positive. Although it is difficult to clearly interpret the correlation based on simple model, they could depend on the in situ local stress, orientation of dominant crack, and hydraulic conductivity. Our study shows that seismic monitoring may be useful for evaluating the perturbation in the crust associated with an external load.

Spatio-Temporal Influence of Extreme Weather on a Taxi Market

Extreme weather conditions, strong gusts, and torrential rainfall threaten the safety of the general public and restrict people’s travel options. Most of the transportation modes are suspended because of safety reasons. Taxis are one of the only few available non-private transport modes to provide services to those who have urgent and unavoidable travel needs. This study uses global positioning system data collected from 460 Hong Kong urban taxis during nine ordinary and one tropical cyclone periods aiming to find out and explain the differences in relation to the percentage of taxis not in operation, the number of served passenger-trips, average time spent by vacant-taxi drivers finding a customer, and the percentage of taxi drivers in cross-district customer-search throughout the same 48 h duration. The findings show an inadequate level of taxi supply and a high passenger demand during the tropical-cyclone-affected period. Up to 80% of taxis were not in operation to serve the urgent and necessary trips. The average customer-search time for taxi drivers, which is anticipated inversely proportional to the demand for taxi rides, was very short (about 5 min). Policy measures are discussed and recommended to the government to improve the taxi services during extreme weather conditions.

Analyzing the spatial-temporal influence of built environment on taxi ridership

Taxi is an important component in urban transportation system, which covers wide area and maintains 24h available. Exploring the relationship of taxi and built environment is very important to manage taxi service and improve transportation system. The issue is addressed by capturing the influence of built environment on taxi ridership considering spatial and temporal non-stationarities. The grid cells are developed as analysis units and the global regression model is adopted for preliminary exploring. Then the GWR are implemented considering spatial heterogeneity and the GTWR is used to analyze the spatiotemporal influence of built environment on taxi ridership. An empirical study conducted in Hong Kong Island using one-week taxi’s GPS data demonstrates the effectiveness of the regression models. It’s verified that GWR performs better than OLS, and GTWR outperforms the rest two regression models, indicating both time and space are critical dimensions.

The Spatio-Temporal Heterogeneity of Financial Agglomeration on Green Development in China Cities Using GTWR Model

By promoting financial agglomerations to support green development in a region is a keyway for China to resolve the sharp contradiction between economic growth and environmental protection. However, existing research only considered the promotion effect of financial agglomerations on green development, but the spatio-temporal non-stationarity of that effect has been overlooked. Using a panel data of 285 prefecture-level cities in China and based on the evaluation of green development by a Driving-Pressure-State-Impact-Response (DPSIR) model, this paper analyzes the spatial correlation of financial agglomeration on green development. The paper also investigates the differences in the spatio-temporal influence of financial agglomeration on green development from both global and local perspectives by employing a Bivariate Local Indicators of Spatial Association (BLISA) model and a Geographically and Temporally Weighted Regression (GTWR) model. The results indicate that: (1) There exists significant spatial dependency between financial agglomeration and green development from 2003 to 2015, with Low-Low (L-L) and Low-High (L-H) spatial clusters as the main cluster types. (2) From the local perspective, the promoting effect of financial agglomerations on green development has showed significant spatial heterogeneity with a gradually decreasing trend from the southeast coast to the northwest inland of China. This work can help to develop policies for supporting green development by formulating differential strategies for financial agglomerations.

Spatial and temporal influence of Pacific subduction on South China: geochemical migration of Cretaceous mafic–intermediate rocks

The Cretaceous igneous rocks in the South China Block (SCB) were associated with the slab subduction and roll-back of the Pacific Plate. Thus, they provide excellent opportunities to examine the spatial–temporal geochemical migration of magmatism in the retreating subduction margins. The Cretaceous mafic–intermediate igneous rocks from the southeastern SCB were aged between 142 and 71 Ma, and can geochemically be subdivided into three groups: Group A (126–129 Ma and 83–93 Ma), Group B (126–142 Ma and 71–108 Ma) and Group C (116–142 Ma and 70–110 Ma). Group A and B were mainly distributed in the SCB interior and derived from asthenosphere and asthenosphere–lithosphere interaction sources, respectively. Group C occurred to the east of the Ganjiang Fault and originated from slab–lithosphere interaction. From the coastal provinces to the interior, these mafic–intermediate igneous rocks show increasing incompatible element ratios and Nd isotopic compositions, reflective of a westerly decreasing involvement of slab-derived components. They show two similar age-pulses at c. 125 Ma and c. 90 Ma as well as the Cretaceous A-type granites, indicating two episodes of subduction retreat of the Pacific slab during the Cretaceous. This spatial–temporal pattern of the Cretaceous mafic–intermediate igneous rocks suggests that the Cretaceous slab metasomatism of Pacific subduction retreat was limited to the east of the Ganjiang Fault.Supplementary material: Tables of geochemical data and additional figures are available at https://doi.org/10.6084/m9.figshare.c.4938576