Open Visual Knowledge Extraction via Relation-Oriented Multimodality Model Prompting

Images contain rich relational knowledge that can help machines understand the world. Existing methods on visual knowledge extraction often rely on the pre-defined format (e.g., sub-verb-obj tuples) or vocabulary (e.g., relation types), restricting the expressiveness of the extracted knowledge. In this work, we take a first exploration to a new paradigm of open visual knowledge extraction. To achieve this, we present OpenVik which consists of an open relational region detector to detect regions potentially containing relational knowledge and a visual knowledge generator that generates format-free knowledge by prompting the large multimodality model with the detected region of interest. We also explore two data enhancement techniques for diversifying the generated format-free visual knowledge. Extensive knowledge quality evaluations highlight the correctness and uniqueness of the extracted open visual knowledge by OpenVik. Moreover, integrating our extracted knowledge across various visual reasoning applications shows consistent improvements, indicating the real-world applicability of OpenVik.Comment: Accepted to NeurIPS 202

Spatial analysis of the impact of urban geometry and socio-demographic characteristics on COVID-19, a study in Hong Kong

The World Health Organization considered the widespread of COVID-19 over the world as a pandemic. There is still a lack of understanding of its origin, transmission, and treatment methods. Understanding the influencing factors of the COVID-19 can help mitigate its spread, but little research on the spatial factors has been conducted. Therefore, this study explores the effects of urban geometry and socio-demographic factors on the COVID-19 cases in Hong Kong. For each patient, the places they visited during the incubation period before going to hospital were identified, and matched with corresponding attributes of urban geometry (i.e., building geometry, road network, greenspace) and socio-demographic factors (i.e., demographic, educational, economic, household and housing characteristics) based on the coordinates. The local cases were then compared with the imported cases using the stepwise logistic regression, the logistic regression with case-control of time, and the least absolute shrinkage and selection operator regression to identify factors influencing local disease transmission. Results show that the building geometry, road network and certain socio-economic characteristics are significantly associated with COVID-19 cases. In addition, the results indicate that urban geometry is playing a more important role than the socio-demographic characteristics in affecting the COVID-19 incidences. These findings provide a useful reference to the government and the general public as to the spatial vulnerability of the COVID-19 transmission and to take appropriate preventive measures in high-risk areas

Inconsistent Association between Perceived Air Quality and Self-Reported Respiratory Symptoms: A Pilot Study and Implications for Environmental Health Studies

As public awareness of air quality issues becomes heightened, people’s perception of air quality is drawing increasing academic interest. However, data about people’s perceived environment need scrutiny before being used in environmental health studies. In this research, we examine the associations between people’s perceptions of air quality and their self-reported respiratory health symptoms. Spearman rank correlation coefficients were estimated and the associations were tested at the 95% confidence level. Using data collected from participants in two representative communities in Hong Kong, the results indicate a weak but significant association between people’s perceived air quality and their self-reported frequency of respiratory symptoms. However, there are disparities in such an association between different genders, age groups, household income levels, education levels, marital statuses, and geographic contexts. The most striking disparities are between genders and geographic contexts. Multiple significant associations were observed for male participants (correlation coefficients: 0.169~0.205, p-values: 0.021~0.049), while none was observed for female participants. Besides, multiple significant associations were observed in the old town (correlation coefficients: 0.164~0.270, p-values: 0.003~0.048), while none was observed in the new town. The results have significant implications for environmental health research using social media data, whose reliability depends on the association between people’s perceived or actual environments and their health outcomes. Since inconsistent associations exist between different groups of people, researchers need to scrutinize social media data before using them in health studies

People’s political views, perceived social norms, and individualism shape their privacy concerns for and acceptance of pandemic control measures that use individual-level georeferenced data

Abstract Background As the COVID-19 pandemic became a major global health crisis, many COVID-19 control measures that use individual-level georeferenced data (e.g., the locations of people’s residences and activities) have been used in different countries around the world. Because these measures involve some disclosure risk and have the potential for privacy violations, people’s concerns for geoprivacy (locational privacy) have recently heightened as a result, leading to an urgent need to understand and address the geoprivacy issues associated with COVID-19 control measures that use data on people’s private locations. Methods We conducted an international cross-sectional survey in six study areas (n = 4260) to examine how people’s political views, perceived social norms, and individualism shape their privacy concerns, perceived social benefits, and acceptance of ten COVID-19 control measures that use individual-level georeferenced data. Multilevel linear regression models were used to examine these effects. We also applied multilevel structure equation models (SEMs) to explore the direct, indirect, and mediating effects among the variables. Results We observed a tradeoff relationship between people’s privacy concerns and the acceptance (and perceived social benefits) of the control measures. People’s perceived social tightness and vertical individualism are positively associated with their acceptance and perceived social benefits of the control measures, while horizontal individualism has a negative association. Further, people with conservative political views and high levels of individualism (both vertical and horizontal) have high levels of privacy concerns. Conclusions Our results first suggest that people’s privacy concerns significantly affect their perceived social benefits and acceptance of the COVID-19 control measures. Besides, our results also imply that strengthening social norms may increase people’s acceptance and perceived social benefits of the control measures but may not reduce people’s privacy concerns, which could be an obstacle to the implementation of similar control measures during future pandemics. Lastly, people’s privacy concerns tend to increase with their conservatism and individualism

Lane-Level Road Information Mining from Vehicle GPS Trajectories Based on Naïve Bayesian Classification

In this paper, we propose a novel approach for mining lane-level road network information from low-precision vehicle GPS trajectories (MLIT), which includes the number and turn rules of traffic lanes based on naïve Bayesian classification. First, the proposed method (MLIT) uses an adaptive density optimization method to remove outliers from the raw GPS trajectories based on their space-time distribution and density clustering. Second, MLIT acquires the number of lanes in two steps. The first step establishes a naïve Bayesian classifier according to the trace features of the road plane and road profiles and the real number of lanes, as found in the training samples. The second step confirms the number of lanes using test samples in reference to the naïve Bayesian classifier using the known trace features of test sample. Third, MLIT infers the turn rules of each lane through tracking GPS trajectories. Experiments were conducted using the GPS trajectories of taxis in Wuhan, China. Compared with human-interpreted results, the automatically generated lane-level road network information was demonstrated to be of higher quality in terms of displaying detailed road networks with the number of lanes and turn rules of each lane

A Space-time Path Supported Estimation Approach for Vehicles' Fuel-consumption and Emissions

The fuel-consumption and emissions from transportation present severe challenges to the human environment. This article proposes a novel approach of space-time path supported estimation for vehicles' fuel-consumption and emissions. In the proposed approach,space-time paths of vehicles are built under space-time integrated 3-dimensions coordinate firstly and mobile activities (MA) and stationary activities (SA) are extracted from these space-time paths. Then the approach estimates the fuel-consumption and emissions from each Space-Time Path Segment (STPS) and the moving parameters with COPERT model. Finally this article presents an N-Dimensional model for visualizing the moving characteristics,fuel-consumption and emissions of each STPS in an integrated frame. In the case study,fuel-consumption and emissions of a single vehicle and an area of road network are estimated and analyzed using GPS trace data. The results show that the space-time path supported approach is superior to the traditional average speed based approach in the aspects of precision and visualization. The proposed fuel-consumption and emissions estimating approach is effective in energy and emissions information acquisition

Estimating Vehicle Fuel Consumption and Emissions Using GPS Big Data

The energy consumption and emissions from vehicles adversely affect human health and urban sustainability. Analysis of GPS big data collected from vehicles can provide useful insights about the quantity and distribution of such energy consumption and emissions. Previous studies, which estimated fuel consumption/emissions from traffic based on GPS sampled data, have not sufficiently considered vehicle activities and may have led to erroneous estimations. By adopting the analytical construct of the space-time path in time geography, this study proposes methods that more accurately estimate and visualize vehicle energy consumption/emissions based on analysis of vehicles’ mobile activities (MA) and stationary activities (SA). First, we build space-time paths of individual vehicles, extract moving parameters, and identify MA and SA from each space-time path segment (STPS). Then we present an N-Dimensional framework for estimating and visualizing fuel consumption/emissions. For each STPS, fuel consumption, hot emissions, and cold start emissions are estimated based on activity type, i.e., MA, SA with engine-on and SA with engine-off. In the case study, fuel consumption and emissions of a single vehicle and a road network are estimated and visualized with GPS data. The estimation accuracy of the proposed approach is 88.6%. We also analyze the types of activities that produced fuel consumption on each road segment to explore the patterns and mechanisms of fuel consumption in the study area. The results not only show the effectiveness of the proposed approaches in estimating fuel consumption/emissions but also indicate their advantages for uncovering the relationships between fuel consumption and vehicles’ activities in road networks

Toward a Healthy Urban Living Environment: Assessing 15-Minute Green-Blue Space Accessibility

Exposure to green-blue space has been shown to be associated with better physical and mental health outcomes. The advent of COVID-19 has underlined the importance for people to have access to green-blue spaces in proximity to their residences due to pandemic-related restrictions on activity space. The implementation of the 15-min concept, which advocates that people should be able to reach locations of essential functions like green-blue spaces within 15 min of active travel, can bring green-blue spaces nearer to where people live. Nonetheless, there is still a lack of understanding of the social and spatial (in)equality in 15-min green-blue space accessibility by active travel in cities seeking to embrace the concept, such as Hong Kong. This study explores 15-min green-blue space accessibility by walking and cycling in Hong Kong to reveal the distribution of disadvantaged neighborhoods. The results show that neighborhoods in Kowloon’s districts are the most disadvantaged in accessing green-blue spaces within 15 min of active travel. Our study provides policymakers with valuable insights and knowledge conducive to formulating policies aimed at reducing inequality in 15-min accessibility

Toward a Healthy Urban Living Environment: Assessing 15-Minute Green-Blue Space Accessibility

Exposure to green-blue space has been shown to be associated with better physical and mental health outcomes. The advent of COVID-19 has underlined the importance for people to have access to green-blue spaces in proximity to their residences due to pandemic-related restrictions on activity space. The implementation of the 15-min concept, which advocates that people should be able to reach locations of essential functions like green-blue spaces within 15 min of active travel, can bring green-blue spaces nearer to where people live. Nonetheless, there is still a lack of understanding of the social and spatial (in)equality in 15-min green-blue space accessibility by active travel in cities seeking to embrace the concept, such as Hong Kong. This study explores 15-min green-blue space accessibility by walking and cycling in Hong Kong to reveal the distribution of disadvantaged neighborhoods. The results show that neighborhoods in Kowloon’s districts are the most disadvantaged in accessing green-blue spaces within 15 min of active travel. Our study provides policymakers with valuable insights and knowledge conducive to formulating policies aimed at reducing inequality in 15-min accessibility

Fine-grained analysis of traffic congestions at the turning level using GPS traces

For the issue that existing approaches on studying traffic conditions using GPS traces lack of detailed analysis of traffic congestion, this paper puts forward an approach for detecting traffic congestion events based on taxis' GPS traces at turning level. Firstly, this approach analyzed taxis' operating patterns and filtered valid traces. Then this approach detected traffic congestion traces of three different intensities:mild congestion, moderate congestion and serious congestion, based on analyzing traffic conditions from the filtered valid trace segments. Finally, traffic flow speed, congestion time and congestion distance of each turning direction at an intersection were explored at a fine-grained level. The experimental results show that the proposed approach is able to detect congestions of different intensities and analyze congestion events at turning level