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With climate change increasingly threatening people in uneven and disproportional ways, community-based solutions and interventions have become critical to ensure equitable, just, and inclusive climate action in cities. This paper examines community-based climate action in San Francisco (USA) through the lens of climate justice and intersectionality. Through a qualitative analysis of community-based organisations (CBOs) and their justice-oriented adaptation and mitigation efforts, our research examines the contributing factors and pathways by which CBO climate action leads to intersectional climate justice. Our analysis examines how CBOs 1) recognise and rectify historical and compounding vulnerabilities; 2) plan and act in ways that are people-centric and place-based; and 3) work collectively with organisations and government through alliances, coalitions, and participatory processes. We find that CBOs have the potential to work collectively to ensure processes are just and outcomes are equitable for those most at risk of climate change. [ABSTRACT FROM AUTHOR]

Monarch butterflies, Danaus plexippus L., in western North America are undergoing a seemingly volatile shift in seasonal population dynamics which includes novel winter-breeding on non-native milkweed in urban areas of northern California. The survival, viability and outcome of monarch pupae in the wild was evaluated for the first time by regular monitoring of a cohort of 104 pupae in a San Francisco managed urban landscape during the winter of 2021/22. Seventeen pupae were destroyed by landscaping activities. Almost half (49.4%) of the remaining pupae eclosed, with ∼ 70% likely infected with varying levels of the protozoan parasite Ophryocystis elektroscirrha (OE). A third of the pupae (32.2%) died of unknown causes, while the remainder (18.4%) disappeared, presumed predated. Two monarchs were killed while eclosing, by European paper wasps, Polistes dominula (Christ, 1791). Microscopic examination of pupal exuviae for spores of OE appeared to be a satisfactory proxy for microscopic examination of butterfly abdomens, in determining the presence/absence of infection. The developing importance of winter breeding in western monarch ecology necessitates modifications to conservation guidelines that enhance the survival of immature stages, acknowledging the role of non-native milkweeds while minimizing parasite infection. [ABSTRACT FROM AUTHOR]

The movie Try Harder! features a group of students at Lowell High School in San Francisco, California, as they navigate their elite public institution and apply to top-tier universities. A critical analysis of this film allows us to understand new trends and emerging discourses in urban cities, showing the way racialized groups are pit against each other to legitimize white supremacy. I employ the lenses of critical studies of race to examine the following questions: (1) how does the model minority myth shape urban contexts? and (2) how are racial, political, and academic discourses constructing notions of merit via the film Try Harder? Few studies examine the model minority myth in relation to urban contexts, despite its rising influence in political debates and education policy. This paper aims to fill this void by addressing the changing landscape of urban education and academic discourse. [ABSTRACT FROM AUTHOR]

Polarimetric target decomposition algorithms have played an important role in extracting the scattering characteristics of buildings, crops, and other fields. However, there is limited research on the scattering characteristics of grasslands and a lack of volume scattering models established for grasslands. To improve the accuracy of the polarimetric target decomposition algorithm applicable to grassland environments, this paper proposes an adaptive polarimetric target decomposition algorithm (APD) based on the anisotropy degree (A). The adaptive volume scattering model is used in APD to model volume scattering in forest and grassland regions separately by adjusting the value of A. When A > 1, the particle shape becomes a disk, and the grassland canopy is approximated as a cloud layer composed of randomly oriented disk particles; when A < 1, the particle shape is a needle, simulating the scattering mechanism of forests. APD is applied to an L-band AirSAR dataset from San Francisco, a C-band AirSAR dataset from Hunshandak grassland in Inner Mongolia Autonomous Region, and an X-band COSMO-SkyMed dataset from Xiwuqi grassland in Inner Mongolia Autonomous Region to verify the effectiveness of this method. Comparison studies are carried out to test the performance of APD over several target decomposition algorithms. The experimental results show that APD outperforms the algorithms tested in terms of this study in decomposition accuracy for grasslands and forests on different bands of data. [ABSTRACT FROM AUTHOR]

This paper examines spatiotemporal patterns and socioeconomic influences on host participation in Airbnb's short-term rental (STR) marketplace in San Francisco during the years 2019–2022, a four-year period that spans the COVID-19 pandemic. This provides the motivation for the study to examine how San Francisco's demographic and socioeconomic fluctuations influenced Airbnb hosts to rent their properties on the platform. To do so, Airbnb property densities, indicators of host participation, are estimated at the census tract level and subsequently mapped in a GIS along with points of interest (POIs) located all over the city. Mapping unveils spatiotemporal patterns and changes in Airbnb property densities, which are also analyzed for spatial autocorrelation using Moran's I. Clusters and outliers of property densities are identified using K-means clustering and geostatistical methods such as local indicators of spatial association (LISA) analysis. Locationally, San Francisco's Airbnb hotspots are not located in the city's core, unlike other major Airbnb markets in metropolitan areas. Instead, such hotspots are in the city's northeastern neighborhoods around ethnic enclaves, in close proximity to POIs that are frequented by visitors, and have a higher proportion of hotel and lodging employment and lower median household income. A conceptual model posits associations of Airbnb property densities with sixteen demographic, socioeconomic factors, indicators of trust, social capital, and sustainability, along with proximity to points of interest. Ordinary least squares (OLS) regressions reveal that occupation in professional, scientific, and technical services, hotel and lodging employment, proximity to POIs, and proportion of Asian population are the dominant factors influencing host participation in San Francisco's shared accommodation economy. The occupational influences are novel findings for San Francisco. These influences vary somewhat for two main types of properties—entire home/apartment and private rooms. Implications of these findings are discussed in relation to supply side motivations of Airbnb hosts to participate in San Francisco's STR marketplace. [ABSTRACT FROM AUTHOR]

In the wake of seismic events, prompt and accurate building damage assessment is crucial to inform post-disaster interventions and recovery efforts. This paper advances a novel multi-period planning strategy for post-earthquake building inspections, conceptualizing the task as a multi-period orienteering problem (MPOP). In this framework, each building selected for inspection hosts a specific reward indicating its informative value for damage assessment. The objective is to design inspection routes that maximize damage information acquisition while adhering to time constraints. After data collection, we utilize a Gaussian process regression (GPR) model to estimate the damage in uninspected buildings. To validate our approach, we conduct an earthquake simulation with realistic building information from San Francisco. The experimental outcomes reveal that our multi-period damage assessment framework maintains robust performance across diverse scenarios and consistently surpasses conventional period-by-period inspection strategies, yielding enhanced damage information acquisition and greater precision in damage estimation. This outcome underscores the effectiveness of our proposed method in strengthening post-earthquake damage assessment and improving recovery planning. • Formulate the inspection routing problem as a multi-period orienteering problem. • A Gaussian process regression model is applied to make damage estimations. • Compare efficiency of multi-period routing strategy and period-by-period strategy. • Validate the framework on instances from San Francisco. [ABSTRACT FROM AUTHOR]

This paper assesses the equity impacts of for-hire autonomous vehicles (AVs) and investigates regulatory policies that promote spatial and social equity in future autonomous mobility ecosystems. To this end, we consider a multimodal transportation network, where a ride-hailing platform operates a fleet of AVs to offer mobility-on-demand services in competition with a public transit agency that offers transit services on a transportation network. A game-theoretic model is developed to characterize the intimate interactions between the ride-hailing platform, the transit agency, and multiclass passengers with distinct income levels. An algorithm is proposed to compute the Nash equilibrium of the game and conduct an ex-post evaluation of the performance of the obtained solution. Based on the proposed framework, we evaluate the spatial and social equity in mobility benefits using the Theil index, and find that although the proliferation of for-hire AVs in the ride-hailing network improves overall mobility, the benefits are not fairly distributed among distinct locations or population groups, implying that the deployment of AVs will enlarge the existing spatial and social inequity gaps in the transportation network if no regulatory intervention is in place. To address this concern, we investigate two regulatory policies that can improve transport equity: (a) a minimum service-level requirement on ride-hailing services, which improves the spatial equity in the transport network; (b) a subsidy on transit services by taxing ride-hailing services, which promotes the use of public transit and improves the spatial and social equity of the transport network. We show that the minimum service-level requirement entails a trade-off: as a higher minimum service level is imposed, the spatial inequity reduces, but the social inequity will be exacerbated. On the other hand, subsidies on transit services improve mobility for low-income households in underserved areas. In certain regimes, the subsidy increases public transit ridership and simultaneously bridges spatial and social inequity gaps. These results are validated through realistic numerical studies for San Francisco. [ABSTRACT FROM AUTHOR]

This paper investigates the joint planning and operation of a ride-hailing network with a mixture of level-4 autonomous vehicles (AVs) and for-hire human drivers. Distinct from most existing works that focus on fully autonomous vehicles, we envision a more foreseeable future where ride-hailing platform utilizes a fleet of level-4 AVs to provide mobility-on-demand services in selected areas of the city, complemented by a group of for-hire drivers who can pick up or drop off passengers outside of the AV's service area. The platform not only makes planning decisions regarding the selection of service area of AVs, but also makes operational decisions regarding the ride fares, driver compensations, fleet size, and vehicle relocation strategies, etc., while interacting with passengers and for-hire human drivers on the transportation network. The overall profit-maximization problem of the platform is formulated as a mixed-integer nonlinear program, which is highly non-convex and difficult to address. To tackle this challenge, we developed a decomposition-based algorithm that can compute a near-optimal solution for the optimization problem, while offering a theoretical upper bound on the gap between the derived solution and the unknown globally optimal solution. The proposed method is validated in a numerical study for San Francisco. We show that activating level-4 AVs in selected areas (instead of over the entire network) can improve the platform profit by up to 20%. We also observe that as the cost of AV infrastructures reduces, the spatial diffusion of AV services will follow an interesting pattern, where the platform will first activate all high-demand zones for AV services simultaneously, and then progressively expand AV services to other areas one zone after another as a result of the complex trade-off between demand levels, activation cost, and network connectivity. We further evaluate the impacts of AV diffusion on human drivers and passengers and demonstrate that (a) human drivers will first be incentivized to relocate to lower-demand areas and then be forced to leave the TNC market if the cost of AV infrastructure continues to decline; (b) passengers in remote areas will surprisingly experience higher trip costs even though the cost of AV decreases, particularly after AV is activated in urban areas but before it is activated in remote areas. This finding reveals the unequal distribution of benefits in the autonomous ride-hailing network, underscoring the need for additional regulatory involvement. • An equilibrium model for ride-hailing services with level-4 autonomous vehicles. • A hierarchical decomposition method for non-convex mixed integer nonlinear program. • Investigated the trade-off between demand, activation cost, and network connectivity. • Examined the impact of level-4 AVs on passengers over the transportation network. [ABSTRACT FROM AUTHOR]

The timely transportation of goods to customers is an essential component of economic activities. However, heavy-duty diesel trucks used for goods delivery significantly contribute to greenhouse gas (GHG) emissions within many large metropolitan areas, including Los Angeles, New York, and San Francisco. To reduce GHG emissions by facilitating freight electrification, this paper proposes Joint Routing and Charging (JRC) scheduling for electric trucks. The objective of the associated optimization problem is to minimize the cost of transportation, charging, and tardiness. A large number of possible combinations of road segments as well as a large number of combinations of charging decisions and charging durations leads to a combinatorial explosion in the possible decisions electric trucks can make. The resulting mixed-integer linear programming (MILP) problem is thus extremely challenging because of the combinatorial complexity even in the deterministic case. Therefore, a Surrogate Level-Based Lagrangian Relaxation (SLBLR) method is employed to decompose the overall problem into significantly less complex truck subproblems. In the coordination aspect, each truck subproblem is solved independently of other subproblems based on the values of Lagrangian multipliers. In addition to serving as a means of guiding and coordinating trucks, multipliers can also serve as a basis for transparent and explanatory decision-making by trucks. Testing results demonstrate that even small instances cannot be solved using the off-the-shelf solver CPLEX after several days of solving. The SLBLR method, on the other hand, can obtain near-optimal solutions within a few minutes for small cases, and within 30 min for large ones. Furthermore, it has been demonstrated that as battery capacity increases, the total cost decreases significantly; moreover, as the charging power increases, the number of trucks required decreases as well. • Present a formulation of a joint heavy-duty vehicle fleet routing & charging problem. • Use surrogate level-based Lagrangian relaxation approach to solve the problem. • The proposed method obtains near-optimal solutions for realistic testing cases. [ABSTRACT FROM AUTHOR]

• We apply Latent Dirichlet Allocation algorithm to discover activity patterns from floating vehicle trajectory data. • We incorporate activity patterns into traffic safety analysis to supplement traditional traffic exposure measures. • We use Graph Convolutional Network to explore multiple factors influencing spatial patterns of traffic crashes. • We estimate non-linear relationships between variables and traffic crashes using interpretive methodologies. In recent years, increased attention has been given to understanding the spatial pattern of crashes in urban areas. Accurately capturing the spatial relationship between crash counts and variables requires extracting hidden information from multiple data sources. In this study, we propose a machine learning model to explore the spatial impact of activity patterns on spatially aggregated crash counts. Our paper introduces a two-step framework: (a) the Latent Dirichlet Allocation (LDA) model, an unsupervised method for mining hidden activity patterns from floating vehicle trajectory data, and (b) the Graph Convolutional Network (GCN) model, which builds the spatial relationship between multi-source data. The data and hidden activity patterns were aggregated into 175 Traffic Analysis Zones (TAZs) in San Francisco using spatial partitioning. The GCN model provided higher prediction accuracy than commonly used machine learning algorithms that did not consider combined spatial relationships and those that only considered traditional vehicle counts data. Furthermore, we used attribution algorithms to obtain the respective weight scores of each factor. Our results reveal that daily vehicle kilometers traveled, road density, population density, commercial activity during weekends, and residential activity during morning peak hours on weekdays are factors associated with crashes. [ABSTRACT FROM AUTHOR]