Station Density Research Articles

Ambient vibrations of a deep maar resonator

We introduce non-invasive seismic methods for identifying and characterizing maar sedimentary infills. We conducted a dense ambient vibration survey and employed state-of-the-art 3D resonance analysis techniques to map the lateral extent and depths of the sedimentary layers, a challenge with traditional geophysical methods due to unfavorable aspect ratios (depth > lateral dimension). The ambient vibrations of maars are predominantly driven by normal mode motions due to 3D resonance of crater infills. Dense station coverage enabled detailed images of the normal mode shapes revealing infill symmetries. The resonance results in extreme ground motion amplification, with factors reaching up to 30 on the vertical component, challenging conventional beliefs about site effects and methodologies based on widely used horizontal-to-vertical spectral ratios. These results are supported by numerical simulations of the maar's seismic response. The observed response is so specific that it can be used to identify partly eroded maar structures in the field.

Sub-daily precipitation returns levels in ungauged locations: Added value of combining observations with convection permitting simulations

Extreme rainfall events trigger natural hazards, including floods and debris flows, posing serious threats to society and the economy. Accurately quantifying extreme rainfall return levels in ungauged locations is crucial for improving flood protection infrastructure and mitigating water-related risks. This paper quantifies the added value of combining rainfall observations with Convection Permitting Model (CPM) simulations to estimate sub-daily extreme rainfall return levels in ungauged locations. We assess the performance of CPM-informed estimates of extreme return level against a traditional interpolation techniques. We find that kriging methods with external drift outperform inverse distance weighting for both traditional and CPM-informed approaches. We then assess the effectiveness of the two methods under different scenarios of station density. At the highest station density (1/196 km²), traditional interpolation methods outperform the CPM-informed method for durations under 6 h. The performance becomes comparable between 6 and 24 h. For lower station densities (1/400 and 1/800 km²), the CPM-informed method outperforms the traditional method, with average reductions in fractional standard error of 24 %, 13 %, and 8 % for return periods of 2, 10 and 50 years, respectively for a rain gauge density of 1/800 km², and 16 %, 8 %, and 3 % for density of 1/400 km². Information from CPM simulations can thus be useful for estimating sub-daily extreme rainfall events in ungauged sites, particularly in data-scarce areas in which the density of rain gauges is low.

Examining the Impact of the Built Environment on Multidimensional Urban Vitality: Using Milk Tea Shops and Coffee Shops as New Indicators of Urban Vitality

Urban vitality is a critical driver of sustainable urban development, significantly contributing to the enhancement of human well-being. A thorough and multidimensional comprehension of urban vitality is essential for shaping future urban planning and policy-making. This study, focused on Chengdu, proposes a framework for assessing various dimensions of UV through the distribution of milk tea and coffee shops. Using random forest and multi-scale geographically weighted regression models, this study investigates the factors influencing urban vitality from both mathematical thresholds and spatial heterogeneity, and develops spatial maps of future vitality to inform targeted urban strategies. The results show that (1) the milk tea index is effective in capturing population vitality, while the coffee index is more closely associated with economic vitality and urban renewal; (2) office buildings (13.46%) and commercial complexes (13.70%) have the most significant impact on both economic and population vitality, while the importance of transportation factors has notably decreased; (3) the influence of these factors demonstrates spatial heterogeneity and nonlinear relationships, with subway station density of 0.5–0.8 stations per kilometer being optimal for stimulating both types of vitality. The minimum threshold for economic vitality in a given unit is a housing price exceeding 6000 RMB/m2; (4) the future vitality map suggests that urban planners should pay greater attention to non-central districts with high development potential. Moreover, spontaneous social interactions and consumer behaviors stimulated by various shops are critical components of urban vitality. In designing the physical environment and urban spatial forms, special attention should be given to enhancing the attractiveness of physical spaces and their capacity to accommodate social interaction.

Combining low- and high-frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products

Abstract. In the central Arctic, high-quality water vapour observations are sparse due to the low density of meteorological stations and uncertainties in satellite remote sensing. Different reanalyses also disagree on the amount of water vapour in the central Arctic. The Multidisciplinary drifting Observatory for the Study of the Arctic Climate (MOSAiC) expedition provides comprehensive observations that are suitable for evaluating satellite products and reanalyses. Radiosonde observations provide high-quality water vapour estimates with a high vertical but a low temporal resolution. Observations from the microwave radiometers (MWRs) on board the research vessel Polarstern complement these observations through high temporal resolution. In this study, we demonstrate the high accuracy of the combination of the two MWRs HATPRO (Humidity and Temperature Profiler) and MiRAC-P (Microwave Radiometer for Arctic Clouds – Passive). For this purpose, we developed new retrievals of integrated water vapour (IWV) and profiles of specific humidity and temperature using a neural network approach, including observations from both HATPRO and MiRAC-P to utilize their different water vapour sensitivity. The retrievals were trained with the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis version 5 (ERA5) and synthetic MWR observations simulated with the Passive and Active Microwave radiative TRAnsfer tool (PAMTRA). We applied the retrievals to synthetic and real observations and evaluated them with ERA5 and radiosondes launched during MOSAiC, respectively. To assess the benefit of the combination of HATPRO and MiRAC-P compared to single MWR retrievals, we compared the errors with respect to MOSAiC radiosondes and computed the vertical information content of the specific humidity profiles. The root mean squared error (RMSE) of IWV was reduced by up to 15 %. Specific humidity biases and RMSE were reduced by up to 75 % and 50 %, respectively. The vertical information content of specific humidity could be increased from 1.7 to 2.4 degrees of freedom. We also computed relative humidity from the retrieved temperature and specific humidity profiles and found that RMSE was reduced from 45 % to 15 %. Finally, we show a case study demonstrating the enhanced humidity profiling capabilities compared to the standard HATPRO-based retrievals. The vertical resolution of the retrieved specific humidity profiles is still low compared to radiosondes, but the case study revealed the potential to resolve major humidity inversions. To what degree the MWR combination detects humidity inversions, also compared to satellites and reanalyses, will be part of future work.

A Stochastic Approach to the Power Requirements of the Electric Vehicle Charging Infrastructure: The Case of Spain

Battery electric vehicles represent a technological pathway for reducing carbon emissions in personal road transport. However, for the widespread adoption of this type of vehicle, the user experience should be similar to that of combustion engine vehicles. To achieve this objective, a robust and reliable public charging infrastructure is essential. In Spain, the electric recharging infrastructure is growing quickly in metropolitan areas but much more slowly on roads and highways. The upcoming charging stations must be located along high-volume traffic corridors and in proximity to the Trans-European Transport Network. The main contribution of this research is to offer a method for examining the essential electricity infrastructure investments required in scenarios involving substantial electric vehicle adoption. The methodology includes a sensitivity analysis of fleet composition and market share, recharging user behavior, charging station density, and vehicle efficiency improvements. To this end, the authors have developed a simplified probabilistic model, addressing the effect of the involved parameters through a comprehensive scenario analysis. The results show that the actual number of high-capacity charging plugs on Spanish roads is significantly lower than the European regulation requirements for the year 2030 considering an electric vehicle market share according to the Spanish Integrated National Energy and Climate Plan 2021–2030 objectives and it is far from the necessary infrastructure to cover the expected demand according to the traffic flow. Under these circumstances, the charging peak power demand reaches over 7.4% of the current Spanish total power demand for an electric vehicle fleet, which corresponds to only 12% of the total.

The efficacy of attractive targeted sugar baits in reducing malaria vector abundance in low-endemicity settings of northwest Mali

BackgroundAttractive targeted sugar baits (ATSBs) have the potential to significantly reduce infective female Anopheles mosquitoes in arid areas, such as in Northern Mali. Malaria is epidemic in the north due to the limited viability of Anopheles species in the desert climate.The goal of this study was to determine of the effect of ATSB on the number of older female An. gambiae and on the number of sporozoite-positive females in villages in northern Mali.MethodsVillages were located in the north of Mali. In this study, 5677 ATSB stations were deployed, two on each home, in ten villages during late July and early August 2019. Ten villages served as controls. After a pre-treatment monitoring period in July, An. gambiae populations were monitored again from August to December using CDC-UV light traps, pyrethrum spray catches (PSC), and human landing catches (HLC). Mosquitoes were dissected to estimate their age, while ELISA detected sporozoite positivity. The monthly entomological inoculation rates (EIRs) were calculated for HLC indoors and outdoors. Data from villages were compared using t-tests, while bait station weighted density versus amount of collected females was checked with a Pearson’s correlation.ResultsA total of 2703 female An. gambiae were caught from treated villages, 4582 from control villages, a 41.0% difference. Dissection of 1759 females showed that ATSB significantly reduced the number of older females. The proportion of older females in treated villages was 0.93% compared to 9.4% in control villages. ELISA analysis of 7285 females showed that bait stations reduced the number of sporozoite-positive females. The infective females in treated villages was 0.30% compared to 2.73% in the controls. The greater the density of bait stations deployed, the fewer the older, infective females (P < 0.05).EIRs were low in control villages except in months when An. gambiae populations were high. EIRs in ATSB placement villages remained zero. Significant reductions (P < 0.0001) in An. gambiae males were observed.ConclusionsBait stations reduced all measures of vector populations in this study. In a low-transmission setting, ATSB has the potential to greatly reduce malaria.

Understanding the interplay among urban public transport modes: Spatial variation and built environment effects

Research on public transport systems is continually driven by the societal benefits of transport services. However, the nuanced effects of the built environment on public transport mode shares across different modes have received little attention. Using multi-source travel data from Shenzhen, this study presents an analytical framework, which integrates the light gradient boosting machine and Shapley additive explanations, to delve into the complex interplay among public transport modes. The results reveal that most origin-destination pairs with available metro services exhibit a metro share exceeding 50%, underscoring the high attractiveness of the metro. Travel distance emerges as the primary determinant of mode share, with transport-related characteristics proving more influential than land use modifications. This study identifies nonlinear effects of the built environment on mode share, with specific thresholds for built environment characteristics suggesting fine-tuned planning strategies. Metro station densities of 2 counts/km2 correlates with increased metro share, while bus stop densities of 15 counts/km2 are associated with higher bus share. Areas within 2 km of a metro station or with bus stop densities below 15 counts/km2 tend to have higher bike shares. Specific land use ratios and high levels of land use mix are linked to increased shares of certain modes. The findings suggest optimizing bus services in relation to metro availability and offer guidance for balanced planning in promoting metro and shared mobility to achieve a sustainable transport system.

Exploring the complex relationship between metro and shared bikes in the built environment: Competition, connection, and complementation

Merging the flexibility of bike-sharing systems with the high capacity of metro transit significantly enhances both connectivity and efficiency in urban transportation, promoting eco-friendly travel options and supporting sustainable urban development. Current studies primarily examine how these two transportation modes work together to enhance urban travel efficiency and convenience. However, there is still a lack of discussion on the spatial heterogeneity of the competitive and complementary relationships between two modes across different built environments. This study selects Shenzhen as a case study and employs a data-driven approach to explore the relationships between bike-sharing and the metro system in practical application, including competition, connection, and complementation. The OPGD model is deployed to assess how the built environment influences these dynamics. The results reveal that bike-sharing typically complements the metro system, with longer ride durations occurring mainly in the urban core areas. Conversely, competitive interactions between these two modes are less frequent and associated with shorter rides, typically occurring in locales with a high density of metro stations. Educational, service, and residential factors are the main influences on people's choice of the "bike-sharing + metro" travel mode. The built environment exerts a greater impact on competitive relationships and less on complementary ones.

Geostatistical Simulation of Daily Rainfall Fields—Performance Assessment for Extremes in West Africa

Abstract The spatial description of high-resolution extreme daily rainfall fields is challenging because of the high spatial and temporal variability of rainfall, particularly in tropical regions due to the stochastic nature of convective rainfall. Geostatistical simulations offer a solution to this problem. In this study, a stochastic geostatistical simulation technique based on the spectral turning bands method is presented for modeling daily rainfall extremes in the data-scarce tropical Ouémé River basin (Benin). This technique uses meta-Gaussian frameworks built on Gaussian random fields, which are transformed into realistic rainfall fields using statistical transfer functions. The simulation framework can be conditioned on point observations and is computationally efficient in generating multiple ensembles of extreme rainfall fields. The results of tests and evaluations for multiple extremes demonstrate the effectiveness of the simulation framework in modeling more realistic rainfall fields and capturing their variability. It successfully reproduces the empirical cumulative distribution function of the observation samples and outperforms classical interpolation techniques like ordinary kriging in terms of spatial continuity and rainfall variability. The study also addresses the challenge of dealing with uncertainty in data-poor areas and proposes a novel approach for determining the spatial correlation structure even with low station density, resulting in a performance boost of 9.5% compared to traditional techniques. Additionally, we present a low-skill reference simulation method to facilitate a comprehensive comparison of the geostatistical simulation approaches. The simulations generated have the potential to provide valuable inputs for hydrological modeling.

Cross-border competition in the gasoline retail market: Impact of proximity at the German-Polish border

In this article, I analyze how persistently lower gasoline prices in Poland affect the prices set in the German border region. Based on a complete dataset of German gasoline prices and an assessment of driving distances between gasoline stations, I estimate the impact of one additional kilometer of distance to the nearest Polish competitor on the price charged by German gasoline stations. Following the fixed effects filtered estimator approach proposed by Pesaran and Zhou (2018) and controlling for various station characteristics, I find no evidence that German gasoline stations enter price competition with their Polish counterparts. My descriptive analysis of gasoline station infrastructure in the German border region reveals increasingly sparse gasoline station density when approaching the Polish border, along with an increasing share of premium brands. These results may reflect the effect of substantially lower taxes in Poland, discouraging German gasoline stations from locating near the border and effectively ruling out cross-border price competition. Although indirect, my findings suggest the presence of fuel tourism.

Urban traffic congestion and its association with gas station density: insights from Google Maps data

Analyzing air pollution caused by traffic conditions requires appropriate indicators. Currently, air pollution indicators are approximated by the number of vehicles and gas station density. However, this approach cannot provide information at a smaller level. This study aims to identify traffic congestion distribution from Google Maps data as an alternative air pollution indicator at smaller level using map digitization method. In addition, this study examines its relationship with the existing indicator called gas station density. The results show that the digitization method can map the traffic congestion distribution where most areas in West, North, and Central Jakarta are classified as high traffic. In addition, this study found that there is a strong and significant relationship of 0.58277 between traffic congestion distribution and gas station density. Thus, traffic congestion distribution and gas station density data from Google Maps can be used as an indicator of traffic-related air pollution, especially land transportation. Furthermore, this research is expected to serve as a basis for the government in determining mitigation strategies related to traffic congestion and the resulting emissions.

Investigation on the 2018 prolonged freezing event in South China using the WRF model at convection-permitting resolution: Model performance and sensitivity analysis of physical parameterizations

The coincidence of extreme cold, precipitation, and freezing weather is one of the most significant natural disasters in winter, with extensive impacts on public transportation, the energy industry, and the ecological landscape. Despite the importance of such events, comprehensive analyses of the performance of cutting-edge numerical weather prediction models at convection-permitting scales remain inadequate. This paper investigates the prolonged freezing event that occurred from January 24 to 28, 2018 in South China using the Weather Research and Forecasting (WRF) model with a horizontal grid spacing of 3 km. The focus is on evaluating the performance of the WRF model and the impact of microphysical parameterizations and nudging techniques. The results show that the WRF model successfully captures the main features of the prolonged freezing event, including the low-level collision of cold and warm air in the lower reaches of the Yangtze River valley and the spatial distribution of precipitation. However, the model overestimates the strength of warm air from the south, leading to a northward shift of the precipitation belt. The choice of physical parameterizations significantly influences model performance, with microphysical schemes of P3, Thompson, and MYDM showing better model skill in terms of root mean square error (RMSE). The analysis also highlights the importance of nudging techniques, particularly grid nudging, which significantly improves model performance. Furthermore, the inclusion of local, very dense station observations from the China Meteorological Administration (CMA) during the nudging process provides additional improvement. A further investigation reveals that the accuracy in reproducing low-level convergence over South China is the main factor influencing model performance across different sensitivity simulations. This study underscores the value of convection-permitting simulations in accurately reproducing extreme freezing events over China.

Global-scale ERA5 product precipitation and temperature evaluation

ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis Fifth Generation) is a significant dataset in climate reanalysis. Conducting a global-scale accuracy assessment and spatiotemporal distribution characteristic study of its temperature and precipitation data is crucial for gaining in-depth understanding of extreme climate changes. This study, based on CRU TS v4.04 (Climatic Research Unit Time-Series version 4.04) gridded data, employs multiple statistical indicators and detection metrics to assess the accuracy of the ERA5 temperature and precipitation dataset at a monthly scale over the past 70 years. Additionally, it utilizes Empirical Orthogonal Functions (EOF) to explore its spatiotemporal distribution characteristics. The results indicate that: (1) ERA5 demonstrates good consistency in most regions globally; however, the influence of uneven station density distribution results in weaker correlation in areas with sparse station density. (2) The accuracy of ERA5 data exhibits certain differences between the two periods (1950–1979, 1979–2019); with the overall performance of ERA5 temperature data near the Earth’s surface being better than that of precipitation data. (3) ERA5 precipitation data in the mid and low latitudes of the Southern and Northern Hemispheres exhibit characteristics of positive–negative distribution, while temperature data show an opposite distribution in the Northern and Southern Hemispheres (with a predominant pattern of warmer temperatures in the north and colder temperatures in the south). Additionally, the typical patterns for both variables occur in January and July each year. The above study will provide reference and insights for future climate change prediction and adaptation efforts.

Understanding human mobility and trip demand through sparse trajectories of private e-bikes

Understanding human mobility and trip demand through e-bike trajectories is crucial for urban planning, environmental enhancement, and sustainable development. However, existing studies predominantly focus on shared (e−)bike trips, neglecting private e-bike trips. With the recent availability of sparse trajectory for private e-bikes, we established a novel analysis framework to reveal human mobility and trip demand in Wuhan, China. First, we propose a two-step method for extracting trip behavior from sparse trajectories of private e-bikes, involving the identification of staying areas and the generation of e-bike trips. Second, we establish a spatial random forest method to capture the nonlinear relationship between private e-bike trips and driving factors. Finally, we use the interpretable SHAP method to reveal the driving mechanisms of e-bike trips and explore the impact of various factors on these trips. The results indicate that (1) trip distances of private e-bikes follow a lognormal distribution, with an Adj. R-Square of 0.99, while trip times exhibit a Hill distribution, with an Adj. R-Square of 0.95; (2) Private e-bike trips are not commonly employed to address the first/last mile problem in public transportation and are more frequently used for daily commuting needs, with over 65% of these trips covering distances greater than 1 km or lasting longer than 5 min; (3) private e-bike trips positively correlate with the density of POIs like Hospital, School, and Transportation Station. However, compared to shared (e−)bike trips, Transportation Station Density, especially Metro Station Density, is less important for private e-bike trips; and (4) private e-bike trips are also positively correlated with Congestion Level and House Price, meaning that areas with severe traffic congestion or high housing prices tend to have more private e-bike trips. This study provides a new framework for understanding private e-bike trip patterns, also helping authorities better grasp the factors influencing e-bike trip demand.

Investigating the Nonlinear Effect of Land Use and Built Environment on Public Transportation Choice Using a Machine Learning Approach

Understanding the relationship between the demand for public transportation and land use is critical to promoting public-transportation-oriented urban development. Taking Beijing as an example, we took the Public Transportation Index (PTI) during the working day’s early peak hours as the dependent variable. And 15 land use and built environment variables were selected as the independent variables according to the “7D” built environment dimensions. According to the Modifiable Areal Unit Problem (MAUP), the size and shape of the spatial units will affect the aggregation results of the dependent variable and the independent variables. To find the ideal spatial unit division method, we assess how well the nonlinear model fits several spatial units. Extreme Gradient Boosting (XGBoost) was utilized to investigate the nonlinear effects of the built environment on PTI and threshold effects based on the ideal spatial unit. The results show that (1) the best spatial unit division method is based on traffic analysis zones (TAZs); (2) the top four explanatory variables affecting PTI are, in order: mean travel distance, residential density, subway station density, and public services density; (3) there are nonlinear relationships and significant threshold effects between the land use variables and PTI. The priority regeneration TAZs were identified according to the intersection analysis of the low PTI TAZs set and the PTI-sensitive TAZs set based on different land use variables. Prioritized urban regeneration TAZs require targeted strategies, and the results of the study may provide a scientific basis for proposing strategies to renew land use to increase PTI.

Evaluation of the In2Care Mosquito Station at low deployment density: a field study to manage Aedes aegypti and Culex quinquefasciatus (Diptera: Culicidae) in North Central Florida.

In the last 2 decades, there has been an increase in the geographic range and frequency of vector-borne diseases. Management of mosquito populations has become challenging due to increasing rates of resistance to existing insecticidal products and formulations. Several alternative tools have emerged to suppress or replace mosquito populations. One of these tools is the In2Care Mosquito Station (In2Care station). This dual-action station contains the insect growth regulator pyriproxyfen which disrupts the development of immatures and the entomopathogenic fungus Beauveria bassiana (B. bassiana) strain GHA which kills exposed adult mosquitoes. The In2Care stations have previously been shown to effectively control Aedes aegypti in field settings at a density of 6 stations/acre rather than the label-recommended 10 stations/acre. To further test the efficacy of low station density deployment, we deployed In2Care stations in the Pleasant Street Historic District of Gainesville, Florida, at a density of 3 stations/acre over a period of 2 years in the presence or absence of ground larvicidal applications. The deployment of stations resulted in no measurable impact on Ae. aegypti and Culex quinquefasciatus adult or immature abundance suggesting that the low-density deployment of In2Care stations is insufficient to reduce Ae. aegypti and Cu. quinquefasciatus abundance within treatment areas.

Energy-Magnitude Station Corrections across the Conterminous United States

Abstract The determination of source parameters requires corrections for propagation and site effects. The source parameter of interest for this study is the radiated seismic energy, which we use in turn to compile a global energy magnitude Me catalog. The energy estimated for P waves recorded at distances between 20° and 98° is corrected for propagation effects using the numerical Green’s function for the AK135Q model, in which the correction is performed in the spectral domain over the frequency range 0.012–1 Hz. To quantify the impact of site effects on Me, we perform a mixed-effects regression to isolate repeated station effects from event and propagation contributions to the overall magnitude variability. We find that the standard deviation of the interstation residuals is 0.19, whereas the standard deviation of the path-to-path residuals is 0.24. To discuss the spatial variability of the station-specific magnitude residuals, we analyze the results obtained for permanent and temporary networks installed in the conterminous United States. The high density of stations considered (4502 stations from 97 networks) allows us to compare a map of the station corrections with the physiographic classification of the United States. We find a good agreement, with positive magnitude corrections for stations installed in the Atlantic Plain and in the Interior Plains divisions; negative corrections characterize installations in the Rocky Mountains system, in the Interior Highlands, in the ranges of the Pacific Mountains division and in the Appalachian Highlands. Moreover, the alternation of positive and negative magnitude corrections corresponds well with the province and section subdivisions.

The role of citizen science in assessing the spatiotemporal pattern of rainfall events in urban areas: a case study in the city of Genoa, Italy

Abstract. Climate change in the Mediterranean region is manifesting itself as an increase in average air temperature and a change in the rainfall regime: the value of cumulative annual rainfall generally appears to be constant, but the intensity of annual rainfall maxima, between 1 and 24 h, is increasing, especially in the period between late summer and early autumn. The associated ground effects in urban areas consist of flash floods and pluvial floods, often in very small areas, depending on the physical-geographical layout of the region. In the context of global warming, it is therefore important to have an adequate monitoring network for rain events that are highly concentrated in space and time. This research analyses the meteo-hydrological features of the 27 and 28 August 2023 event that occurred in the city of Genoa, Italy, just 4 d after the record maximum air temperature was recorded: between 19:00 and 02:00 UTC almost 400 mm of rainfall was recorded in the eastern sector of the historic centre of Genoa, with significant ground effects such as flooding episodes and the overflowing of pressurised culverts. Rainfall observations and estimates were made using both official or “authoritative” networks (rain gauges and meteorological radar) and rain gauge networks inspired by citizen science principles. The combined analysis of observations from authoritative and citizen science networks reveals, for the event analysed, a spatial variability of the precipitation field at an hourly and a sub-hourly timescale that cannot be captured by the current spatial density of the authoritative measurement stations (which have one of the highest densities in Italy). Monthly total rainfall and short-duration annual maximum time series recorded by the authoritative rain gauge network of the Genoa area are then analysed. The results show significant variation even at distances of less than 2 km in the average rainfall depth accumulated over sub-hourly duration. Extreme weather monitoring activity is confirmed as one of the most important aspects in terms of flood prevention and protection in urban areas. The integration between authoritative and citizen science networks can prove to be a valid contribution to the monitoring of extreme events.

A study of prehospital EMS response time and influencing factors in the main urban area of Chongqing, China.

Shortening the prehospital emergency medical service (EMS) response time is crucial for saving lives and lowering mortality and disability rates in patients with sudden illnesses. Descriptive analyses of prehospital EMS response time and each component were conducted separately using ambulance trip data from the 120 Dispatch Command Centre in the main urban area of Chongqing in 2021, and then, logistic regression analyses were used to explore the influencing factors. The median prehospital EMS response time in the main urban area of Chongqing was 14.52 min and the mean was 16.14 min. A 44.89% of prehospital EMS response time exceeded 15 min. Response time was more likely to surpass this threshold during peak hours and in high population density areas. Conversely, lower probabilities exceeding 15 min were observed during the night shift, summer, and autumn seasons, and areas with a high density of emergency station. 33.28% of preparation time was >3 min, with the night shift and high population density areas more likely to be >3 min, while for the summer and autumn seasons, high Gross National Product (GDP) per capita areas had a lower likelihood of having preparation time >3 min. 45.52% of travel time was >11 min, with peak hours, summer and autumn, and high GDP per capita areas more likely to have had a travel time >11 min, while night shift and high emergency station density areas had a lower likelihood of travel time >11 min. The primary factors influencing prehospital EMS response time were shifts, traffic scenarios, seasons, GDP per capita, emergency station density, and population density. Relevant departments can devise effective interventions to reduce response time through resource allocation and department coordination, staff training and work arrangement optimisation, as well as public participation and education, thereby enhancing the efficiency of prehospital emergency medical services.

Spatial Characteristics of Multidimensional Urban Vitality and Its Impact Mechanisms by the Built Environment

Urban vitality, intricately connected to urban morphology, has long been a cornerstone of urban planning and design. The accelerated pace of urbanization has created abundant living and working spaces, but it has also brought about a series of issues such as traffic congestion, environmental pollution, insufficient public spaces, and uneven urban development, leading to a decline in urban vitality. The spatial distribution patterns of urban vitality and their influencing factors are diverse and vary across different cities, necessitating a multidimensional exploration of the relationship between urban vitality and the built environment. Utilizing the central urban area of Beijing as a case study, this research leverages multi-source urban spatial data to delineate the spatial characteristics of social, economic, cultural, and comprehensive vitality. Furthermore, a comprehensive set of built-environment indicators is developed across five dimensions to analyze their correlation with urban vitality. The results indicate: (1) There is a significant spatial clustering of various vitality types in Beijing, with a pronounced correlation between high-density population aggregation and vigorous economic activities. (2) Subdistricts exhibiting high social vitality display an “n”-shaped distribution around the Second Ring Road. In contrast, those with high economic vitality are concentrated along Chang’an Street and various district commercial centers. High cultural vitality subdistricts are distributed in a northwest–southeast trajectory from the Fourth Ring Road to the Second Ring Road, and subdistricts with high comprehensive vitality exhibit a concentric distribution radiating outwards from the center. (3) Social vitality is most closely related to comprehensive vitality, and the various vitality types in Beijing’s central urban area develop relatively evenly. (4) The built environment significantly affects all types of urban vitality. Factors such as floor area ratio, POI density, POI mixing degree, and intersection density are fundamental to enhancing urban vitality, whereas the greening rate somewhat inhibits it. (5) Future spatial planning should utilize the radiating effect of high-vitality subdistricts to optimize population distribution, enhance POI mixing, increase metro station density, and strengthen critical urban structures for synergistic economic and cultural development. This study provides a foundation and promotion strategies for optimizing the layout and enhancing vitality at the subdistrict scale within Beijing’s central urban area.