 Subjects -> GEOGRAPHY (Total: 493 journals)
| A B C D E F G H I J K L M N O P Q R S T U V W X Y Z | | The end of the list has been reached or no journals were found for your choice. |
|
|
- Understanding Policy and Technical Aspects of AI-enabled Smart Video
Surveillance to Address Public Safety
Abstract: Recent advancements in artificial intelligence (AI) have seen the emergence of smart video surveillance (SVS) in many practical applications, particularly for building safer and more secure communities in our urban environments. Cognitive tasks, such as identifying objects, recognizing actions, and detecting anomalous behaviors, can produce data capable of providing valuable insights to the community through statistical and analytical tools. However, artificially intelligent surveillance systems design requires special considerations for ethical challenges and concerns. The use and storage of personally identifiable information (PII) commonly pose an increased risk to personal privacy. To address these issues, this paper identifies the privacy concerns and requirements needed to address when designing AI-enabled smart video surveillance. Further, we propose the first end-to-end AI-enabled privacy-preserving smart video surveillance system that holistically combines computer vision analytics, statistical data analytics, cloud-native services, and end-user applications. Finally, we propose quantitative and qualitative metrics to evaluate intelligent video surveillance systems. The system shows the 17.8 frame-per-second (FPS) processing in extreme video scenes. However, considering privacy in designing such a system results in preferring the pose-based algorithm to the pixel-based one. This choice resulted in dropping accuracy in both action and anomaly detection tasks. The results drop from 97.48% to 73.72% in anomaly detection and 96% to 83.07% in the action detection task. On average, the latency of the end-to-end system is 36.1 seconds.
PubDate: 2023-05-18
- Analyzing the impact of COVID-19 on the electricity demand in Austin, TX
using an ensemble-model based counterfactual and 400,000 smart meters
Abstract: The COVID-19 pandemic caused lifestyle changes and has led to the new electricity demand patterns in the presence of non-pharmaceutical interventions such as work-from-home policy and lockdown. Quantifying the effect on electricity demand is critical for future electricity market planning yet challenging in the context of limited smart metered buildings, which leads to limited understanding of the temporal and spatial variations in building energy use. This study uses a large scale private smart meter electricity demand data from the City of Austin, combined with publicly available environmental data, and develops an ensemble regression model for long term daily electricity demand prediction. Using 15-min resolution data from over 400,000 smart meters from 2018 to 2020 aggregated by building type and zip code, our proposed model precisely formalizes the counterfactual universe in the without COVID-19 scenario. The model is used to understand building electricity demand changes during the pandemic and to identify relationships between such changes and socioeconomic patterns. Results indicate the increase in residential usage , demonstrating the spatial redistribution of energy consumption during the work-from-home period. Our experiments demonstrate the effectiveness of our proposed framework by assessing multiple socioeconomic impacts with the comparison between the counterfactual universe and observations.
PubDate: 2023-05-06
- Seasonal characteristics of crime: an empirical investigation of the
temporal fluctuation of the different types of crime in London
Abstract: Most types of crimes show seasonal fluctuations but the difference and similarity of the periodicity between different crimes are understudied. Interpreting the seasonality of different crime types and formulating clusters of crimes that share similar seasonal characteristics would help identify the common underlying factors and revise the patterns of patrolling and monitoring to enable sustained management of the control strategies. This study proposes a new methodological framework for measuring similarities and differences in the timing of peaks and troughs, as well as the waveforms of different crimes. The method combines a Poisson state-space model with cluster analysis and multi-dimensional scaling. A case study using twelve types of crimes in London (2013–2020) demonstrated that the amplitude of the seasonal fluctuation identified by this method explained 95.2% of the similarity in their waveforms, while the timing of the peaks covered 87.5% of the variance in their seasonal fluctuation. The high predictability of the seasonal patterns of crimes as well as the stable categorisation of crimes with similar seasonal characteristics enable sustainable and measured planning of police resource allocation and, thereby, facilitates a more efficient management of the urban environment.
PubDate: 2023-05-04
- Why do we love the high line' A case study of understanding long-term user
experiences of urban greenways
Abstract: The High Line park (HLP) in New York City is one of the most successful contemporary greenway parks, inspiring urban planners, designers, artists, and administrators worldwide. This study provides a comprehensive understanding of user experiences in a long-term time frame (2011–2018) through the lens of online reviews. Using a mixed-methods approach, we conducted Latent Dirichlet Allocation (LDA) topic modeling to quantitatively identify the key topics that represent 34,060 reviews and 30,285 users, followed by qualitative analysis to inductively interpret the LDA topics. The results identified experiential, programmatic and physical elements of the HLP that are meaningful to users. Three primary components were found that make HLP successful according to users: spectacular visual and activity-related experiences, the historical transformation and cultural exploration, and the added value from park services ranging from amenities to on-site living performance. The article helps inform future decision-making and planning & design practices for future greenway projects.
PubDate: 2023-04-27
- Scalable flood inundation mapping using deep convolutional networks and
traffic signage
Abstract: Floods are one of the most prevalent and costliest natural hazards globally. The safe transit of people and goods during a flood event requires fast and reliable access to flood depth information with spatial granularity comparable to the road network. In this research, we propose to use crowdsourced photos of submerged traffic signs for street-level flood depth estimation and mapping. To this end, a deep convolutional neural network (CNN) is utilized to detect traffic signs in user-contributed photos, followed by comparing the lengths of the visible part of detected sign poles before and after the flood event. A tilt correction approach is also designed and implemented to rectify potential inaccuracy in pole length estimation caused by tilted stop signs in floodwaters. The mean absolute error (MAE) achieved for pole length estimation in pre- and post-flood photos is 1.723 and 2.846 in., respectively, leading to an MAE of 4.710 in. for flood depth estimation. The presented approach provides people and first responders with a reliable and geographically scalable solution for estimating and communicating real-time flood depth data at their locations.
PubDate: 2023-04-06
- Enhanced Two-Step Virtual Catchment Area (E2SVCA) model to measure
telehealth accessibility
Abstract: The use of telehealth has increased significantly over the last decade and has become even more popular and essential during the COVID-19 pandemic due to social distancing requirements. Telehealth has many advantages including potentially improving access to healthcare in rural areas and achieving healthcare equality. However, there is still limited research in the literature on how to accurately evaluate telehealth accessibility. Here we present the Enhanced Two-Step Virtual Catchment Area (E2SVCA) model, which replaces the binary broadband strength joint function of the previous Two-Step Virtual Catchment Area (2SVCA) with a step-wise function that more accurately reflects the requirements of telehealth video conferencing. We also examined different metrics for representing broadband speed at the Census Block level and compared the results of 2SVCA and E2VCA. Our study suggests that using the minimum available Internet speed in a Census Block can reveal the worst-case scenario of telehealth care accessibility. On the other hand, using the maximum of the most frequent available speeds reveals optimal accessibility, while the minimum of the most frequent reflects a more common case. All three indicators showed that the 2SVCA model generally overestimates accessibility results. The E2SVCA model addresses this limitation of the 2SVCA model, more accurately reflects reality, and more appropriately reveals low accessibility regions. This new method can help policymakers in making better decisions about healthcare resource allocations aiming to improve healthcare equality and patient outcomes.
PubDate: 2023-04-03
- Predicting road flooding risk with crowdsourced reports and fine-grained
traffic data
Abstract: The objective of this study is to predict road flooding risks based on topographic, hydrologic, and temporal precipitation features using machine learning models. Existing road inundation studies either lack empirical data for model validations or focus mainly on road inundation exposure assessment based on flood maps. This study addresses this limitation by using crowdsourced and fine-grained traffic data as an indicator of road inundation, and topographic, hydrologic, and temporal precipitation features as predictor variables. Two tree-based machine learning models (random forest and AdaBoost) were then tested and trained for predicting road inundations in the contexts of 2017 Hurricane Harvey and 2019 Tropical Storm Imelda in Harris County, Texas. The findings from Hurricane Harvey indicate that precipitation is the most important feature for predicting road inundation susceptibility, and that topographic features are more critical than hydrologic features for predicting road inundations in both storm cases. The random forest and AdaBoost models had relatively high AUC scores (0.860 and 0.810 for Harvey respectively and 0.790 and 0.720 for Imelda respectively) with the random forest model performing better in both cases. The random forest model showed stable performance for Harvey, while varying significantly for Imelda. This study advances the emerging field of smart flood resilience in terms of predictive flood risk mapping at the road level. In particular, such models could help impacted communities and emergency management agencies develop better preparedness and response strategies with improved situational awareness of road inundation likelihood as an extreme weather event unfolds.
PubDate: 2023-03-21
- DigitalExposome: quantifying impact of urban environment on wellbeing
using sensor fusion and deep learning
Abstract: The increasing level of air pollutants (e.g. particulates, noise and gases) within the atmosphere are impacting mental wellbeing. In this paper, we define the term ‘DigitalExposome’ as a conceptual framework that takes us closer towards understanding the relationship between environment, personal characteristics, behaviour and wellbeing using multimodal mobile sensing technology. Specifically, we simultaneously collected (for the first time) multi-sensor data including urban environmental factors (e.g. air pollution including: Particulate Matter (PM1), (PM2.5), (PM10), Oxidised, Reduced, Ammonia (NH3) and Noise, People Count in the vicinity), body reaction (physiological reactions including: EDA, HR, HRV, Body Temperature, BVP and movement) and individuals’ perceived responses (e.g. self-reported valence) in urban settings. Our users followed a pre-specified urban path and collected the data using a comprehensive sensing edge device. The data is instantly fused, time-stamped and geo-tagged at the point of collection. A range of multivariate statistical analysis techniques have been applied including Principle Component Analysis, Regression and Spatial Visualisations to unravel the relationship between the variables. Results showed that Electrodermal Activity (EDA) and Heart Rate Variability (HRV) are noticeably impacted by the level of Particulate Matter in the environment. Furthermore, we adopted Convolutional Neural Network (CNN) to classify self-reported wellbeing from the multimodal dataset which achieved an f1-score of 0.76.
PubDate: 2023-03-20
- Spatial analysis and optimization of self-pickup points of a new retail
model in the Post-Epidemic Era: the case of Community-Group-Buying in
Xi’an City
Abstract: The Community-Group-Buying Points (CGBPs) flourished during COVID-19, safeguarding the daily lives of community residents in community lockdowns, and continuing to serve as a popular daily shopping channel in the Post-Epidemic Era with its advantages of low price, convenience and neighborhood trust. These CGBPs are allocated on location preferences however spatial distribution is not equal. Therefore, in this study, we used point of interest (POI) data of 2,433 CGBPs to analyze spatial distribution, operation mode and accessibility of CGBPs in Xi’an city, China as well as proposed the location optimization model. The results showed that the CGBPs were spatially distributed as clusters at α = 0.01 (Moran’s I = 0.44). The CGBPs operation mode was divided into preparation, marketing, transportation, and self-pickup. Further CGBPs were mainly operating in the form of joint ventures, and the relying targets presented the characteristic of ‘convenience store-based and multi-type coexistence’. Influenced by urban planning, land use, and cultural relics protection regulations, they showed an elliptic distribution pattern with a small oblateness, and the density showed a low–high-low circular distribution pattern from the Palace of Tang Dynasty outwards. Furthermore, the number of communities, population density, GDP, and housing type were important driving factors of the spatial pattern of CGBPs. Finally, to maximize attendance, it was suggested to add 248 new CGBPs, retain 394 existing CGBPs, and replace the remaining CGBPs with farmers’ markets, mobile vendors, and supermarkets. The findings of this study would be beneficial to CGB companies in increasing the efficiency of self-pick-up facilities, to city planners in improving urban community-life cycle planning, and to policymakers in formulating relevant policies to balance the interests of stakeholders: CGB enterprises, residents, and vendors.
PubDate: 2023-03-20
- Evaluating urban green and blue spaces with space-based multi-sensor
datasets for sustainable development
Abstract: Urban green and blue spaces refer to the natural and semi-natural areas within a city or urban area. These spaces can include parks, gardens, rivers, lakes, and other bodies of water. They play a vital role in the sustainability of cities by providing a range of ecosystem services such as air purification, carbon sequestration, water management, and biodiversity conservation. They also provide recreational and social benefits, such as promoting physical activity, mental well-being, and community cohesion. Urban green and blue spaces can also act as buffers against the negative impacts of urbanization, such as reducing the heat island effect and mitigating the effects of stormwater runoff. Therefore, it is important to maintain and enhance these spaces to ensure a healthy and sustainable urban environment. Assessing urban green and blue spaces with space-based multi-sensor datasets can be a valuable tool for sustainable development. These datasets can provide information on the location, size, and condition of green and blue spaces in urban areas, which can be used to inform decisions about land use, conservation, and urban planning. Space-based sensors, such as satellites, can provide high-resolution data that can be used to map and monitor changes in these spaces over time. Additionally, multi-sensor datasets can be used to gather information on a variety of environmental factors, such as air and water quality, that can impact the health and well-being of urban residents. This information can be used to develop sustainable solutions for preserving and enhancing urban green and blue spaces. This study examines how urban green and blue infrastructures might improve sustainable development. Space-based multi-sensor datasets are used to estimate urban green and blue zones for sustainable development. This work can inform sustainable development research at additional spatial and temporal scales.
PubDate: 2023-03-17
DOI: 10.1007/s43762-023-00091-0
- How do taxi usage patterns vary and why' A dynamic spatiotemporal
analysis in Beijing
Abstract: Existing studies lack attention to taxi usage dynamics, considering its trip proportion over other travel modes and its influencing factors at fine spatiotemporal resolutions. To fill these gaps, we propose a method for examining taxi usage in a grid of 1 km × 1 km cells per hour during a one-day cycle in Beijing. This method measures the differences between taxi trips from taxi trajectory data and mobile signaling data in the same week in January 2017. To explain the spatiotemporal variation in taxi usage, multiple linear models were used to investigate taxi usage dynamics with alternative transport modes, socioeconomic factors, and built environments. In summary, this study proposes to develop an indicator to measure taxi usage using multiple data sources. We confirm that taxi usage dynamics exist in both temporal and spatial dimensions. In addition, the effects of taxi usage factors vary over each hour in a one-day cycle. These findings are useful for urban planning and transport management, in which the dynamic interactions between taxi demand and distribution of facilities should be included.
PubDate: 2023-03-06
DOI: 10.1007/s43762-023-00087-w
- Are older adults living in compact development more active' – Evidence
from 36 diverse regions of the United States
Abstract: With the population of older adults growing globally, this study asks the question: are older adults living in compact developments more active than those living in sprawling developments' Older adults can be deemed more active if they travel more in total or travel more by non-auto travel modes (such as walking, transit). By analyzing disaggregated data from 36 regions of the United States, this study finds that older adults living in compact neighborhoods do not travel more in total but travel more by walking and public transportation than those living in sprawling neighborhoods. In addition, older adults travel less, are more auto-dependent, and make more home-based-nonwork trips, compared to younger adults. Older adults with lower income travel less than those with higher income. Older adults living in compact neighborhoods with the lowest income level generate the highest number of transit trips. It is important for planners and policy makers to not only create built environments that support older adults’ travel needs, but also to avoid social inequity.
PubDate: 2023-03-02
DOI: 10.1007/s43762-023-00086-x
- Uncovering the spatiotemporal evolution of the service industry based on
geo-big-data- a case study on the bath industry in China
Abstract: The bath industry has multiple attributes, such as economic, health, and cultural communication. Therefore, exploring this industry's spatial pattern evolution is crucial to forming a healthy and balanced development model. Based on POI (Points of Interest) and population migration data, this paper uses spatial statistics and radial basis function neural network to explore the spatial pattern evolution and influencing factors of the bath industry in mainland China. The results show that: (1) The bath industry presents a strong development pattern in the north, south-northeast, and east-northwest regions and weak development in the rest of the country. As a result, the spatial development of new bath space is more malleable. (2) The input of bathing culture has a guiding role in developing the bath industry. The growth of market demand and related industries has a specific influence on the development of the bath industry. (3) Improving the bath industry's adaptability, integration, and service level are feasible to ensure healthy and balanced development. (4) Bathhouses should improve their service system and risk management control during the pandemic.
PubDate: 2023-02-27
DOI: 10.1007/s43762-023-00085-y
- Delay in timing and spatial reorganization of rainfall due to
urbanization- analysis over India’s smart city Bhubaneswar
Abstract: Bhubaneswar is the first designed ‘smart city’ in India and has experienced rapid urbanization since 2000. The question undertaken in this study is to assess if there is a change in the rainfall over this rapidly urbanizing region, and if so, what are the characteristics of the change' The broader intent is to understand if the change in urbanization and rainfall are interlinked' The India Meteorological Department (hourly station and daily gridded) and Tropical Rainfall Measurement Mission (3-hourly) datasets are analyzed for the 1980–2018 period (39 years) for different seasons separately. Wavelet and trend analysis reveal that precipitation intensity has increased over the study period. The assessments of the hourly rainfall data show an interesting feature. There is a decrease in the midnight to early-morning rain, with a corresponding increase in the late-afternoon to midnight rainfall. The increase in the rainfall is preferentially downwind and on the east side of the city. A supervised classified land use land cover map of the Bhubaneswar region is developed for 1980, 1990, 2000, 2010, and 2019 using Landsat imagery to compute the urban sprawl. The urban area and population density over Bhubaneswar is increasing with time. Analysis of the LULC and rainfall data indicates that the rainfall over urban regions and the shift in the timing of rains to evenings is highly correlated with the urban sprawl.
PubDate: 2023-02-23
DOI: 10.1007/s43762-023-00081-2
- Impact of COVID-19 on online grocery shopping discussion and behavior
reflected from Google Trends and geotagged tweets
Abstract: People express opinions, make connections, and disseminate information on social media platforms. We considered grocery-related tweets as a proxy for grocery shopping behaviors or intentions. We collected data from January 2019 to January 2022, representing three typical times of the normal period before the COVID-19 pandemic, the outbreak period, and the widespread period. We obtained grocery-related geotagged tweets using a search term index based on the top 10 grocery chains in the US and compiled Google Trends online grocery shopping data. We performed a topic modeling analysis using the Latent Dirichlet Allocation (LDA), and verified that most of the collected tweets were related to grocery-shopping demands or experiences. Temporal and geographical analyses were applied to investigate when and where people talked more about groceries, and how COVID-19 affected them. The results show that the pandemic has been gradually changing people’s daily shopping concerns and behaviors, which have become more spread throughout the week since the pandemic began. Under the causal impact of COVID-19, people first experienced panic buying groceries followed by pandemic fatigue a year later. The normalized tweet counts show a decrease of 40% since the pandemic began, and the negative causal effect can be considered statistically significant (p-value = 0.001). The variation in the quantity of grocery-related tweets also reflects geographic diversity in grocery concerns. We found that people in non-farm areas with less population and relatively lower levels of educational attainment tend to act more sensitively to the evolution of the pandemic. Utilizing the COVID-19 death cases and consumer price index (CPI) for food at home as background information, we proposed an understanding of the pandemic’s impact on online grocery shopping by assembling, geovisualizing, and analyzing the evolution of online grocery behaviors and discussion on social media before and during the pandemic.
PubDate: 2023-02-22
DOI: 10.1007/s43762-023-00083-0
- Out-of-school hours care places in Xi’an City of China: location choice,
spatial relationships, and influencing factors
Abstract: s Affected by the burden reduction policy, out-of-school hours care places have become a hot issue of social concern. Taking Xi’an out-of-school hours care places as a research case, this paper discusses its location choice, spatial relationships and influencing factors using methods such as text analysis, spatial analysis, and mathematical statistics. The results show that: (1) the distribution of out-of-school hours care places in Xi’an is closely related to the community and schools. The names mostly use words such as “sunshine,” “teacher,” and “love,” which are mainly distributed on the lower floors (one to three floors), of which the first floor accounts for the largest proportion. (2) The high-value areas of out-of-school hours care places are mainly concentrated in the Lianhu District, Yanta District, Xincheng District, and the north Chang’an District. Their distribution has obvious directionality, showing a “northeast-southwest” trend, and the global spatial autocorrelation is positively correlated. (3) The spatial pattern of out-of-school hours care places is basically consistent with that of primary and secondary schools, and most of them are located within 1000 m of it. (4) The influencing factors mainly include the distribution of primary and secondary schools, residential areas, population density, house rent, and policies.
PubDate: 2023-02-20
DOI: 10.1007/s43762-023-00084-z
- The impacts of land cover spatial combination on nighttime light intensity
in 2010 and 2020: a case study of Fuzhou, China
Abstract: As human activities highly depend on the land resources and changed the land cover (LC) condition, the relationship between LC and nighttime light (NTL) intensity has been widely analyzed to support the foundation of NTL applications and help explain the drivers of urban economic development. However, previous studies always paid attention to the effect of each LC type on NTL intensity, with limited consideration of the joint effects of any two LC types. To fill this gap, this study measured the land cover spatial combination (LCSC) by using a spatial adjacency matrix, and then analyzed its impacts on NTL intensity based on an extreme gradient boosting (XGBoost) regression model with the assistant of sharpley additive explanations (SHAP) method. Our results presented that the LCSC can better (R2 of 82.4% and 98.1% in 2010 and 2020) explain the relationship between LC and NTL intensity with the traditional LC metrics (e.g., area and patch count), since the LCSC is much more sensitive to the diverse land functions. It is noteworthy that the impacts, as well as their dynamics, of LCSC between any two LC types on NTL intensity are various. LCSC associated with artificial surface contributed more to NTL intensity. In detail, the LCSC of water/wetland and artificial surface can increasingly promote the NTL intensity while the LCSC of grassland/forest and artificial surface has a decreasing or inverse U-shaped contribution to NTL intensity. Whereas LCSC associated with non-artificial surface were not conducive to the increase in NTL intensity due to high vegetation density. We also provided three implications to help further urbanization process and discussed the applications of LCSC.
PubDate: 2023-02-02
DOI: 10.1007/s43762-023-00077-y
- Need for considering urban climate change factors on stroke,
neurodegenerative diseases, and mood disorders studies
Abstract: The adverse health impacts of climate change have been well documented. It is increasingly apparent that the impacts are disproportionately higher in urban populations, especially underserved communities. Studies have linked urbanization and air pollution with health impacts, but the exacerbating role of urban heat islands (UHI) in the context of neurodegenerative diseases has not been well addressed. The complex interplay between climate change, local urban air pollution, urbanization, and a rising population in cities has led to the byproduct of increased heat stress in urban areas. Some urban neighborhoods with poor infrastructure can have excessive heat even after sunset, increasing internal body temperature and leading to hyperthermic conditions. Such conditions can put individuals at higher risk of stroke by creating a persistent neuroinflammatory state, including, in some instances, Alzheimer’s Disease (AD) phenotypes. Components of the AD phenotype, such as amyloid beta plaques, can disrupt long-term potentiation (LTP) and long-term depression (LTD), which can negatively alter the mesolimbic function and thus contribute to the pathogenesis of mood disorders. Furthermore, although a link has not previously been established between heat and Parkinson’s Disease (PD), it can be postulated that neuroinflammation and cell death can contribute to mitochondrial dysfunction and thus lead to Lewy Body formation, which is a hallmark of PD. Such postulations are currently being presented in the emerging field of ‘neurourbanism’. This study highlights that: (i) the impact of urban climate, air pollution and urbanization on the pathogenesis of neurodegenerative diseases and mood disorders is an area that needs further investigation; (ii) urban climate- health studies need to consider the heterogeneity in the urban environment and the impact it has on the UHI. In that, a clear need exists to go beyond the use of airport-based representative climate data to a consideration of more spatially explicit, high-resolution environmental datasets for such health studies, especially as they pertain to the development of locally-relevant climate adaptive health solutions. Recent advances in the development of super-resolution (downscaled climate) datasets using computational tools such as convolution neural networks (CNNs) and other machine learning approaches, as well as the emergence of urban field labs that generate spatially explicit temperature and other environmental datasets across different city neighborhoods, will continue to become important. Future climate – health studies need to develop strategies to benefit from such urban climate datasets that can aid the creation of localized, effective public health assessments and solutions.
PubDate: 2023-01-30
DOI: 10.1007/s43762-023-00079-w
- Understanding cycling mobility: Bologna case study
Abstract: Understanding human mobility in touristic and historical cities is of the utmost importance for managing traffic and deploying new resources and services. In recent years, the need to enhance mobility has been exacerbated due to rapid urbanisation and climate changes. The main objective of this work is to study cycling mobility within the city of Bologna, Italy. We used six months dataset that consists of 320,118 self-reported bike trips. First, we performed several descriptive analysis to understand the temporal and spatial patterns of bike users for understanding popular roads and most favourite points within the city. The findings show how bike users present regular daily and weekly temporal patterns and the characteristics of their trips (i.e. distance, time and speed) follow well-known distribution laws. We also identified several points of interest in the city that are particularly attractive for cycling. Moreover, using several other public datasets, we found that bike usage is more correlated to temperature and precipitation and has no correlation to wind speed and pollution. We also exploited machine learning approaches for predicting short-term trips in the near future (that is for the following 10, 30, and 60 minutes), which could help local governmental agencies with urban planning. The best model achieved an R square of 0.91 for the 30-minute time interval, and a Mean Absolute Error of 2.52 and a Root Mean Squared Error of 3.88 for the 10-minute time interval.
PubDate: 2023-01-29
DOI: 10.1007/s43762-022-00073-8
- Urban modification of heavy rainfall: a model case study for Bhubaneswar
urban region
Abstract: An increase in urbanization has been witnessed from 1980 to 2019 in Bhubaneswar, Odisha. The impact of this increase in urban areas on rainfall pattern and intensity has been assessed in this study. To evaluate these changes, four heavy rainfall events, such as 06th March 2017, 23rd May 2018, 20 – 22 July 2018, and 04 – 08 August 2018, have been simulated with 1980, 2000, and 2019 land use land cover (LULC) obtained from United States Geological Survey imageries. With these two LULC sensitivities, urban canopy model (UCM) experiments have also been carried out. These experiments suggest that incorporating corrected LULC is necessary for simulating heavy rainfall events using the Weather Research and Forecasting (WRF) model. Urbanization increases the rainfall intensity, and the spatial shift was more pronounced along the peripheral region of the city. The vertically integrated moisture flux analysis suggests that more moisture present over the area received intense rainfall. An increase in urbanization increases the temperature at the lower level of the atmosphere, which increases [planetary boundary layer height, local convection, and rainfall over the region. Contiguous Rain Area method analysis suggests that the 2019 LULC with single layer UCM predicts a better spatial representation of rainfall. This combination works well for all the four cases simulated.
PubDate: 2023-01-27
DOI: 10.1007/s43762-023-00080-3
|
Open Access journal
