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New media platforms offer diverse modes of mediation of every day and tourist places and communities. Spatial social media now augment older forms of mediation, partly by enabling contributions from 'ordinary' users, who create and share spatial discourses. This study examines the discursive construction of peripheral places, produced through user-generated content. Employing qualitative methodology, we sample and analyze 1,053 texts, shared on Google Maps in southern regions of Israel. The key conclusions suggest that compared to traditional media discourses depicting peripheral spaces in Israel, the findings demonstrate a shift from homogeneous depictions to more diverse and multilayered ones. Digital affordances result in more actors and stakeholders partaking in discursive construction, including private and institutional local players, visitors and tourists. Theoretical contributions are offered to the field of digital placemaking, by considering the subjective, evaluative and ideological layers that augment geographical data digital maps provide ('bottom-up' perspective), and to the fields of study of marginalized peripheral and rural communities and tourism crisis in peripheral (post-Coronavirus) locations. [ABSTRACT FROM AUTHOR]

Background: Tele-assisted home-based palliative care (THPC) usually fulfills the desire of terminal patients to pass away at home. The overall costs of such a service deserve evaluation. Objectives: This study aims to determine health care utilization and costs for cancer patients at the end of life, stratified by THPC service. Design: Patients who received THPC were matched 1:1 based on age, gender, year of death, and propensity score with those who did not receive THPC. Setting/Subjects: A total of 773 cancer patients passed away in a regional hospital in Taiwan during the period of 2012–2020, of which 293 received THPC. Measurements: We measured the rates and costs of outpatient clinic visits, emergency department (ED) visits, hospitalizations, and intensive care unit (ICU) admissions during the last week, the last two weeks and the last month before death. In addition, we estimated the driving times and expenses required for transportation from each cancer patient's home to the hospital using Google Maps. National Health Insurance (NHI) reimbursements and out-of-pocket expenses were also calculated. Results: In comparison with patients without THPC, those who received THPC had a 50% lower likelihood of visiting the ED or being hospitalized, a more than 90% reduced chance of ICU admission, but were four times more likely to obtain their medicines from outpatient clinics. THPC patients had similar out-of-pocket expenditures, approximately half of the NHI costs, and lower rates and costs for ambulance transportation to the ED. Conclusions: THPC reduced health care costs for terminal cancer patients in the last week, the last two weeks, and the last month before death, while also increasing the likelihood of patients being able to rest and pass away at home. [ABSTRACT FROM AUTHOR]

This work presents a numerical mesh generation method for 3D urban scenes that could be easily converted into any 3D format, different from most implementations which are limited to specific environments in their applicability. The building models have shaped roofs and faces with static colors, combining the buildings with a ground grid. The building generation uses geographic positions and shape names, which can be extracted from OpenStreetMap. Additional steps, like a computer vision method, can be integrated into the generation optionally to improve the quality of the model, although this is highly time-consuming. Its function is to classify unknown roof shapes from satellite images with adequate resolution. The generation can also use custom geographic information. This aspect was tested using information created by procedural processes. The method was validated by results generated for many realistic scenarios with multiple building entities, comparing the results between using computer vision and not. The generated models were attempted to be rendered under Graphics Library Transmission Format and Unity Engine. In future work, a polygon-covering algorithm needs to be completed to process the building footprints more effectively, and a solution is required for the missing height values in OpenStreetMap. [ABSTRACT FROM AUTHOR]

These days, traveling is made easier by utilizing easily accessible online directions such as Google Maps. Google Maps provides real-time routes by displaying and presenting the closest routes that users can take. However, lately, the routes provided by Google Maps services often get users lost by presenting routes such as forests, narrow roads, and even dead ends. Therefore, this study aims to determine the level of user satisfaction and sentiment into two categories, namely positive and negative, based on reviews on the Google Play Store platform using the Support Vector Machine (SVM) algorithm and topic modeling using Latent Dirichlet Allocation (LDA) to find out the collection of topics that are the main topics of conversation by users regarding Google Maps services. The results of this study show that the SVM algorithm is feasible to use in sentiment analysis classification with an accuracy value of 86%, precision of 93%, recall of 53%, and f1-score of 52%. In addition, topic modeling is applied to generate coherence values for each topic, which shows that the higher the coherence value, the more specific the topic is. The highest coherence value generated in this study was two topic models with a coherence value of 35.15%, but this study took five with a coherence value of 33.39%. The five topic models to be applied in this study are selected because they have a good enough coherence value to identify the main topics and hidden topics in Google Maps user reviews with the Latent Dirichlet Allocation model. The topic model shows five aspects users often discuss: Google Maps route accuracy, system and service errors, navigation application directions, lost time history, and convoluted route provision. [ABSTRACT FROM AUTHOR]

This study aims to analyze the usage patterns of rest areas on highways and toll roads using a data mining approach, utilizing popular time information from Google Maps and web scraping techniques. We collected popular time data for rest areas along highways and expressways using web scraping technology and Google Maps API. The research methodology includes a data collection phase that extracts popular time information from Google Maps using web scraping techniques. The collected data was then analyzed using data mining techniques to identify road user visitation patterns, specifically at rest areas. The analysis included understanding the most popular times visitors visit rest areas and providing deep insights into road user preferences. The findings indicate specific time patterns during which visitors visit rest areas. These findings can have important implications in the planning and managing of road infrastructure, enabling improved services at rest areas according to road users' needs. They can also assist in determining appropriate locations for constructing new rest areas or necessary improvements to existing rest areas. [ABSTRACT FROM AUTHOR]

In a number of cases, for example, when choosing the design of the exit part of a chimney, when taking into account the characteristics of the underlying surface and meteorological characteristics of the area, or as a means of verifying variant calculations in the ANSYS environment, a study of the trajectory of the smoke plume in natural conditions is required. One of the simplest, most convenient and cheapest ways to determine a trajectory is to photograph it and then decipher it. So, for example, a vertical chimney, if the direction of the camera is raised above the horizon, and the chimney is not located in the center of the photo, it looks inclined in the photo. This means that at the top of the image and at the bottom there are different horizontal linear scales of the image. The angle at which the camera was installed can also be calculated from the photograph. In addition, the same vertical height interval occupies a smaller length in the upper part of the pipe and a larger one in the lower part. This means that in the upper part of the image and in the lower part there are different vertical linear scales of the image. It is also necessary to take into account the fact that objects of the same size have different sizes in the picture. The further away an object is located, the less space it takes up in the photo. Thus, in order to calculate the trajectory of a smoke plume from a photograph, we need to know the orientation of the main optical axis during the photograph and the distance to the photograph plane, which we choose perpendicular to the main optical axis and passing through the object, from which we can determine the initial average linear scales, and the direction smoke plume spread.TARGET. Development of a new, simpler method for recalculating the trajectory of a smoke plume from a chimney using available Google maps to calculate the original scales in a photograph.METHODOLOGY Allows you to trace the entire sequence of actions from determining the coordinates of the shooting point to recalculating the coordinates of the selected points of the torch boundaries in the photograph relative to the emission source onto an orthogonal plane.RESULTS. A new method for calculating the trajectory of a smoke plume from a single photograph has been developed, the accuracy of which was tested using known sizes in the original objects, and which turned out to be higher than that previously used for these purposes.

The great importance of the Nile River emerges from the fact that it represents the main water source for Egypt and its being an important water path for sailing or for Nile tourism cruises. Because of climate change and the construction of the Ethiopian Renaissance Dam, Egypt may have to reduce the discharges from the High Aswan Dam (HAD), which may cause a reduction in water depth below a safe level for navigation. Consequently, this study aims to present a quasi-real-time approach to detect the locations of bottlenecks that threat navigation path via mobile Google Maps. Accordingly, the topographic, hydraulic data, the alignment and geometry of the navigational channel, were collected from the Ministry of Water Resources and Irrigation of Egypt. AutoCAD Civil 3d is adopted in this study to determine navigational bottleneck locations of the Nile River's first reach. Two surface models were created: The Riverbed surface and the navigational path surface using a Triangular Irregular Network (TIN). As a result of the created TIN-Surface modelling ; the amount of sediment, that should be dredged, was estimated (at about 2.1 million.m3). In addition, the navigation path bottlenecks map for the studied reach was created based on minimum water levels at Aswan High Dam's winter closure and the navigational path's geometry. In conclusion, this study provides a simple solution for avoiding bottlenecks by presenting a user-friendly web Google Maps of these locations to represent the Nile River's current navigation condition in quasi-real-time and support the decision-maker on river navigation conditions.

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. [ABSTRACT FROM AUTHOR]

While research into tourism and accessibility has significantly advanced over the past decade, the perceived image of Accessible Tourism Destinations (ATDs) has remained largely unexplored. This study addresses this gap by examining the perceived image of Aveiro (Portugal) and A Coruña (Spain) as exemplars of ATDs. In this investigation, we scrutinized 1,051 Online Travellers Reviews (OTRs) posted between 2016 to 2023 in Google Maps. We employed content analysis and text mining techniques using the RapidMiner software. The findings reveal a correlation between accessibility conditions and visitors' satisfaction. Common concerns among travellers include the availability of parking spaces and adaptations for people with disabilities (PwD). Areas identified for improvement encompass information provision and signage. Further investigation is recommended to understand the factors influencing the increase in positive sentiments in Aveiro during the COVID-19 pandemic. This research presents a strategic framework for Destination Management Organisations (DMOs) to enhance the quality of the tourism offer while showcasing the potential of data mining within this field. [ABSTRACT FROM AUTHOR]

Urban parks play a crucial role in supporting physical activities, promoting human well-being, and contributing to overall physical and mental health. However, there is often unequal access to these parks in metropolitan areas, and the quality of parks can vary, especially in middle-sized cities like Oklahoma City (OKC), where this inequality is not extensively studied. Common metrics for assessing public perspective and satisfaction with parks include park usage and sentiments. In our study, we utilized 4,531 Google Map reviews to demonstrate public ratings and sentiments regarding 94 local urban parks in OKC, aiming to identify potential drivers behind these evaluations. Additionally, we explored and compared public opinions and perceptions of parks catering to different socioeconomic levels. Our findings revealed that the diversity and quantity of park amenities and facilities significantly influence subjective ratings and qualitative sentiments derived from Google Maps reviews of local urban parks in OKC. Moreover, we observed that people discussed various topics and aspects related to parks in different income areas. However, there was a consistent focus on children’s playing environments and physical activity/recreation facilities across all discussions, such as playground and splash pads. These findings could be valuable for enhancing the design and amenities of local urban parks, ensuring they better meet the diverse needs of the community. [ABSTRACT FROM AUTHOR]

Background: Birthing people in the United States face numerous challenges when accessing adequate prenatal care (PNC), with transportation being a significant obstacle. Nevertheless, previous studies that relied solely on the distance to the nearest provider cannot differentiate the effects of travel burden on provider selection and care utilization. These may exaggerate the degree of inequality in access and fail to capture perceived travel burden. This study investigated whether travel distances to the initially visited provider, to the predominant PNC provider, and perceived travel burden (measured by the travel disadvantage index (TDI)) are associated with PNC utilization. Methods: A retrospective cohort of people with live births were identified from South Carolina Medicaid claims files in 2015–2018. Travel distances were calculated using Google Maps. The estimated TDI was derived from local pilot survey data. PNC utilization was measured by PNC initiation and frequency. Repeated measure logistic regression test was utilized for categorical variables and one-way repeated measures ANOVA for continuous variables. Unadjusted and adjusted ordinal logistic regressions with repeated measure were utilized to examine the association of travel burdens with PNC usage. Results: For 25,801 pregnancies among those continuously enrolled in Medicaid, birthing people traveled an average of 24.9 and 24.2 miles to their initial and predominant provider, respectively, with an average TDI of -11.4 (SD, 8.5). Of these pregnancies, 60% initiated PNC in the first trimester, with an average of 8 total visits. Compared to the specialties of initial providers, predominant providers were more likely to be OBGYN-related specialists (81.6% vs. 87.9%, p <.001) and midwives (3.5% vs. 4.3%, p <.001). Multiple regression analysis revealed that every doubling of travel distance was associated with less likelihood to initiate timely PNC (OR: 0.95, p <.001) and a lower visit frequency (OR: 0.85, p <.001), and every doubling of TDI was associated with less likelihood to initiate timely PNC (OR: 0.94, p =.04). Conclusions: Findings suggest that the association between travel burden and PNC utilization was statistically significant but of limited practical significance. [ABSTRACT FROM AUTHOR]

The present study aims to explore the service quality determinants in historic centers (HC) by applying a user-generated content analysis with indicators of the TOURQUAL protocol for evaluating the quality of tourist services as the theoretical basis. We analyze 7,849 reviews from 9 HC (5 Brazilians and 4 Portuguese), from January 2019 to September 2020, available on TripAdvisor and Google Maps platforms. The findings demonstrate that the dimensions of technical quality, experience, and environment are the most identified among the reviews. Additionally, we noticed a lack of use of Unesco titles as marketing strategy. The research proposes a model of importance for dimensions of quality of service in historic centers that enables managers to improve the quality of tourist services. [ABSTRACT FROM AUTHOR]

The significant importance of the Nile River emerges not only from the fact that it represents the main water source for Egypt but also from its being an important water path for sailing to transport goods or for Nile tourism cruises. Because of climate change and the construction of the Ethiopian Renaissance Dam, Egypt may have to reduce the discharges from the High Aswan Dam (HAD), which may cause a reduction in water depth below a safe level for navigation. Consequently, this study aims to present a quasi-real-time approach to detect the locations of bottlenecks that threat navigation path via mobile Google Maps. Accordingly, the topographic, hydraulic data, the alignment and geometry of the navigational channel, were collected from the Ministry of Water Resources and Irrigation of Egypt. AutoCAD Civil 3d is adopted in this study to determine navigational bottleneck locations of the Nile River's first reach. Two surface models were created: The Riverbed surface and the navigational path surface using a Triangular Irregular Network (TIN). As a result of the created TIN-Surface modelling via the current study; the amount of sediment, that should be dredged, was estimated (at about 2.1 million.m³). In addition, the navigation path bottlenecks map for the studied reach was created based on minimum water levels at Aswan High Dam's winter closure and the navigational path's geometry. In conclusion, this study provides a simple solution for avoiding bottlenecks by presenting a user-friendly web Google Maps of these locations to represent the Nile River's current navigation condition in quasi-real-time and support the decision-maker on river navigation conditions. [ABSTRACT FROM AUTHOR]

Background and Objectives: Cadastral boundary detection deals with locating the boundary of the ownership and use of land. Recently, there has been high demand for accelerating and improving the automatic detection of cadastral mapping. As this problem is in its starting point, there are few researches using deep learning algorithms. Methods: In this paper, we develop an algorithm with a Mask R-CNN core followed with geometric post-processing methods that improve the quality of the output. Many researches use classification or semantic segmentation but our algorithm employs instance segmentation. Our algorithm includes two parts, each of which consists of a few phases. In the first part, we use Mask R-CNN with the backbone of a pre-trained ResNet-50 on the ImageNet dataset. In the second part, we apply three geometric post-processing methods to the output of the first part to get better overall output. Here, we also use computational geometry to introduce a new method for simplifying lines which we call pocket-based simplification algorithm. Results: We used 3 google map images with sizes 4963 × 2819, 3999 × 3999, and 5520 × 3776 pixels. And divide them to overlapping and non-overlapping 400×400 patches used for training the algorithm. Then we tested it on a google map image from Famenin region in Iran. To evaluate the performance of our algorithm, we use popular metrics Recall, Precision, and F-score. The highest Recall is 95%, which also maintains a high precision of 72%. This results in an F-score of 82%. Conclusion: The idea of semantic segmentation to derive boundary of regions, is new. We used Mask R-CNN as the core of our algorithm, that is known as a very suitable tools for semantic segmentation. Our algorithm performs geometric postprocess improves the f-score by almost 10 percent. The scores for a region in Iran containing many small farms is very good. [ABSTRACT FROM AUTHOR]

Strangely, the palimpsest of human occupation is often far less visible in the city than it is in wilder spaces. Artist, architect, researcher and educator Kirsty Badenoch describes an exploratory expedition to draw out these ghosts within the rugged landscapes of the Scottish Highlands, and a subsequent exhibition of the work through diary entries and photographic forms. The work itself is made through transcriptive collaborations with the volatile climate and flora and fauna of the site, creating a tale of palimpsestuous relationships that dislocate the artistic self as nature leads the dance, or at the very least calls the tune. [ABSTRACT FROM AUTHOR]

The article offers information on the challenges and implications of relying on Google Maps in urban environments, particularly outside Western contexts. Topics include the limitations of Google Maps in accurately reflecting local transit schedules and routes, the impact of Western-centric technological standards on global perceptions of urban efficiency, and the need for urban studies to embrace diverse perspectives to avoid reinforcing North-South academic divides.

Mining communities often rely on tourism as a vehicle for post-mining territorial development. Sometimes, these expectations of the locals are justified by the natural setting and/or the well-preserved industrial heritage; however, these potential tourist destinations are disadvantaged primarily by their image, often associated with decay in the perception of travellers. In this paper, we treat travellers as stakeholders, able to decisively influence the image of a destination by uploading content (photos, reviews and ratings) on Google Maps and TripAdvisor, and we emphasise that user-generated content should be considered when shaping the tourism development strategies. Taking as case studies three former mining regions trying to capitalise on their tourist potential—Jiu Valley and Ștei, in Romania and La Louvière, in Belgium—this article proposes a method for assessing the image of the destination, also aiming to identify those aspects that require improvement.

Urbanization and climate change increasingly challenge urban water management. In this context, the design of stormwater drainage systems, which traditionally relies on historical rainfall records, is being questioned. Although significant efforts have been dedicated to optimizing drainage networks, the upgrading of existing systems remains understudied. This research devised a set of viable stormwater drainage networks, referencing the road network of the Sino-Singapore Tianjin Eco-City (data from Google Maps). On this basis, utilizing design rainfall data (sourced from the local meteorological center), an extensive array of scenario analyses was conducted. The investigation assessed the performance of implementing two redundancy-based interventions—introducing loops and enlarging pipe diameters—as well as the patterns of flood risk response, and by integrating a multi-objective optimization algorithm, this study proposes a framework for the optimization of grey infrastructure upgrades based on component replacement. The findings suggest that a precise deployment strategy for grey infrastructure is essential. The former improves the effective flow distribution of the drainage system, while the latter enhances its flow capacity, making each intervention suitable for drainage systems with a different degree of centralization. Further research shows that an integrated hybrid scheme brings significant flood risk improvement with strong applicability for most urban drainage systems. The upgrade model proposed in this study could be a valuable initiative, offering theoretical insights for the construction and development of resilient cities. [ABSTRACT FROM AUTHOR]

This study explores the public library service evaluation domain through user-generated content on Google Maps, highlighting digital feedback's significant yet underexplored potential in understanding public library patronage across Taiwan's six major cities. Utilizing a mixed-methods research design, this study integrates Google Maps review content analysis with social network analysis to delineate public perceptions and identify areas for service enhancement in public libraries. It innovatively leverages personal experiences extracted from over 60,000 Google Maps reviews to evaluate public library services in cities such as Taipei, New Taipei, Taoyuan, Taichung, Tainan, and Kaohsiung. The research taps into the National Library of Taiwan's National Library Statistics System to provide a robust analysis of library performance and user satisfaction, offering a novel perspective by emphasizing user-centric feedback from Google Maps as a primary data source. This approach provides quantitative data on library usage and geographic distribution and enriches our understanding of the qualitative experiences of library users. In analyzing the keywords from Google Maps reviews of public libraries, we categorize and interpret these under the three core LibQUAL+ dimensions—Affect of Service, Information Control, and Library as Place. The findings expose variances in perceived service quality among the cities, with Kaohsiung and Taichung receiving the highest accolades for service satisfaction. Simultaneously, the study identifies potential areas for improvement, particularly in cities with lower satisfaction ratings like Taipei. This personalized feedback illustrates the intimate relationship between public libraries and their communities, offering invaluable insights for policymakers and library management to enhance service delivery and user experience. [ABSTRACT FROM AUTHOR]

The popularity of working from home (WFH) in the US has surged over the past two decades, with the COVID‐19 pandemic further accelerating this trend. We hypothesize that WFH not only reduces the frequency of physical commutes but also lowers the time cost of commutes due to decreased urban congestion levels; both factors would flatten house‐price gradients. Analyzing big data from Google Maps on travel time in California, we first confirm that COVID‐19, as a WFH‐boosting shock, induced larger decreases in morning travel time in cities with a higher WFH potential. We then empirically validate the effect of WFH on house‐price gradients, channeled through its impact on commuting time; this effect explains 20% of the total WFH‐induced flattening of house‐price gradients during the pandemic in California. [ABSTRACT FROM AUTHOR]

Urban Green Spaces have become essential tools for promoting public health, especially in over-urbanized cities. Following visitors' outdoor recreation experiences, this paper aims to identify visitors' complaints about nation gardens in Istanbul. The study site consisted of 12 nation gardens in Istanbul. Initially, 38,965 Google Maps reviews about the nation gardens through December 2022 were screened. Then, 4143 comments containing the visitors' complaints were identified. After gathering these comments in an Excel spreadsheet, the data was imported to MAXQDA software. Braun and Clarke's six-phased thematic analysis approach was applied to explore codes and themes. Considering the visitors' complaints, 91 codes were generated. Relevant codes are categorized under five themes: outdoor areas/physical characteristics, facilities, visitors' interactions, accessibility/entrance management, and restrictions and security implications. Outdoor areas/physical characteristics, facilities, and visitor interactions are the most common complaints, followed by accessibility and security. Park planners can use visitor feedback to improve existing parks and design new ones, maximizing individual and social benefits, such as health outcomes, and ensuring the effective use of green spaces. [ABSTRACT FROM AUTHOR]

The study grasps the transformation of agency in the context of mundane production and use of mapping apps. It is asked theoretically and empirically, who produces and maintains Google Maps as cartographic infrastructure and how is this kind of agency being reflected and acted upon from a lifeworld perspective. Firstly, findings are compiled from various interdisciplinary studies on digital cartography, platform capitalism and digital infrastructures. Since these studies focus on agency almost exclusively from a structural perspective, a qualitative study is conducted to explore the use of Google Maps in everyday life. 20 interviews with German users show that digitalization and datafication profoundly change the dynamics of how agency is perceived and reflected. It can be understood as a form of extension of agency because it is deeply rooted in the entanglement of Google Maps' infrastructure and city dwellers everyday practices. [ABSTRACT FROM AUTHOR]

Mining communities often rely on tourism as a vehicle for post-mining territorial development. Sometimes, these expectations of the locals are justified by the natural setting and/or the well-preserved industrial heritage; however, these potential tourist destinations are disadvantaged primarily by their image, often associated with decay in the perception of travellers. In this paper, we treat travellers as stakeholders, able to decisively influence the image of a destination by uploading content (photos, reviews and ratings) on Google Maps and TripAdvisor, and we emphasise that user-generated content should be considered when shaping the tourism development strategies. Taking as case studies three former mining regions trying to capitalise on their tourist potential—Jiu Valley and Ștei, in Romania and La Louvière, in Belgium—this article proposes a method for assessing the image of the destination, also aiming to identify those aspects that require improvement. [ABSTRACT FROM AUTHOR]

In the present study, a new eye-tracking dataset (OnMapGaze) and a graph-based metric (GraphGazeD) for modeling visual perception differences are introduced. The dataset includes both experimental and analyzed gaze data collected during the observation of different cartographic backgrounds used in five online map services, including Google Maps, Wikimedia, Bing Maps, ESRI, and OSM, at three different zoom levels (12z, 14z, and 16z). The computation of the new metric is based on the utilization of aggregated gaze behavior data. Our dataset aims to serve as an objective ground truth for feeding artificial intelligence (AI) algorithms and developing computational models for predicting visual behavior during map reading. Both the OnMapGaze dataset and the source code for computing the GraphGazeD metric are freely distributed to the scientific community. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVDs) represent the leading cause of death globally. Romania recorded the highest mortality rate due to CVDs in the EU in 2022, with 162,984 deaths, while the number of registered patients with CVDs surpassed 4 million. This study aims to measure the population's potential spatial accessibility to cardiovascular hospitals in Romania, as timely access to such healthcare facilities is crucial to minimise avoidable mortality due to CVDs. Although distance is an essential parameter of spatial accessibility, time-based analysis is more reflective of real-world scenarios due to the unpredictability of travel. The potential spatial accessibility was measured using the Application Program Interface (API) offered through the Google Maps platform and a personal car as the transportation mode. The country's cardiovascular hospital network comprises 161 units, of which 84 can provide complex care. Because all of them are located in urban areas, three different time slots were considered to distinguish between high and low traffic congestion situations. We created hierarchies of ten-minute and five km intervals for travel time and distance, respectively, to emphasize the population percentages with better or low potential spatial accessibility. Results showed that only 15% of the population can reach the nearest cardiovascular hospital in less than 20 min, and 23% must travel for over 60 min, while 45.7% live farther than 20 km from a cardiovascular hospital. Inhabitants living in remote areas, especially rural ones, are the most vulnerable, having to travel for the longest time and distance. Actions like improving the existing transport infrastructure and upgrading healthcare facilities and equipment are needed to ensure better medical care and an adequate response to population needs. This study can support local authorities in optimising spatial accessibility to cardiovascular care by identifying the most burdened hospitals in the context of low medical specialised staff and large catchment areas. [ABSTRACT FROM AUTHOR]

The impact of partial and full COVID lockdowns in 2020 on vehicle miles traveled (VMT) in Kuwait was estimated using data extracted from the Directions API of Google Maps and a Python script running as a cronjob. This approach was validated by comparing the predictions based on the app to measuring traffic flows for 1 week across four road segments considered in this study. VMT during lockdown periods were compared to VMT for the same calendar weeks before the pandemic. NOx emissions were estimated based on VMT and were used to simulate the spatial patterns of NOx concentrations using an air quality model (AERMOD). Compared to pre-pandemic periods, VMT was reduced by up to 25.5% and 42.6% during the 2-week partial and full lockdown episodes, respectively. The largest reduction in the traffic flow rate occurred during the middle of these 2-week periods, when the traffic flow rate decreased by 35% and 49% during the partial and full lockdown periods, respectively. The AERMOD simulation results predicted a reduction in the average maximum concentration of emissions directly related to VMT across the region by up to 38%, with the maximum concentration shifting to less populous residential areas as a result of the lockdown. [ABSTRACT FROM AUTHOR]

Landscapes varied throughout human history, some offering more fitness benefits than others. Therefore, natural selection has likely designed in us landscape preferences that motivated us to seek some environments over others (Orians, 1980). These evolved landscape preferences may influence several aspects of modern-day society, including how we manipulate our urban environment. In Study 1, we conducted a content analysis of the naming conventions of apartment buildings and residential neighborhoods. We hypothesized that there would be more nature words (e.g., valley, river, arbor) in apartment and neighborhood names than nonnature words (e.g., 4th Street; Renaissance, Washington). Names of 2,980 apartment buildings and neighborhoods were collected with a program that uses Google Maps from each of the 48 contiguous United States. Results strongly supported our hypothesis; There were 52% more nature words than nonnature words in the names of apartment buildings and residential neighborhoods. Study 2 used a mixed-subjects experimental design to determine if apartments and neighborhoods with nature names were perceived as more valuable than those without nature names. Participants rated five photographs of apartments and five photographs of neighborhoods photoshopped to display nature or nonnature names for their price (in monthly rent or home's mortgage) and perceived rate of vacancy. Apartments and neighborhoods with manipulated nature names were rated as statistically more expensive. There were no statistical differences between perceived vacancy rates in apartments or neighborhoods named after nature compared with those not named after nature. The current preregistered studies are the first to document that our evolved landscape preferences may affect how developers name our homesteads, further exploiting biophilia. [ABSTRACT FROM AUTHOR]

Path redundancy is essential for safety and reliability in many real-world routing problems, such as the design of networks for power transmission, transportation, etc. These problems are typically posed to find the shortest path on a weighted graph. For the shortest path with path redundancy, particularly in the Disjoint Shortest 2-Path (DS2P) problem, two disjoint paths are desired such that the combined weight of the two paths is minimized while a minimum distance path separation is maintained. The conventional formulation of the above requires a large-scale mixed-integer programming (MIP) model. However, this approach is practically intractable due to the model's complexity and extremely long run-time. We demonstrate why DS2P is NP-complete and propose an efficient heuristic to find an approximate solution to the problem in a much shorter time frame. We demonstrate the approach on a realistic dataset for power transmission routing, integrating the computational methodology with a visualization interface using Google Maps. The resulting prototype software is freely available through GitHub and can be deployed on a cloud platform, such as Amazon AWS. [ABSTRACT FROM AUTHOR]

Sentiment analysis, the meta field of Natural Language Processing (NLP), attempts to analyze and identify the sentiments in the opinionated text data. People share their judgments, reactions, and feedback on the internet using various languages. Urdu is one of them, and it is frequently used worldwide. Urdu-speaking people prefer to communicate on social media in Roman Urdu (RU), an English scripting style with the Urdu language dialect. Researchers have developed versatile lexical resources for features-rich comprehensive languages, but limited linguistic resources are available to facilitate the sentiment classification of Roman Urdu. This effort encompasses extracting subjective expressions in Roman Urdu and determining the implied opinionated text polarity. The primary sources of the dataset are Daraz (an e-commerce platform), Google Maps, and the manual effort. The contributions of this study include a Bilingual Roman Urdu Language Detector (BRULD) and a Roman Urdu Spelling Checker (RUSC). These integrated modules accept the user input, detect the text language, correct the spellings, categorize the sentiments, and return the input sentence's orientation with a sentiment intensity score. The developed system gains strength with each input experience gradually. The results show that the language detector gives an accuracy of 97.1% on a close domain dataset, with an overall sentiment classification accuracy of 94.3%. [ABSTRACT FROM AUTHOR]

Government of Jakarta has struggled to mitigate the traffic congestion by implementing the odd-even policy. This policy has restricted some roads for vehicles to pass in Jakarta in a certain time and this policy needs to be studied further for its impact on distance and time, especially for private companies. The research was conducted in private sectors which its warehouse located in Tangerang and its customers spread in Jakarta, Bogor, Depok, Tangerang and Bekasi. The study focuses on 49 routes that every route had some customers’ location. The sequence for every route was determined if had a shortest distance and/or fastest time with the help of optimoroute software. Then, every route was processed by Google Maps both in normal condition and odd-even policy’s condition in order to get the distance and travel time. The results indicated that around 44.89% of routes have further distance and 16.55% have longer time, these results mean costlier for private companies since odd-even policy has been implemented. The study also compared two alternatives between maintaining current condition and switching to rent a vehicle with third party logistics, the result indicated that maintaining current condition alternative was still beneficial than switching to rent a vehicle alternative.

This study identifies the destination attributes of sustainable urban waterfronts that are frequently mentioned in tourists' online reviews. We analyzed the influence of these attributes on tourists' ratings based on stimuli–organism–response theory, and the associations between these destination attributes. The online reviews (both text reviews and star ratings) from TripAdvisor and Google Maps of the sustainable waterfront destinations of the Liuchuan and Luchuan rivers in Taichung city (Taiwan) were collected and analyzed through text and data mining. Destination attributes were grouped into two types: sustainable landscapes (aesthetics, water resource rehabilitation, sustainable lighting, emotional experiences, and low-impact development waterfronts) and sustainable recreational spaces (leisure activities, festivals, inclusive destinations, photography, and tourist experiences). Two destination attributes common to-- both types were identified: nightscapes and waterfronts. These attributes predicted tourists' ratings through support vector machine analysis. Sensitivity analysis revealed that sustainable landscape-type attributes had a greater impact on tourists' ratings than the sustainable recreational space type. In addition, three important association rules between twelve attributes were identified and these helped provide information pattern combination attributes from tourists' comments with support and confidence for the destination attributes. These findings will contribute to urban planning and design in relation to sustainable waterfront destinations. They highlight the need for planners to consider both tourists' landscapes and recreational needs in order to achieve economic and ecological sustainability. [ABSTRACT FROM AUTHOR]

Accidents can have a significant impact on road safety and the efficiency of transport. With the ever-increasing number of vehicles on the road, it is crucial to quickly detect and respond to such events to decrease the impact. The current paper presents a road accident and incident detection solution utilizing traffic API monitoring and continuously analyzing real-time traffic data from various sources like GPS-enabled devices. Consequently, the proposed approach can identify abnormal patterns in the movement of vehicles, such as unexpected road closures or an accident. It numerically analyzes the traffic pattern and identifies incidents and accidents using live traffic data collected from the map service. When the approach identifies any abnormality, it can immediately let the user know, allowing them to act accordingly. It can facilitate authorities in identifying accident-prone areas and deploy safety measures. This can help law enforcement agencies to identify drivers at risk of causing accidents and implement education and enforcement programs targeted towards them. Hence, the proposed approach can aid in reducing the negative impact of traffic incidents, and the public can respond to these incidents more preparedly. [ABSTRACT FROM AUTHOR]

Airport service quality ratings found on social media such as Airline Quality and Google Maps offer invaluable insights for airport management to improve their quality of services. However, there is currently a lack of research analysing these reviews by airport services using sentimental analysis approaches. This research applies multiclass models based on Aspect-Based Sentimental Analysis to conduct a comprehensive analysis of travellers' reviews, in which the major airport services are tagged by positive, negative, and non-existent sentiments. Seven airport services commonly utilised in previous studies are also introduced. Subsequently, various Deep Learning architectures and Machine Learning classification algorithms are developed, tested, and compared using data collected from Twitter, Google Maps, and Airline Quality, encompassing travellers' feedback on airport service quality. The results show that the traditional Machine Learning algorithms such as the Random Forest algorithm outperform Deep Learning models in the multiclass prediction of airport service quality using travellers' feedback. The findings of this study offer concrete justifications for utilising multiclass Machine Learning models to understand the travellers' sentiments and therefore identify airport services required for improvement. [ABSTRACT FROM AUTHOR]

A geographic information system (GIS) is a very effective management and analysis tool. Geographic locations rely on data. The use of artificial neural networks (ANNs) for the interpretation of natural resource data has been shown to be beneficial. Back-propagation neural networks are one of the most widespread and prevalent designs. The combination of geographic information systems with artificial neural networks provides a method for decreasing the cost of landscape change studies by shortening the time required to evaluate data. Numerous designs and kinds of ANNs have been created; the majority of them are PC-based service domains. Using the ArcGIS Network Analyst add -on, you can locate service regions around any network site. A network service area is a region that comprises all accessible roadways (that is, routes that are within defined impedance) (that is, streets that are within specified impedance). In contrast to the Google Maps application, the 5-minute service area for a site on a network comprises all streets that can be accessed within five minutes. [ABSTRACT FROM AUTHOR]

Background: In the context of humanitarian logistics, efficiently evacuating people from disaster-stricken areas is a complex challenge. This study focuses on the Tula River region in Hidalgo, Mexico, exploring the evacuation and support of individuals in temporary shelters. Despite the fact that the topic has been addressed in the literature, it is necessary to have quick response methods that can be used by decision-makers to adapt and utilize existing spaces as temporary shelters, in addition to knowing how to evacuate people. Methods: Addressing this void, a methodology to minimize evacuation and aid distribution costs is introduced. Leveraging existing algorithms, particularly Integer Linear Programming, the model determines shelter activation and utilizes the Vehicle Routing Problem to assess aid delivery strategies. Results: The research identifies optimal evacuation routes from 13 affected areas to 34 shelters and analyzes aid distribution costs under various demand scenarios: original, increased, and decreased by 10%, based on the number of transport units allocated and Google Maps distances. It also evaluates the costs associated with humanitarian aid distribution under varying collection strategies, involving state and municipal governments. Conclusion: This approach provides a decision-making foundation and can be adapted for similar analyses in other communities during extreme events. [ABSTRACT FROM AUTHOR]

Research on road safety is continuously developing with the applications of sensors and technologies. Another area that draws the attention of the developers for road safety are in the road accident response. As the number of riders and cyclists increase, so do the accidents particularly at night where visibility is limited. This study presents a method of integration of inflatable safety vests for riders with light emitting diode LED signaling strips embedded in its front and back, and sensors to send the location of the rider when an accident hap-pens. The LED strips are controlled using a wireless remote switch to make the rider more visible other than that embedded in the motorcycle or bicycle. The airbag will activate once an accident occurs and or the rider is detached from the motorcycle. A force-sensitive resistor (FSR) is used as a triggering device attached to the vest when an accident happens where a global positioning system (GPS) module will send the location and a map to a specific mobile number for response. Three trials were conducted to test the functionality of the LED lights for signaling. The device functioned well, that both the left, right, and standby mode were activated. The functionality of the location tracker is also tested in three different locations. The FSR was triggered and it gave the exact location by sending the coordinates and a link to view it on google maps with an average transmit and receive time of 3 seconds. It is recommended that the prototype be developed using light-weight materials and batteries. [ABSTRACT FROM AUTHOR]

Ecotourism, a dynamic force in global tourism, holds promise for conserving the environment while ensuring benefits for local economies. In this study, we developed an ecotourism distribution map of Indonesia. We utilized location-based social networks (LSBNs) data derived from Google Maps API to map 172 ecotourism sites in Indonesia. Furthermore, we investigated the distribution patterns of ecotourism within Indonesia's protected landscapes and ecoregions. The factors that influenced ecotourism distribution in the region were analyzed using the MaxEnt model (because of its application for presence-only data). The key findings revealed that ecotourism sites are predominantly distributed across national parks and protected forest areas, and generally consist of mountainous and hilly terrain according to the ecoregion types. The MaxEnt model results indicated that population density was the most influential factor in ecotourism distribution. The significance of our study lies in its methodologies and results, which offered novel approaches to nationwide mapping and addressed the lack of an ecotourism site map of Indonesia. Notably, the proposed model can be customized for other regions with limited ecotourism data; thus, our study can serve as a foundation for future interdisciplinary studies on ecotourism, sustainability, and landscape planning. [ABSTRACT FROM AUTHOR]

Accurate estimation of ambulance transport time from the scene of incident to arrival at the emergency department (ED) is important for effective resource management and emergency care system planning. Further, differences in transport times between different urgency levels highlight the benefits of ambulance transports with highest urgency level in a setting where ambulances are allowed to not follow standard traffic rules. The objective of the study is to compare ambulance urgency level on the differences in estimates of ambulance transport times generated by Google Maps and the observed transport times in a prehospital setting where emergency vehicles have their own traffic laws. The study was designed as a natural experiment and register study. Ambulance transports dispatched with different levels of urgency (Level A and B) were included in the Central Denmark Region (a mixed urban and rural area) from March 10 to June 11, 2021. Ambulance transports for highest urgency level were compared to lowest urgency level with Google Maps estimated transport times as reference. We analyzed 1981 highest urgency level and 8.958 lowest urgency level ambulance transports. Google Maps significantly overestimated the duration of transports operating at highest level of urgency (Level A) by 1.9 min/10 km (95% CI 1.8; 2.0) in average and 4.8 min/10 km (95% CI 3.9; 5.6) for the first driven 10 km. Contrary, Google Maps significantly underestimated the duration of transports operating at lowest level of urgency (Level B) by −1.8 min/10 km (95% CI −2.1; −1.5) in average and −4.4 min/10 km (95% CI −5.4; −3.5) for the first driven 10 km. Google Maps systematically overestimates transport times of ambulance transports driven with Level A, the highest level of urgency in a setting where ambulances are allowed to not follow standard traffic rules. The results highlight the benefit of using urgency Level A and provide valuable information for emergency care management. [ABSTRACT FROM AUTHOR]

This study introduces a data-driven and machine-learning approach to design a personalized tourist recommendation system for Nepal. It examines key tourist attributes, such as demographics, behaviors, preferences, and satisfaction, to develop four sub-models for data collection and machine learning. A structured survey is conducted with 2400 international and domestic tourists, featuring 28 major questions and 125 variables. The data are preprocessed, and significant features are extracted to enhance the accuracy and efficiency of the machine-learning models. These models are evaluated using metrics such as accuracy, precision, recall, F-score, ROC, and lift curves. A comprehensive database for Pokhara City, Nepal, is developed from various sources that includes attributes such as location, cost, popularity, rating, ranking, and trend. The machine-learning models provide intermediate categorical recommendations, which are further mapped using a personalized recommender algorithm. This algorithm makes decisions based on weights assigned to each decision attribute to make the final recommendations. The system's performance is compared with other popular recommender systems implemented by TripAdvisor, Google Maps, the Nepal tourism website, and others. It is found that the proposed system surpasses existing ones, offering more accurate and optimized recommendations to visitors in Pokhara. This study is a pioneering one and holds significant implications for the tourism industry and the governing sector of Nepal in enhancing the overall tourism business. [ABSTRACT FROM AUTHOR]

We have developed a robust, novel, and cost-effective method for determining the geolocation of vehicles observed in drone camera footage. Previous studies in this area have relied on platform GPS and camera geometry to estimate the position of objects in drone footage, which we will refer to as object-to-drone location (ODL). The performance of these techniques is degraded with decreasing GPS measurement accuracy and camera orientation problems. Our method overcomes these shortcomings and reliably geolocates objects on the ground. We refer to our approach as object-to-map localization (OML). The proposed technique determines a transformation between drone camera footage and georectified aerial images, for example, from Google Maps. This transformation is then used to calculate the positions of objects captured in the drone camera footage. We provide an ablation study of our method's configuration parameter, which are: feature extraction methods, key point filtering schemes, and types of transformations. We also conduct experiments with a simulated faulty GPS to demonstrate our method's robustness to poor estimation of the drone's position. Our approach requires only a drone with a camera and a low-accuracy estimate of its geoposition, we do not rely on markers or ground control points. As a result, our method can determine the geolocation of vehicles on the ground in an easy-to-set up and costeffective manner, making object geolocalization more accessible to users by decreasing the hardware and software requirements. Our GitHub with code can be found at https://github.com/OSUPCVLab/VehicleGeopositioning [ABSTRACT FROM AUTHOR]