Google Maps Application Programming Interface Research Articles

Factors Influencing Compliance to Follow-up Visits in Orthopaedic Surgery.

Orthopaedic procedures require postoperative follow-up to maximize recovery. Missed appointments and noncompliance can result in complications and increased healthcare costs. This study investigates the relationship between patient postoperative visit attendance and the distance traveled to receive care. A retrospective review of all surgeries performed by a single orthopaedic surgeon in 2019 at level 1 trauma center in a midsized city serving a largely rural population was completed. We excluded patients who underwent another subsequent procedure. Distance to care and time traveled were determined by the patient's address and the clinic address using Google Maps Application Programming Interface. Other variables that may affect attendance at follow-up visits were also collected. Univariate and multivariate logistic regression was done with purposeful selection. We identified 518 patients of whom 32 (6%) did not attend their first scheduled follow-up appointment. An additional 47 (10%) did not attend their second follow-up. In total, 79 patients (15%) did not attend one of their appointments. Younger age, male sex, Black or African American race, self-pay, Medicaid insurance, accident insurance, and increased distance were individual predictors of missing an appointment. In the final multivariate logistic regression model, male sex (OR 1.74), Black or African American race (OR 2.78), self-pay (OR 3.12), Medicaid (OR 3.05), and traveling more than 70 miles to clinic (OR 2.02) markedly predicted missing an appointment, while workers' compensation (OR 0.23) predicted attendance. Several nonmodifiable patient factors predict patient noncompliance in attending orthopaedic postoperative visits. When patients are considered at high risk of being lost to follow-up, there may be an opportunity to implement interventions to improve follow-up rate and patient outcomes, minimize patient costs, and maximize profitability for the hospital.

ISpot: An intelligent real-time pothole spot identification model using a CNN algorithm

Maintaining roads is a very intricate and significant global concern. Detecting road abnormalities, including potholes, is crucial in road monitoring and management. Identifying potholes is essential to minimize road accidents and car damage and improve travel comfort. Authorities have long seen road maintenance as a significant concern. However, the absence of accurate identification and connecting of road potholes exacerbates the problem. An end-to-end system named Intelligent Spotting (iSpot) of Pathole has been developed to address this issue by providing real-time identification, tracking, and geographical mapping of potholes around the city. A Convolutional Neural Network (CNN) framework is suggested and assessed using a real-world dataset for detecting potholes. Real-time maps displaying pothole locations are created using the Google Maps Application Programming Interface (API). Both pothole identification and mapping are combined into an Android application to offer a comprehensive service via this technology. The suggested model outperforms the baseline techniques regarding accuracy, precision, and F score.

Geographical challenges and inequity of healthcare access for high-risk paediatric heart disease.

Geographical context is an important consideration for health system design to promote equality in access to care for patients with childhood heart disease (CHD), particularly those living in regional, rural, and remote areas. To help inform future policy and practice recommendations, this study aimed to (i) describe the geographic distribution of high-risk CHD patients accessing an Australian state-wide specialist service and (ii) estimate travel time for accessing healthcare via general practitioners (primary), nearest paediatric centre (secondary) and specialist paediatric cardiac centre (tertiary). Participants included a cohort of children (0-18year) who accessed state-wide specialist CHD services over a 3-year period (2019-2021) in Queensland, Australia. Locations for patient residence, general practitioner, closest paediatric centre and tertiary cardiac centre were mapped using geographical information system (GIS) software (ArcGIS Online). Travel distance and times were estimated using a Google Maps Application Programming Interface (API). 1019 patients (median age 3.8 years) had cardiac intervention and were included in the sample. Of this cohort, 30.2% lived outside the heavily urbanised South East Queensland (SEQ) area where the tertiary centre is located. These patients travel substantially further and longer to access tertiary level care (but not secondary or primary level care) compared to those in SEQ. Median distance for patients residing outside SEQ to access tertiary care was 953km with a travel time of 10h 43min. This compares to 5.5km to the general practitioner and 20.6km to a paediatric service (8.9 and 54min respectively). This geographical mapping of CHD services has demonstrated a key challenge inherent in providing specialist cardiac care to children in a large state-based healthcare system. A significant proportion of high-risk patients live large distances from tertiary level care. The greater accessibility of primary care services highlights the importance of supporting primary care physicians outside metropolitan areas to acquire or build the ability and capacity to care for children with CHD. Strengthening local primary and secondary services not only has the potential to improve the outcomes of high-risk patients, but also to reduce costs and burden associated with potentially avoidable travel from regional, rural, or remote areas to access specialist CHD services.

Rate of Emergency Lower Extremity Amputations in the United States Among Medicare Beneficiaries.

To determine the rate of emergency versus elective lower extremity amputations in the United States. Lower extremity amputation is a common endpoint for patients with poorly controlled diabetes and multilevel peripheral vascular disease. Although the procedure is ideally performed electively, patients with limited access may present later and require an emergency operation. To what extent rates of emergency amputation for lower extremities vary across the United States is unknown. Evaluation of Medicare beneficiaries who underwent lower extremity amputation between 2015 and 2020. The rate was determined for each zip code and placed into rank order from lowest to highest rate. We merged each beneficiary's place of residence and location of care with the American Hospital Association Annual Survey using Google Maps Application Programming Interface to determine the travel distance for patients to undergo their procedure. Of 233,084 patients, 66.3% (154,597) were men, 69.8% (162,786) were White. The average age (SD) was 74 years (8). There was wide variation in rates of emergency lower extremity amputation. The lowest quintile of zip codes demonstrated an emergency amputation rate of 3.7%, whereas the highest quintile demonstrated 90%. The median travel distance in the lowest emergency surgery rate quintile was 34.6 miles compared with 10.5 miles in the highest quintile of emergency surgery ( P < 0.001). There is wide variation in the rate of emergency lower extremity amputations among Medicare beneficiaries, suggesting variable access to essential vascular care. Travel distance and rate of amputation have an inverse relationship, suggesting that barriers other than travel distance are playing a role.

Online Destinations Map using Google Maps API Based on the Private Database

Online mapping applications have undergone a radical change as a result of the widespread use of the Internet,and an interested in the developing of the online mapping services due to the use of the Google Maps Application Programming Interface (API), Yahoo Maps API, and ESRI ArcGIS API, as well as the support for maps by operating systems on tablets and smartphones. One of the most helpful technologies for identifying locations and providing information about roads is online mapping. Although these applications are simple to use, they have a limited amount of the data that may be inaccurate, and the mapping technology does not have sophisticated or detailed characteristics of the required places. As a result, developers and programmers use maps as a platform to create features that are tailored to the needs of the user by using various programming languages. The goal of this paper is to develop the Google API by connecting it to a local, private database that houses data that is regularly gathered and developed. It also aims to create opportunities that make it easier for clients to access information stored on particular servers that can provide a variety of locations and details in order to meet their needs.

Implementation of Geographic Information System for Tourist Locations and Lodging Services in Lhokseumawe City Based on Android

The advancement of information technology is now increasingly widespread and very rapidly growing so that it can help people to enjoy the various conveniences that have been produced by this technology. One aspect of technology currently developing rapidly is mobile technology on smartphone devices (smartphones). This android-based geographic information system application is one of the results of these technological developments, which have the benefits and uses that are needed in this day and age. So with the development of the world of technology, people prefer the way of convenience and efficiency, for example, by utilizing the sophistication of smartphones. Implementing the Geographic Information System (GIS) for tourism and lodging services in Lhokseumawe aims to build tourism applications and lodging services in Lhokseumawe based on Android and implement Google services. Maps Application Programming Interface (API) to facilitate tourists in obtaining information on mapping the location of tourist attractions and lodging, routes, and tourist support facilities in Lhokseumawe City. The method used is a waterfall process model. Implementing the Lhokseumawe Tourism Application and lodging services using Javascript programming with Android Studio, MySQL database, and maps sourced from the Google Maps API. The result is an Android-based Lhokseumawe City Tourism and lodging service application that helps make it easier for tourists to obtain information about tourist attractions, lodging, and routes from their current location to the desired tourist and lodging locations in Lhokseumawe City with the help of the Global Positioning System (GPS). Tourist information and lodging services are entirely related only to data entered into the database server. The results of this application display a list and location of tourist and lodging services as well as the shortest route to the nearest tourist or inn to the user's location point. It is also hoped that this system can be developed to produce a better system than the previous one.

SOS Application Under Android: Help Pro

Information and Communication Technologies are regularly used in medical services and associations around the world. An increasing number of devices running Android as Smartphones, smart TV and tablets, are comprehensively utilized for numerous purposes. For these devices, there are distinctive types of health and medical applications that provide an easy access to patients and their caretakers to save time [1]. In this work, we develop an Android application named Help Pro with an important goal: save lives by a single tap. In case of emergency, Help Pro users could have an ambulance available within 100 m of the user’s real-time location. Through the Dijkstra algorithm, this application employs Google Maps API (Application Programming Interface) to trace the nearest way in order to an available ambulance arrives on time. Consequently, not only time is efficiently saved but also precious lives.

Mobile Application for Data Acquisition in Integrated Forest and Land Fires Patrols

Hotspot is one of the indicators of forest and land fires which is usually checked in the field through an integrated patrol program by a team formed by the Ministry of Environment and Forestry (KLHK). The findings from these patrol activities are usually reported through texts posted in the chat group media using the WhatsApp application. This method is, however, considered ineffective in presenting information queries from the patrol data. Therefore, this research aims to develop a mobile application to record data retrieved during forest and land fire patrols in the Sumatra region. The prototyping system development method was applied and this includes analyzing the needs of the user, designing and modeling the prototype, coding the system, testing the system, and system usage. Moreover, the Ionic 3 framework was employed in the system development through the application of the API from the back-end module. The application was integrated into the Google Maps Application Programming Interface (API) and the system was tested using the Blackbox method to show the features in the system function effectively.

PotSpot: Participatory sensing based monitoring system for pothole detection using deep learning

Proper maintenance of roads is an extremely complex task and also an important issue all over the world. One of the most critical road monitoring and maintenance activities is the detection of road anomalies such as potholes. Identification of potholes is necessary to avoid road accidents, prevent damage of vehicles, enhance travelling comforts, etc. Although maintenance of roads is considered to be a serious issue by the authorities over the years, lack of proper detection and mapping of road potholes makes the issue more severe. To overcome this problem, an end-to-end system called PotSpot is built for real-time detection, monitoring, and spatial mapping of potholes across the city A Convolutional Neural Network (CNN) model is proposed and evaluated on real-world dataset for pothole detection. Additionally, real-time pothole-marked maps are generated with the help of Google Maps API (Application Programming Interface). To provide an end-to-end service through this system, both the pothole detection and pothole mapping are integrated through an android application. The proposed model is also compared with six baselines namely Artificial Neural Network (ANN), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and three pre-trained CNN models InceptionV3, VGG19 and VGG16 in terms of performance metrics to verify its effectiveness. The proposed model achieves better accuracy (≈ 97.6 %) as compared to the above-mentioned baseline methods. It is also observed that the Area Under the Curve (AUC) value for the proposed pothole detection model (AUC= 0.97) is higher than the baseline methods.

Risk assessment of horizontal curves using reliability analysis based on Google traffic data

The risk associated with horizontal curves makes them one of the most critical elements in the transportation network compared with other roadway elements. In this regard, the current geometric design practices of horizontal alignment need to be reviewed due to ignoring the uncertainty of design inputs resulting from drivers’ behavior. Besides, numerous studies utilized the regression speed prediction models developed from several studies in different countries to estimate the operating speed on horizontal curves. However, there are differences in road characteristics, environmental conditions, and drivers’ behavior that differ from one country to another. Thus, this paper presents a new technique based on Google Maps Application Programming Interface (GMAPI) to estimate reliable operating speeds and validate the regression models. It also introduces the reliability analysis approach to overcome the shortcomings of current geometric design practices and evaluate the risk levels on different horizontal curves. Reliability analysis is performed on five horizontal curves located along Gamassa-Al Mansoura road, in Dakahlia, Egypt. The results confirm that the GMAPI method is more accurate than the regression models. It also proves that the regression models are not recommended for all countries. Moreover, this study provides the designers with risk level charts for the design of horizontal curves based on the reliability approach. These charts could be very useful for highway designers to easily predict the safety margin for different design alternatives.

A COMPARISON OF RAINFALL ESTIMATION USING HIMAWARI-8 SATELLITE DATA IN DIFFERENT INDONESIAN TOPOGRAPHIES

The Himawari-8 satellite can be used to derive precipitation data for rainfall estimation. This study aims to test several methods for suchestimation employing the Himawari-8 satellite. The methods are compared in three regions with different topographies, namely Bukittinggi, Pontianak and Ambon. The rainfall estimation methods that are tested are auto estimator, IMSRA, non-linear relation and non-linear inversion approaches. Based on the determination of the statistical verification(RMSE, standard deviation and correlation coefficient) of the amount of rainfall, the best method in Bukittinggi and Pontianak was shown to be IMSRA, while for the Ambon region was the non-linear relations. The best methods from each research area were mapped using the Google Maps Application Programming Interface (API).

Deep Learning Model for House Price Prediction Using Heterogeneous Data Analysis Along With Joint Self-Attention Mechanism

House price prediction is a popular topic, and research teams are increasingly performing related studies by using deep learning or machine learning models. However, because some studies have not considered comprehensive information that affects house prices, prediction results are not always sufficiently precise. Therefore, we propose an end to end joint self-attention model for house prediction. In this model, we import data on public facilities such as parks, schools, and mass rapid transit stations to represent the availability of amenities, and we use satellite maps to analyze the environment surrounding houses. We adopt attention mechanisms, which are widely used in image, speech, and translation tasks, to identify crucial features that are considered by prospective house buyers. The model can automatically assign weights when given transaction data. Our proposed model differs from self-attention models because it considers the interaction between two different features to learn the complicated relationship between features in order to increase prediction precision. We conduct experiments to demonstrate the performance of the model. Experimental data include actual selling prices in real estate transaction data for the period from 2017 to 2018, public facility data acquired from the Taipei and New Taipei governments, and satellite maps crawled using the Google Maps application programming interface. We utilize these datasets to train our proposed and compare its performance with that of other machine learning-based models such as Extreme Gradient Boosting and Light Gradient Boosted Machine, deep learning, and several attention models. The experimental results indicate that the proposed model achieves a low prediction error and outperforms the other models. To the best of our knowledge, we are the first research to incorporate attention mechanism and STN network to conduct house price prediction.

Spatial disparity of income-weighted accessibility in Brazilian Cities: Application of a Google Maps API

Equity in public transit ridership has attracted the attention of planning authorities as a mechanism to tackle social exclusion. The association of accessibility indexing with different income groups is fundamental to analyses of socio-spatial inequalities and identifying gaps in public transit services. However, few studies have addressed accessibility inequalities in medium-sized cities of the global South. This paper aims to identify spatial gaps in public transit service in seven medium-sized Brazilian cities by analyzing the relative accessibility of public transit and private automobiles for travel to central business districts (CBDs), which are primary employment and service centers. Demographic and socioeconomic data on the seven cities were extracted from the country's 2010 population census. To measure accessibility to CBDs, a Google Maps application programming interface was used to produce realistic estimates of travel times for public transit and private automobiles over different time periods. This method is more accurate than traditional accessibility calculation methods and provides real-time information on traffic conditions, such as speed limits, traffic jams, and waiting times. The study found significant intra-regional differences in accessibility to CBDs by public transit and private automobiles, providing a scientific basis to optimize the socio-spatial distribution of public transit services in seven cities in five different regions of Brazil.

Patterns of Care and Clinical Outcomes in Patients with Cerebral Sinus Venous Thrombosis

Objectives: To explore the association between rurality, transfer patterns and level of care with clinical outcomes of CVST patients in a rural Midwestern state. Materials and Methods: CVST patients admitted to the hospitals between 2005 and 2014 were identified by inpatient diagnosis codes from statewide administrative claims dataset. Records were linked across interhospital transfers using probabilistic linkage. Rurality was defined by Rural-Urban Commuting Areas using the 2-category approximation. Driving distances were estimated using GoogleMaps Application Programming Interface. Hospital stroke certification was defined by the Joint Commission. Severity of CVST was estimated by cost of care corrected for inflation and cost-to-charge ratios. Outcome was discharge disposition and total length of stay (LOS). Wilcoxon rank-sum, Chi-square, Fisher's exact tests and linear and logistic regressions were used. Results: 168 CVST patients were identified (79.8% female; median age = 32, IQR = 24.0–45.5). Median LOS was four days (IQR = 2–7) and patients traveled a median of 8.1 miles (IQR = 2.5–28.5) to the first hospital; 42% of patients were transferred to a second hospital, 5% to a third. More than half (58.3%) bypassed the nearest hospital. 86% visit a primary or comprehensive stroke center (CSC) during their acute care. Rurality was not significantly associated with LOS or discharge disposition after adjusting for age, sex and cost of care. Patients in CSC demonstrated greater likelihood of being discharged home compared to at a primary stroke center after adjusting for age and disease severity (p = 0.008). Conclusions: While rurality was not significantly associated with LOS or disposition outcome, care at a CSC increases likelihood of being discharge home.

Empowering Denver Public Schools to Optimize School Bus Operations

Denver Public Schools (DPS) serves roughly 90,000 K–12 students using a mixed bus fleet. Developing and reviewing bus-route assignments manually has been challenging and time consuming for DPS. During 2017–2018, DPS analysts reviewed and adjusted over 700 routes assigned to approximately 200 buses, considering time and capacity feasibility. We developed a decision support tool (DST) to generate feasible bus-route assignments and help inform DPS’s decisions. The DST employs optimization models to solve the bus-route assignment problem using distance data from Google Maps Application Programming Interface and various interroute reposition-time scenarios to account for the impact of potential traffic delays. The model incorporates multiple objectives related to minimizing cost, meeting demand, and maximizing “consistency”—that is, the difference between a newly created and previously implemented solution The solutions generated by the DST for the 2017–2018 school year utilized significantly fewer buses and lower reposition mileage compared with the DPS solution. Considering the convenience, efficiency, and flexibility of generating high-quality bus-route assignments using the DST, the DPS transportation team has used the DST in the route planning process since 2018.

Integration of custom street view and low-cost motion sensors

Virtual reality is an artificial computer-generated environment generally referred as virtual reality environment which can be navigated and interacted with by a user. Street View, which was released by Google in 2007, is an ideal tool to discover places and locations. This service doesn’t only provide spatial information, but also a virtual reality environment for the user. Since this service is only available in certain locations, Google enables users to create a street view with custom panoramic images with the help of Google Maps Application Programming Interface (API) for JavaScript. In this study, it is aimed to integrate body motions with a custom created street view service for Yildiz Technical University Davutpasa Campus which has a historical environment and huge places to discover. Microsoft Kinect for Xbox 360 motion sensor along with Flexible Action and Articulated Skeleton Toolkit (FAAST) interface has been employed for this purpose. This integration provides a low-cost alternative for virtual reality experience. The proposed system can be implemented for virtual museums, heritage sites or planetariums consisting of panoramic images.

Geographic Service Delivery for Endovascular Clot Retrieval: Using Discrete Event Simulation to Optimize Resources

Endovascular clot retrieval (ECR) is the standard of care for acute ischemic stroke caused by large vessel occlusion. Reducing stroke symptom onset to reperfusion time is associated with improved functional outcomes. This study aims to develop a computational model to predict and identify time-related outcomes of community stroke calls within a geographic area based on variable parameters to support planning and coordination of ECR services. A discrete event simulation (DES) model to simulate and predict ECR service was designed using SimPy, a process-based DES framework written in Python. Geolocation data defined by the user, as well as that used by the model, were sourced using the Google Maps application programming interface. Variables were customized by the user on the basis of their local environment to provide more accurate prediction. A DES model can estimate the delay between the time that emergency services are notified of a potential stroke and potential cerebral reperfusion using ECR at a capable hospital. Variables can be adjusted to observe the effect of modifying each parameter input. By varying the percentage of stroke patients receiving ECR, we were able to define the levels at which our existing service begins to fail in service delivery and assess the effect of adding centers. This novel computational DES model can aid the optimization of delivery of a stroke service within a city, state, or country. By varying geographic, population, and other user-defined inputs, the model can be applied to any location worldwide.

Geospatial Analyses of Accessibility to Down Syndrome Specialty Care

To assess whether the location of 71 Down syndrome specialty care clinics in the US make them inaccessible to a considerable portion of the American population. Using a population-based representative sample of 64 761 individuals with Down syndrome and a Google Maps Application Programming Interface Python program, we calculated the distance each patient with Down syndrome would need to travel to reach the nearest clinic. Two conceptualizations were used-the state fluidity method, which allowed an individual to cross state lines for care and the state boundary method, which required individuals receive care in their state of residence. Almost 1 in 5 US individuals face significant geographic obstacles to receiving specialty care. This finding is especially prominent in the South, where >33% of patients with Down syndrome must travel >2hours to reach their nearest clinic. Down syndrome specialty care clinics are inaccessible to a considerable portion of American society. Innovative usage of technology might be useful to minimize these disparities in healthcare accessibility.

Application of Strategic Transport Model and Google Maps to Develop Better Clot Retrieval Stroke Service

Background and purpose: Two hubs are designated to provide endovascular clot retrieval (ECR) for the State of Victoria, Australia. In an earlier study, Google Maps application programming interface (API) was used to perform modeling on the combination of hospitals optimizing for catchment in terms of current traveling time and road conditions. It is not known if these findings would remain the same if the modeling was performed with a large-scale transport demand model such as Victorian Integrated Transport Model (VITM). This model is developed by the Victorian State Government Transport has the capability to forecast travel demand into the future including future road conditions which is not possible with a Google Maps based applications. The aim of this study is to compare the travel time to potential ECR hubs using both VITM and the Google Maps API and model stability in the next 5 and 10 years.Methods: The VITM was used to generate travel time from randomly generated addresses to four existing ECR capable hubs in Melbourne city, Australia (i.e., Royal Melbourne Hospital/RMH, Monash Medical Center/MMC, Alfred Hospital/ALF, and Austin Hospital/AUS) and the optimal service boundaries given a delivering time threshold are then determined.Results: The strategic transport model and Google map methods were similar with the R2 of 0.86 (peak and off peak) and the Nash-Sutcliffe model of efficiency being 0.83 (peak) and 0.76 (off-peak travel). Futures modeling using VITM found that this proportion decreases to 82% after 5 years and 80% after 10 years. The combination of RMH and ALF provides coverage for 74% of cases, 68% by 5 years, and 66% by 10 years. The combination of RMH and AUS provides coverage for 70% of cases in the base case, 65% at 5 years, and 63% by 10 years.Discussion: The results from strategic transport model are similar to those from Google Maps. In this paper we illustrate how this method can be applied in designing and forecast stroke service model in different cities in Australia and around the world.

Assessment of transport equity to Central Business District (CBD) in Sydney, Australia

ABSTRACTEquity in public transport services is becoming a high priority of transport authorities. Although studies regarding accessibility disparity give some insight into transportation equity, to date there has been no profound application of relative accessibility in equity assessment that combines with public transport need related to social status. In addition, accurate estimation of travel times for building relative accessibility still presents a challenge. Therefore, this research aims to investigate and map spatial distributions of public transport-related equity by examining relative accessibility to CBD, public transport needs, and population density. Google Maps Application Programming Interface is used for a realistic estimation of travel times. Results indicate that most socially disadvantaged groups experience high service levels of public transport to CBD, Sydney. However, there are still 16 regions that require further improvement of public transport services. These results can support urban planners in promoting transport equity among different socio-economic groups.