Maps Application Programming Interface Research Articles

Finding one source of truth between multiple digital asset management platforms at Autodesk

The Autodesk digital asset management (DAM) system utilises an application programming interface (API) protocol to connect assets between two digital asset management platforms. In preparing, designing and implementing the API, the DAM librarian team reduced taxonomic noise, reinforced file-naming to one schema and backed up the asset life cycle to a single archive. These decisions and implementations have helped the Autodesk DAM team to work with a single source of truth for marketing and sales assets. This paper covers how the DAM API functions, the benefits the API has brought the team and asset stakeholders, and the single source of truth model reinforced by the API call.

Development of mobile application for hypertension surveillance and telemonitoring in older persons in rural community

Hypertension is a cause of cardiovascular complications and premature death worldwide. Prevalence of hypertension is doubled older population. Close monitoring must be promptly equipped by healthcare team without delays to preserve intact cardiovascular system. In Thailand, village health volunteers (VHVs, OSOMO in Thai) are key healthcare personnel that co-ordinate health information between the older persons and health care professionals. The VHVs have been trained to measure blood pressure, record, and submit a report to health professionals in monthly basis. However, the blood pressure records in some health areas were still incomplete due to incontiguous data collection and difficult access to the older persons. This project was aimed to develop a geographical information system (GIS)-based mobile application for hypertension surveillance and monitoring in older persons in rural community by the VHVs using the analysis, design, development, implementation, and evaluation (ADDIE) model. Results showed that blood pressure data of 6,421 (76.92%) of the older persons were recorded. We employed the Google Map Application Programming Interface (API) to identify latitude and longitude of the older persons’ living locations. Other health data were also recorded.

Исследование передачи SMS-трафика в сети связи

The article explores the features and methods of sending SMS traffic using the API (Application Programming Interface), discusses the parameters and API functions for sending Short Message Service (SMS) messages, also analyzes of causes of the loss of the traffic and the options for subsequent prevention of this loss. The scheme of the interaction between the Short Message Service Center (SMSC) and ordinary subscribers is considered. The popular protocol Short Message Peer-to-Peer (SMPP) was used as the protocol under study, capable of transmitting any type of messages, and is an open protocol in the telecommunications industry, which is designed specifically to provide a flexible interface for exchanging SMS messages between SMS applications, platforms, routers and short message service centers. The analysis of the captured traffic was carried out using special software – Wireshark, that allows to apply filters to track the protocols that are used for SMS transmission. Various types of losses in telecommunication networks are considered and a correlation is established with the factors which affect the communication channel. The results obtained can be used to develop an algorithm for searching for lost packets that occur when traffic passes through switching nodes. This model is able to improve the quality of service for telecom operators.

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.

Multimedia Conferencing at the Network Edge

The fifth generation (5G) mobile system provides enhanced data rates, ultra-low latency, and better network resource usage for Internet of Things (IoT) devices in a dense environment. The integration of 5G IoT and multimedia conferencing enables collaboration between humans and devices by launching new capabilities. In this paper, an approach to define Application Programming Interfaces (API) for multimedia conference control is presented. The API design follows the principles of Multi-access Edge Computing (MEC) and features the inherent advantages of MEC. The API is described by resource structure, data types, and sequence diagrams that illustrate the API functionality. Some implementation issues are discussed, and the injected latency is evaluated.

Using Satellite Images and Deep Learning to Identify Associations Between County-Level Mortality and Residential Neighborhood Features Proximal to Schools: A Cross-Sectional Study.

What is the relationship between mortality and satellite images as elucidated through the use of Convolutional Neural Networks?Background: Following a century of increase, life expectancy in the United States has stagnated and begun to decline in recent decades. Using satellite images and street view images, prior work has demonstrated associations of the built environment with income, education, access to care, and health factors such as obesity. However, assessment of learned image feature relationships with variation in crude mortality rate across the United States has been lacking.Objective: We sought to investigate if county-level mortality rates in the U.S. could be predicted from satellite images.Methods: Satellite images of neighborhoods surrounding schools were extracted with the Google Static Maps application programming interface for 430 counties representing ~68.9% of the US population. A convolutional neural network was trained using crude mortality rates for each county in 2015 to predict mortality. Learned image features were interpreted using Shapley Additive Feature Explanations, clustered, and compared to mortality and its associated covariate predictors.Results: Predicted mortality from satellite images in a held-out test set of counties was strongly correlated to the true crude mortality rate (Pearson r = 0.72). Direct prediction of mortality using a deep learning model across a cross-section of 430 U.S. counties identified key features in the environment (e.g., sidewalks, driveways, and hiking trails) associated with lower mortality. Learned image features were clustered, and we identified 10 clusters that were associated with education, income, geographical region, race, and age.Conclusions: The application of deep learning techniques to remotely-sensed features of the built environment can serve as a useful predictor of mortality in the United States. Although we identified features that were largely associated with demographic information, future modeling approaches that directly identify image features associated with health-related outcomes have the potential to inform targeted public health interventions.

Python interfaces for the Smoldyn simulator.

Smoldyn is a particle-based biochemical simulator that is frequently used for systems biology and biophysics research. Previously, users could only define models using text-based input or a C/C++ application programming interface (API), which were convenient, but limited extensibility. We added a Python API to Smoldyn to improve integration with other software tools, such as Jupyter notebooks, other Python code libraries and other simulators. It includes low-level functions that closely mimic the existing C/C++ API and higher-level functions that are more convenient to use. These latter functions follow modern object-oriented Python conventions. Smoldyn is open source and free, available at http://www.smoldyn.org and can be installed with the Python package manager pip. It runs on Mac, Windows and Linux.

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.

Fishpro (Online Fisheries Product): Application Design Of Sell And Buy Fisheries Product To Increase Fishery Businesses Income Of Nusantara

The fisheries sector is a strategic sector to increase the country's foreign exchange in national development. However, due to the emergence of the Corona Virus Disease 19 (Covid-19) outbreak, the capture fisheries sector was affected by the pandemic. Fishery business actors, namely fishermen, small-scale fish traders (retail traders), and MSME (Micro, Small Medium Enterprises) owners have been affected by the Covid-19 pandemic. The income of fishery business actors has decreased due to decreasing market demand so that fish prices have also decreased. It is more difficult for fishery business actors to market their fishery products. Fisheries actors must be able to take advantage of opportunities during a pandemic by changing the marketing system to digital or online based so that capture fisheries products can be absorbed optimally. Therefore, there is a solution to overcome this problem, namely the application of buying and selling fish, both fresh and processed fish that can be reached by the wider community on an online basis. This application is an online fish buying and selling application that connects sellers with consumers directly with a delivery system with drivers. The application design uses the Waterfall Software Development Lifecycle (SDLC) method which consists of six stages, namely analysis, design, implementation, testing, deployment, and maintenance. The materials (software) used in application design according there are android studio, firebase, and google map API (Application Programming Interface). The FISHPRO application is real-time, which is capable of updating automatically in the system. The features in FISHPRO are purchase, co-book, discussion, news, nutritional information, accounts, activities. This application is claimed to be a complete package application because in one application include features that are useful for users so that they get more information. Application that is simple, practical, easy and comfortable to operate by the user.

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.

Analysis of Factors Affecting the Severity of Automated Vehicle Crashes Using XGBoost Model Combining POI Data

The research and development of autonomous vehicle (AV) technology have been gaining ground globally. However, a few studies have performed an in-depth exploration of the contributing factors of crashes involving AVs. This study aims to predict the severity of crashes involving AVs and analyze the effects of the different factors on crash severity. Crash data were obtained from the AV-related crash reports presented to the California Department of Motor Vehicles in 2019 and included 75 uninjured and 18 injured accident cases. The points-of-interest (POI) data were collected from Google Map Application Programming Interface (API). Descriptive statistics analysis was applied to examine the features of crashes involving AVs in terms of collision type, crash severity, vehicle movement preceding the collision, and degree of vehicle damage. To compare the classification performance of different classifiers, we use two different classification models: eXtreme Gradient Boosting (XGBoost) and Classification and Regression Tree (CART). The result shows that the XGBoost model performs better in identifying the injured crashes involving AVs. Compared with the original XGBoost model, the recall and G-mean of the XGBoost model combining POI data improved by 100% and 11.1%, respectively. The main features that contribute to the severity of crashes include weather, degree of vehicle damage, accident location, and collision type. The results indicate that crash severity significantly increases if the AVs collided at an intersection under extreme weather conditions (e.g., fog and snow). Moreover, an accident resulting in injuries also had a higher probability of occurring in areas where land-use patterns are highly diverse. The knowledge gained from this research could ultimately contribute to assessing and improving the safety performance of the current AVs.

Generative Adversarial Network for Global Image-Based Local Image to Improve Malware Classification Using Convolutional Neural Network

Malware detection and classification methods are being actively developed to protect personal information from hackers. Global images of malware (in a program that includes personal information) can be utilized to detect or classify it. This method is efficient, given that small changes in the program can be detected while maintaining the overall structure of the program. However, if any obfuscation approach that encrypts malware code is implemented, it becomes difficult to extract features such as opcodes and application programming interface functions. Given that malware detection and classification are performed differently depending on whether malware is obfuscated or not, methods that can simultaneously detect and classify general and obfuscated malware are required. This paper proposes a method that uses a generative adversarial network (GAN) and global image-based local image to classify unobfuscated and obfuscated malware. Global and local images of unobfuscated malware are generated using pixel and local feature visualizers. The GAN is utilized to visualize local features and generate local images of obfuscated malware by learning global and local images of unobfuscated malware. The local image of unobfuscated malware is merged with the global image generated via the pixel visualizer. To merge the global and local images of unobfuscated and obfuscated malware, the pixels extracted from global and local images are stored in a two-dimensional array, and then merged images are generated. Finally, unobfuscated and obfuscated malware are classified using a convolutional neural network (CNN). The results of experiments conducted on the Microsoft Malware Classification Challenge (BIG 2015) dataset indicate that the proposed method has a malware classification accuracy of 99.65%, which is 2.18% higher than that of the malware classification approach based on only global images and local features.

Multiparty Call Control at the Network Edge

Network programmability is a key feature of fifth generation (5G) system which, in combination with cloud-based services, can support many use cases, including mission critical and healthcare communications. Programmability enables flexibility in customization of service connectivity. Multi-access Edge Computing (MEC) services and applications are enablers for network programmability. In this paper, MEC capabilities for programmability of multiparty multimedia call control at the network edge are studied. Multiparty video calls are one of the key applications of 5G, and are efficient way to exchange ideas, knowledge, expertise, information, and so on. The paper presents an approach to design MEC Application Programming Interfaces (APIs) which enable third party applications to create multiparty multimedia sessions and dynamically manage session participations. The API functionality is described by required information and message flows. The paper specifies the proposed MEC API with data model. Feasibility study includes modelling and formal validation of multiparty session state models supported by the network and mobile edge application. The latency injected by the API is evaluated by emulation.

Association between Neighborhood Food Environment and Body Mass Index among Older Adults in Beijing, China: A Cross-Sectional Study

Objective: To investigate the association between the neighborhood food environment and body mass index (BMI) among Chinese older adults. Methods: A multi-stage stratified random sampling method was used to recruit participants from 12 communities in Beijing, China, in 2019. Participants (n = 1764, 1034 women) in this study were older adults aged 65 to 80. We collected the participants’ basic information, measured their height and weight, and calculated their BMI. Neighborhood food environments were measured by the density of and proximity to different food outlets using the Baidu Map Application Programming Interface. Adjusted multiple linear regression was performed to estimate the association between the food environment and BMI. Results: Participants had a mean age of 69.7 ± 4.32 years old and an average BMI of 26.3 ± 3.50 kg/m2. Among the three types of stores, convenience stores had the easiest access, followed by greengrocers and supermarkets. Sit-down restaurants had the best access among different restaurants, followed by Chinese fast-food restaurants, and western fast-food restaurants had the worst access. Easier access to greengrocers (β = 0.281, p < 0.001) and sit-down restaurants (β = 0.304, p < 0.001) was associated with higher BMI in the 250 m buffer zone. More supermarkets were associated with higher BMI in the 500 m buffer zone (β = 0.593, p < 0.001). Access to convenience stores was positively associated with BMI in the 800 m buffer zone (β = 0.057, p < 0.001). Better access to Chinese fast-food restaurants was associated with higher BMI (β = 0.071, p = 0.001), and better access to western fast- food restaurants was associated with lower BMI (β = −0.400, p < 0.001) in the 1000 m buffer zone. There was a negative association between the nearest distance to greengrocers and BMI (β = −0.004, p < 0.001). Conclusion: Although we found some significant associations between the neighborhood food environment and obesity, the current results are not strong enough to draw specific conclusions. Policymakers will need to rely on more evidence to derive concrete policy recommendations.

Vulca Loc A Mobile Application for Finding Vulcanizing Shops embedding GPS

This paper discusses the ideas and process of developing a mobile locator application for Vulcanizing Shops in Siargao Island with Global Positioning System (GPS) and Google Map Application Programming Interface (API). This mobile application is an innovation tool to show the location, availability and services of the vehicle services shops available in the island to ease the hassle of the tourists with vehicle errors. It also provides shortest possible route method that includes relevant information about the services of the shops. The study adopts the Rapid Application Development model and used ISO 9126 to evaluate the application in terms of usability (4.37), functionality (4.13) and Maintainability (4.20). Therefore, the application is certain to provide a significant support to the local and foreign tourists; therefore, providing an accurate and hassle time-free locating a vulcanizing shops.

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.

School Commuting Mode Shift: A Scenario Analysis for Active School Commuting Using GIS and Online Map API

Active school commuting provides a convenient opportunity to promote physical activity for children, while also reducing car dependence and its associated environmental impacts. School–home distance is a critical factor in school commuting mode choice, and longer distances have been proven to increase the likelihood of driving. In this study, we combine open-access data acquired from Baidu Map application programming interface (API) with GIS (Geographic Information System) technology to estimate the extent to which the present school–home distances can be reduced for public middle schools in Jianye District, Nanjing, China. Based on the policies for school planning and catchment allocation, we conduct a scenario analysis of school catchment reassignment whereby residences are reassigned to the nearest school. The results show that, despite the government’s ‘attending nearby school’ policy, some students in the study area are subjected to excess school–home distances, and the overall journey-to-school trips can be reduced by 20.07%, accounting for 330.8 km. This excess distance indicates the extent to which the need for vehicle travel can be potentially reduced in favor of active school commuting and a low-carbon lifestyle. Therefore, these findings provide important insights into school siting and school catchment assignment policies seeking to facilitate active school commuting, achieve educational spatial equity and reduce car dependence.