19 skills found
ericnewton76 / Gmaps Api NetC# google maps api interface for interacting with the backend web services for Google Maps
marioestrada / JQuery GMapLightweight jQuery plugin that helps you embed Google Maps, using the API V3, into your website. Original at http://gmap.nurtext.de/
googlearchive / Js V2 SamplesSamples for the DEPRECATED Google Maps JavaScript v2 API (formerly known as 'gmaps-samples')
sha3rawi33 / SehhetnaAm writing about "OUR" achievement of winning the 2nd place in "E-sus Mobile Applications Competition" in which, competitors creates Mobile applications and research ideas for solving the "SDGs" sustainable development goals of the UN. Our team's submitted application was named after me "Healthy Wealthy"; we worked on the Public Health. The application uses location services to find the nearest (Hospital, Pharmacy, and even Doctor In the selected specification). Also, it creates a medical profile for each user where you can add all your history, and automatically, the application creates a special QR code for you "Patient" that you can scan it to get all data about you. another service in our application was "Blood bank" in which you donate/request blood from people near you, offering payment or no. which will make it easier to request and donate blood ! another important service was the "Ambulance" in which you click the emergency button and the application calls and sends your Coordinates to the ambulance for Urgent actions. (the government shall support an API for that). one thing else, is that we've created a service for the governmental alerts with notifications for the users. For instance: (all 6 years old children shall be vaccinated against virus C in all the public hospitals, from 17/4 till 24/4). another, is that we've added a service that is specialized in predicting the possible diseases to harm you if your parents had, like those which can be transmitted genetically. another service, is we've created "ask a doctor" tab, where you can ask a doctor for Urgent help, supporting image uploading, then the doctor looks at your profile and your restrictions then starts to give you advice. the application will support booking appointments with doctors and hospital, providing the nearest navigation to those hospitals/Doctors. $$ This application was built with #Flutter using #Dart #lang and created a mobile application for #Android and #iOS $$ all the Databases were built by using #FireBase and we used #Google #GMaps #API Am also honored to be the coder for this application, by helping from my researcher friend Abd El-naser and designer friend Mohamed Hesham ❤️ Thank you guys ❤️
ipang-dwi / SigsbySistem Informasi Geografis (SIG) / GIS Wisata Kota Surabaya Berbasis Web - www.firstplato.com
sameer-shelavale / Blitz Gmap EditorBlitz GMap Editor allows the user to create custom maps using GMAP version 3 API.
ipang-dwi / GisjogjaGISJOGJA - aplikasi web based sistem informasi geografis (SIG) / GIS wisata kota JOGJA - www.firstplato.com
jfsimon / GMapBundle[DEVEL] A Symfony2 bundle to manage GMap API
shane-tomlinson / Polyline EnableEditing EnableDrawing For GMaps API V3Polyline.enableEditing and Polyline.enableDrawing are two features of GMaps v2 that have not made it into v3. This is a work in progress to add this functionality.
AugustusCosta / Strapi Plugin Gmaps Api ServicesStrapi plugin for use Google Maps API services.
SheffieldSolar / GeocodeGeocode postcodes, addresses, LLSOAs or Constituencies using the Code Point Open database, ONS data or GMaps API
ipang-dwi / GisflotimGISFLOTIM - aplikasi berbasis web sistem informasi geografis (SIG) / GIS wisata FLORES TIMUR - www.firstplato.com
julienben / Gmaps Apistyle EncoderSmall utility to encode your styles when requesting map tiles from Google
alexbieber / Google Map Api ScannerNow you can scan your google map api to see its vulnerable or not specially made for bug bounty hunters!🔴🔴🔴🔴✔
simplyrains / Pointcloud[C++, OpenCV, GMaps API] Project: generating 3D Point cloud from google streetview images using OpenCV and SfM
mariuskubilius / Li3 GMapApiGoogle maps webservice api data source for lithium
alduinien / GoogleMapDownloaderA script to generate high resolution maps from GMap API
zichenma / NopdataNopCommerce (www.nopcommerce.com) is an open source e-commerce online store platform, which is based on .NET framework. Many business use NopCommerce to build their online store. However, for the sales data, it only has simple tables to display these data. This application is special for visualizing and analyzing NopCommerce order data. The application uses D3.js and DC.js JavaScript library, which enables NopCommerce users export order data from NopCommerce administration panel to the NopData and eventually generate line chart, pie chart and order distribution on google map. Web Technologies: HTML5 Web API, Google Geolocation API, IPinfo API, D3.js, Dc.js, Crossfilter.js AngularJS, jQuery, BootStrap,Gmap.js
Kwamb0 / API HomeworkPart I - WeatherPy In this example, you’ll be creating a Python script to visualize the weather of 500+ cities across the world of varying distance from the equator. To accomplish this, you’ll be utilizing a simple Python library, the OpenWeatherMap API, and a little common sense to create a representative model of weather across world cities. Your first objective is to build a series of scatter plots to showcase the following relationships: Temperature (F) vs. Latitude Humidity (%) vs. Latitude Cloudiness (%) vs. Latitude Wind Speed (mph) vs. Latitude After each plot add a sentence or too explaining what the code is and analyzing. Your next objective is to run linear regression on each relationship, only this time separating them into Northern Hemisphere (greater than or equal to 0 degrees latitude) and Southern Hemisphere (less than 0 degrees latitude): Northern Hemisphere - Temperature (F) vs. Latitude Southern Hemisphere - Temperature (F) vs. Latitude Northern Hemisphere - Humidity (%) vs. Latitude Southern Hemisphere - Humidity (%) vs. Latitude Northern Hemisphere - Cloudiness (%) vs. Latitude Southern Hemisphere - Cloudiness (%) vs. Latitude Northern Hemisphere - Wind Speed (mph) vs. Latitude Southern Hemisphere - Wind Speed (mph) vs. Latitude After each pair of plots explain what the linear regression is modelling such as any relationships you notice and any other analysis you may have. Your final notebook must: Randomly select at least 500 unique (non-repeat) cities based on latitude and longitude. Perform a weather check on each of the cities using a series of successive API calls. Include a print log of each city as it’s being processed with the city number and city name. Save a CSV of all retrieved data and a PNG image for each scatter plot. Part II - VacationPy Now let’s use your skills in working with weather data to plan future vacations. Use jupyter-gmaps and the Google Places API for this part of the assignment. Note: if you having trouble displaying the maps try running jupyter nbextension enable --py gmaps in your environment and retry. Create a heat map that displays the humidity for every city from the part I of the homework. heatmap Narrow down the DataFrame to find your ideal weather condition. For example: A max temperature lower than 80 degrees but higher than 70. Wind speed less than 10 mph. Zero cloudiness. Drop any rows that don’t contain all three conditions. You want to be sure the weather is ideal. Note: Feel free to adjust to your specifications but be sure to limit the number of rows returned by your API requests to a reasonable number. Using Google Places API to find the first hotel for each city located within 5000 meters of your coordinates. Plot the hotels on top of the humidity heatmap with each pin containing the Hotel Name, City, and Country. hotel map As final considerations: Create a new GitHub repository for this project called API-Challenge (note the kebab-case). Do not add to an existing repo You must complete your analysis using a Jupyter notebook. You must use the Matplotlib or Pandas plotting libraries. For Part I, you must include a written description of three observable trends based on the data. You must use proper labeling of your plots, including aspects like: Plot Titles (with date of analysis) and Axes Labels. For max intensity in the heat map, try setting it to the highest humidity found in the data set. Hints and Considerations The city data you generate is based on random coordinates as well as different query times; as such, your outputs will not be an exact match to the provided starter notebook. You may want to start this assignment by refreshing yourself on the geographic coordinate system. Next, spend the requisite time necessary to study the OpenWeatherMap API. Based on your initial study, you should be able to answer basic questions about the API: Where do you request the API key? Which Weather API in particular will you need? What URL endpoints does it expect? What JSON structure does it respond with? Before you write a line of code, you should be aiming to have a crystal clear understanding of your intended outcome. A starter code for Citipy has been provided. However, if you’re craving an extra challenge, push yourself to learn how it works: citipy Python library. Before you try to incorporate the library into your analysis, start by creating simple test cases outside your main script to confirm that you are using it correctly. Too often, when introduced to a new library, students get bogged down by the most minor of errors – spending hours investigating their entire code – when, in fact, a simple and focused test would have shown their basic utilization of the library was wrong from the start. Don’t let this be you! Part of our expectation in this challenge is that you will use critical thinking skills to understand how and why we’re recommending the tools we are. What is Citipy for? Why would you use it in conjunction with the OpenWeatherMap API? How would you do so? In building your script, pay attention to the cities you are using in your query pool. Are you getting coverage of the full gamut of latitudes and longitudes? Or are you simply choosing 500 cities concentrated in one region of the world? Even if you were a geographic genius, simply rattling 500 cities based on your human selection would create a biased dataset. Be thinking of how you should counter this. (Hint: Consider the full range of latitudes). Once you have computed the linear regression for one chart, the process will be similar for all others. As a bonus, try to create a function that will create these charts based on different parameters. Remember that each coordinate will trigger a separate call to the Google API. If you’re creating your own criteria to plan your vacation, try to reduce the results in your DataFrame to 10 or fewer cities. Lastly, remember – this is a challenging activity. Push yourself! If you complete this task, then you can safely say that you’ve gained a strong mastery of the core foundations of data analytics and it will only go better from here. Good luck!
