Environmental Sensing Monitoring and Analyzing Water Temperatures Mark Caruso, Joseph A. Hassan, Leanne Denise Keeley,, Sheetal Vasant Nikam, AJ Zada Team One

Introduction During the spring of 2017 a team of Computer Science students at Pace University developed a temperature monitoring device using a microcontroller with attached sensors to collect air and water temperature data from Choate Pond. The collected data was sent over a Wi-Fi connection and stored in a database. In the fall of 2017 another team of Pace CS students began integrating with the previously developed DBMS. The goal was to build a web-based tool that would offer the user options on how they’d like to visualize, and analyze the data. It offered line, histogram, and bar chart visualizations. Standing on the shoulders of the above work, this study focused on data analyses and presenting the data in manner appropriate to its domain. Introduction

Goals Challenges Expand the existing data-set by simulating a situation where data has been collected over several years. Use a set of metrics on the data that is appropriate for its domain. Expose the data, through a web application, in its raw and analyzed form. Manage “Big Data” Provide Meaning through analysis and Visualization Develop Software that can allow for features not yet imagined Goals and Challenges

Data Analyses Data Visualization Big What? A component of a larger System The intersection between... IoT Data Analyses Data Visualization Big What? Environmental Science Web Application The Big Ideas

Big Data Data Analytics Environmental Science Water Temp Velocity 1. Monitor long-term changes in climate Velocity Volume Water Temp 2. Predict biological and chemical activity in bodies of water Big Data 3. judge the overall health of an ecosystem Data Analytics Variety Time Series CSV Veracity Raw Data Temp / Time JSON DTR= Tmax – Tmin / month Storage is More cost effective than paper DD = (Tmax –Tmin)/2 Research – Summary Big Data and Data Analytics

App Environment & Ecosystem CSS HTML5 Client / Server Dev Tools In The Cloud JS MVC PaaS 9 – Request Data & Connect to DB 5 - Return a Resource Bluemix ExpressJS 10 - Query DB & Respond NoSQL System Architecture is done Node + Express JQuery

DV - “A Picture is worth a thousand words” “Data visualization is the presentation of data in a pictorial or graphical format it enables decision makers to be able to see analytics represented visually so they can grasp difficult concepts or identify new patterns.” Research – Data Visualization

D.V. Tools D3 Scalable Vector Graphics Google Charts Charts.js Raster Library Chart Types Interactivity Events Customization Expressivity Rendering Method D3.js Data-Driven-Documents No charts out-of-the box Defined by developer on selected elements using JS event handling functions All aspects are customizable by developer using library methods, CSS, and pure JavaScript JavaScript, CSS & HTM5/SVG Google Charts API 12 core chart types plus 16 more Out-of-box tooltips with additional event handling through JS event handling functions Ready to use out-of-the-box. Customizations available through an options object. Some specific to each chart, others generic JavaScript, HTML5/SVG, VML for older IE browsers such as IE8 Charts.js 9 core chart types out-of-the-box tooltips and standard JS events passed into an options object as a string[] Customization available on major chart components such as colors, fonts, layout, legend, and ToolTips JavaScript HTML5/ Canvas D.V. Tools HTML5 / SVG D3 Scalable Vector Graphics Data-Driven-Documents Google Charts HTML5 <canvas> Charts.js Raster

Water Monitoring Analyzer The Web Application Water Monitoring Analyzer

Next Steps Conclusion Possible to build a single-paged web-based tool that provides an easy to use solution for visualizing and analyzing large data sets. Add additional metrics to the exisiting set. Test the application while receiving real-time data. Reinforces the notion that when it comes to gaining insight into big data, data visualization is the way to go Add a feature to view similar data from various locations. Support the case for offering the device as well as a visualization tool to educators interested in exploring “The Internet of Things” with their students. Compare and combine the data received from the device with other relevant data-sets such as Geographical Information System.