Fall NEARC 2018

AUTHORS: Elizabeth Arabadjis, VHB

ABSTRACT: This presentation will highlight projects undertaken for sustainability groups to leverage technologies to meet goals including IoT, business analytics, and GIS. Various data are analyzed to better understand the planned initiatives. Trends, deficiencies, and the power of the “where” in the data can be used to better understand what is working. Methods used to screen sustainability initiatives using evaluation criteria to determine feasibility, costs, and benefits will be discussed.

AUTHORS: Katie Budreski*, Jonnie Dunne, Lauren Padilla – Stone Environmental

ABSTRACT: As GIS analysts and data scientists, we rely on the high quality datasets prepared by authoritative sources, such as the National Land Cover Database (NLCD) published by the Multi-Resolution Land Characteristics Consortium (MRLC) and the Cropland Data Layer (CDL) published by the National Agriculture Statistics Service (NASS). These datasets allow us to do powerful and meaningful nationwide analyses. But to fully trust the data for planning and decision making, we need to understand its limitations and incorporate known uncertainty into analyses. Additionally, ancillary data and machine learning can be used to improve locational and information confidence, such as high resolution imagery and image segmentation.

AUTHORS: Mark Zito, CDM Smith

ABSTRACT: Geospatial Analysis can be considered a subset of Data Science. To consider yourself a data scientist, you need to be skilled at programming, data visualization, statistics, machine learning, linear algebra and data wrangling. You also need to know the tools that make all this possible and Python and Jupyter notebooks are leading the pack. Through Esri’s ArcGIS API for Python, Jupyter notebooks are now baked into your desktop software package. In this talk, we will cover the basics of using the notebook with your organizations GIS data and even explore some machine learning models that push the boundaries of what you can do in GIS. You may be asking, why Jupyter notebooks? These notebooks allow you to create and share with live code, markdown text, visualizations including maps, statistical models and more. They are all the rage in the data science community and offer a simple way to share and document your code.