The state of the Open Geospatial World in 2019
This article represents my takeaways from FOSS4GNA 2019 conference. FOSS4G - free and open source software for geospatial industry is part of the larger OSGeo organization.
This article represents my takeaways from FOSS4GNA 2019 conference. FOSS4G - free and open source software for geospatial industry is part of the larger OSGeo organization.
Jupyter Notebook has become incredibly popular among data scientists and general users of Python and R. While the Jupyter framework is liberal and lets you be creative, it would benefit you, your team, and your readers if you define a structure and follow it. Based on my experience as developer evangelist and the author of public-facing notebooks for the last three years, I share in this article the patterns I recommend for writing data science samples using Jupyter Notebook. Read more here
At some point in time, each of us would have went through the process of either renting or buying a house. Whether we realize or not, a lot of factors we consider important are heavily influenced by location. In this article, we apply the data wrangling capabilities of scientific Python ecosystem and geospatial data visualization & analysis capabilities of the ArcGIS platform to build a model that will help shortlist good properties (houses). Read more here
One of my favorite statistics is that about 80% of data in the world, contain some element that is spatial. For instance, take the list of gas stations in a city or restaurants in a city, revenue from medical industry, there is always some element of this data that can be categorized as being spatial - be it locations, routes the products take, cost variations in gas prices etc.
This spatial relationship is of significant interest to me and I have been analyzing them for over a decade and have been building software to analyze them for more than half of the past decade. While majority of what I built were proprietary, this blog is a look at what is available in the open-source ecosystem and when to use which tool.
ArcGIS apps give you access to work imagery data from a variety of file formats. The goal is to unify the differences in image characteristics (spatial, spectral, temportal resolutions), file formats (local - different types of image formats, mosaic and web). However, it is useful to understand the basics. This page does not teach you remote sensing or spatial analysis, it just gives you a roadmap to navigate the software.
This wiki covers the steps involved in building a data science project using Azure Machine Learning Workbench product. This also covers the steps involved in productionizing the model as a web service and accessing it over HTTP using its REST API.
Below is a non-exhaustive list of tutorials that I authored for ArcGIS. The objective of these tutorials is to demonstrate spatial data analysis using the scientific computing ecosystem of Python and the ArcGIS stack.
We are pleased to announce the newest release of the ArcGIS API for Python (version 1.3) ahead of the holiday season. This version packs some serious enhancements for GIS administration, content management, and performing spatial analysis… Read more here
Big data is the talk of the town. What is it? What are the components? Let us demystify some of these jargons
The ArcGIS API for Python is a new Python library for working with maps and geospatial data that is powered by Web GIS, whether online or on-premises. It is a Pythonic API that uses Python best practices in its design and employs standard Python constructs and data structures with clean, readable idioms… Read more here