Latest Contributions by AmyNiessen

Article written by Amy Niessen with contributions from Ciara Rowland-Simms On Wednesday, May 15th the Cardiff R&D Center co-hosted a Rust and C++ birthday party at Yolk Recruitment to celebrate Rust's 4th Birthday! Despite short notice, we were able to get the word out in time for a nice mixture of full-time freelance and hobbyist programmers as well as a few students to join us. Quite a few people expressed interest in helping out with future events while demonstrating a lot of enthusiasm for a Rust/C++ community in Cardiff!  To begin, you can't have a party without cake, and to celebrate the birthday properly, you can see the cake and Ferris the crab, which was made from icing by Jack Kelly's partner, Sofia.   We then began to introduce our speakers. We had Dan Morgan from DevOpsGroup, Ciara Rowland-Simms from Esri, Chris Light from Esri, and Jack Kelly from DevOpsGroup, with Chris doubling as MC for the event.   The first two talks were about learning new languages, specifically Rust and C++. Dan had never done C++ before until that week and spoke on the confusion you face when trying to find best practices and up to date learning materials online. His talk will be part of series documenting his journey into C++, driven by advice from the audience about what resources to use next!   Ciara did a talk on learning Rust, having also never used the language. By contrast, there is very coherent documentation story for Rust as it is a very modern language. The learning experience was therefore comparatively painless and she was able to cover install and setup, including debugging, along with discussing some cool and some controversial Rust language features (such as the heavy use of macros, the ability to do shadowing of variables, and implicit returns).   Chris’s talk was Modern C++: ACCU 2019 revelations and covered some of the cool new features of C++ 20. He also discussed C++ 17 and more broadly the modernization of the language. The talk provoked some really good discussion on some of the network capabilities that are lacking in the standard library and provided a great space for talking about where the language is moving.   The final talk was a dive in Rust best practices, helpful tips, formatting and linting tools, and how easy CI/CD can be with Rust. He really highlighted why people are so excited about Rust and how easily it can leveraged to hit the ground running even with only limited experience in the language.   In the end, we had some specific language questions, which is always good. It seemed there were a lot of really good discussions taking place during the event and, of course, people already sharing excitement in the anticipation of future meetups.    Going forward, we also hope to bring in more speakers – which hopefully won’t be difficult given the enthusiasm from audience members at our first event! We really liked having talks which were a mixture of beginner accessible and provoking good discussion for more experienced developers. We struck that balance pretty well this time and hope to do so again in future meetups. Overall, we were really pleased with how the event went and are really excited to see the beginning of a Cardiff C++/Rust community where we can all learn from each other and grow! We look forward to the next meetup and will be sure to announce it on the Meetup.com page. Be sure to follow it to stay in the loop on our next adventure! ... View more

Last month we hosted a unique GeoDev Webinar when we had Manushi Majumdar share her presentation on "Thinking Spatially and Statistically". Manushi introduced types and characteristics of spatial data and advanced GIS analysis techniques. She was able to cover a few basic concepts of statistics and show how they differ in a spatial context, advancing towards Spatial Machine Learning with ArcGIS.  Here are the questions that were received during the webinar along with their respective answers:   Q: What is the difference between machine learning and statistics? For example, with regression, is there a difference? This always puzzles me! A: Here is a resource to understand the difference: https://www.kdnuggets.com/2016/11/machine-learning-vs-statistics.html Q: Is there any geo-processing tools in built in ArcGIS for running machine learning algorithms? A: Yes, ArcGIS has support for several Machine Learning techniques. We would suggest looking this blog post to learn more about ML support within ArcGIS Desktop. https://www.esri.com/arcgis-blog/products/arcgis-pro/analytics/machine-learning-in-arcgis/ Q: Can you provide the link to the notebook again? A: Hub-Tutorials/GeoDev_ServiceRequests.ipynb at master · esridc/Hub-Tutorials · GitHub  Q: Are there any other good resources for finding examples of utilizing Machine Learning with GIS? A: Here are just a few ArcGIS blogs demonstrating examples: https://www.esri.com/arcgis-blog/products/product/analytics/density-based-clustering-exploring-fatal-car-accident-data-to-find-systemic-problems/ https://www.esri.com/arcgis-blog/products/arcgis-pro/analytics/using-forest-based-classification-and-regression-to-model-and-estimate-house-values/ https://www.esri.com/arcgis-blog/products/arcgis-enterprise/analytics/the-science-of-where-seagrasses-grow-arcgis-and-machine-learning/?rmedium=redirect&rsource=blogs.esri.com/esri/arcgis/2017/09/18/the-science-of-where-seagrasses-grow-arcgis-and-machine-learning   Q: Spatial Join: I see the tool has capability to join two layers with out common attributes . But can this be done on multiple layers in a single shot? The built-in tool has only option to select two layers. What are the options? A: Join works on a 1:1 principle, you can only join one layer to another. That said you can use the concept of table 'Relate' to join one table to many using a common attribute in those tables (does not work spatially). Q: Is it possible to integrate ArcGIS with machine learning software like Jupiter notebook? A: You can use ArcPy as well as the ArcGIS API for Python in Jupyter notebooks.  Q: Is there a way to use machine learning to predict or project possible future incident locations without assigning a z-value? A: Z-score (standard score) denotes the number of standard deviations from the mean a data point is. Simply put, it conveys the distribution of a point around the mean. Prediction or Classification does not need z-scores for input variables. Q: Could you walk us through the hotspot analysis? How do you access these tools? A: Read through this https://pro.arcgis.com/en/pro-app/tool-reference/spatial-statistics/h-how-hot-spot-analysis-getis-ord-gi-spatial-stati.htm to learn more about HotSpot Analysis. It can be accessed within the Spatial Statistics (Mapping Clusters) toolbox in ArcGIS Desktop and under the Analyze Patterns category in ArcGIS Online. Q: Can we do the machine learning analyses using 10.6 geoprocessing tools? A: Yes, apart from the usual, ArcGIS Desktop 10.6 comes with two new tools Deep Learning Model To End and Export Training Data For Deep Learning.  Q: I have a GIS online account. How can I access the data demonstration in ArcGIS online? A: The data I used for my examples is publicly available. Once you add it to your ArcGIS Online account, you can use the Summarize Center and Dispersion tool there to generate spatial mean, median and standard dispersion for your data. Q: How can I use ArcGIS for linear regression or logistical regression analysis? A: Support for regression, both linear and logistic, is available in ArcGIS Desktop Spatial Statistics (Modeling Spatial Relationships) toolbox. Click here https://pro.arcgis.com/en/pro-app/tool-reference/spatial-statistics/an-overview-of-the-modeling-spatial-relationships-toolset.htm to learn more. Q: Is machine learning part of programming, or is it remote sensing? A: Machine Learning involves concepts of statistics as well as algorithms to solve problems based on patterns or inferences drawn from data. Remote sensing, on the other hand, involves studying the planet using remote instruments. Machine Learning can have applications in the field of Remote Sensing, for instance, to detect buildings, roads using satellite imagery data. Q: Is the Jupyter environment embedded directly within Esri ML module? A: ArcGIS Enterprise 10.7 comes with Hosted Notebooks, that lets you perform spatial analysis and data science workflows in a notebook within your portal. Other than that, you can use ArcPy or ArcGIS API for Python in an external Jupyter Notebook too. Q: Which interpolation techniques suits best when you are dealing with underground water data? A: While it depends on your sampling size and distance, Kriging might be a good Interpolation technique. Q: Please suggest out-of-the-box tools provided by ArcGIS for machine learning algo. A: Yes, ArcGIS has support for several Machine Learning techniques. I'd suggest looking this blog post to learn more about ML support within ArcGIS Desktop. https://www.esri.com/arcgis-blog/products/arcgis-pro/analytics/machine-learning-in-arcgis/ For more information, Manushi shared her presentation: GeoDev Webinar - Thinking Spatially and Statistically Also, for the full recording of the webinar, click here. ... View more