Dev Summit 2022: Extending ArcGIS and spatial analysis

In this morning’s Developer Summit plenary, Jay Theodore and many of our colleagues presented several powerful workflows to showcase ArcGIS as an enterprise-ready automation platform to operate geospatial infrastructure.

We saw how to conduct GIS service autoscaling, manage organizational assets in near-real time, and to use cloud DevOps to automate an ArcGIS Enterprise on Kubernetes deployment.

By adopting these techniques, administrators may be better equipped to minimize effort to effectively monitor and maintain their organization’s geospatial assets.

Automate your ArcGIS system

In this section, see examples of the ways in which ArcGIS is delivered as an enterprise-ready automation platform. Automation can be performed through APIs, low code and no code solutions.

Learn more about the demonstrations presented in this section:

GIS workflow automation

Nanae conducted incident analysis to identify at-risk populations using hurricane data. She then used a task to automate the analysis to repeat the workflow using the latest incident data available.

Event-driven automation with notebooks

Yundi and Bill demonstrated how to manage organization assets in near real-time using ArcGIS Knowledge and other ArcGIS capabilities.

Automate your deployments

Eva demonstrated how to use an automated deployment pipeline to create an ArcGIS Enterprise organization.

Automate your scaling operations

Shreyas highlighted new autoscaling capabilities in ArcGIS Enterprise on Kubernetes that allow administrators to configure and deploy production systems that respond to unexpected performance demands with minimal intervention and overhead.

Spatial analytics system

From data to analytic engines to experiences, see what makes ArcGIS a powerful platform for spatial data science and GeoAI, applying GIS – both the science and technology.

Advancing spatial science

Lauren Bennett and team showed how ArcGIS is advancing data science with spatiotemporal statistics, multidimensional raster analysis, and Geospatial AI. They present the ways in which spatial analysis can be used in ArcGIS to uncover hidden patterns, aid in decision making, and in turn, solve complex real-world problems. Now, more than ever, our spatial approach is critical to confront complex and interconnected challenges in the world such as climate change, loss of biodiversity, social inequities and more.

Learn more about the demonstrations presented in this section:

Advancements in spatial statistics

Alberto Nieto conducted analysis to predict the presence of hospitable habitats for gopher tortoises across the US to assess the effects of various climatic factors on their presence.

Multidimensional raster analysis

Hong Xu demonstrated how to use Principal Component Analysis (PCA) for spatial and temporal pattern analysis of SST data over several decades.

Geospatial AI

Ling Tang leveraged the power of deep learning to identify what percent of people in a subset of the Rohingya refugee camps lack access to a washroom within a 2.5-minute walk, which can help optimize facility allocation to better address the growing water and sanitation needs in the settlement.

ArcGIS Notebooks web tools

Lingtao Xie showed how to publish a notebook as a web tool to create a route and locate electric vehicle charging stations along the way. Her analysis is based on data provided by the National Renewable Energy Laboratory (NREL).

ArcGIS GeoAnalytics On-Demand Engine

Xirui Xu leverages GeoAnalytics On-Demand Engine within an Azure Databricks hosted notebook to perform custom big data analysis.

About the authors

Sara is a lead product engineer on the ArcGIS Enterprise team. She enjoys spending time at the lake, non-fiction, and cooking.

As Esri’s Chief Technology Officer for Enterprise and AI technologies, Jay Theodore guides the long-term vision of the ArcGIS system, designed for enterprises and infused with advanced Analytics, Data Science and AI techniques.

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