The Multi-Criteria Analysis (MCA) model is an innovative decision-support tool designed to enhance stormwater management practices by incorporating and analysing diverse environmental, structural, and social criteria. Developed to address the complexities of stormwater asset management in urban environments, the MCA model integrates geospatial analysis with a user-friendly interface, utilising tools from the Esri ecosystem such as ArcGIS for Excel for ArcGIS, ArcGIS Experience Builder, ArcGIS Field Maps and ArcGIS Notebooks.
This tool dynamically evaluates multiple criteria, including flood risk models, existing asset condition and age, and runoff quality, across various urban settings to aid in identifying optimal stormwater management interventions. The model uses high-resolution spatial datasets and enables stakeholders to adjust criteria weights based on local priorities, thus providing flexible, location-specific solutions. By leveraging ArcGIS Notebooks and ArcGIS for Excel, the tool automates data processing and analysis, streamlining workflows for large-scale urban stormwater assessments.
This presentation will showcase the MCA model's development process, from tool conceptualisation to implementation, highlighting how Esri’s comprehensive suite of tools can be used to create effective and scalable solutions for complex stormwater challenges. Attendees will gain insights into how ArcGIS for Excel can be used to allow seamless integration of spatial data with tabular analysis, how Experience Builder and Python Notebooks can enhance user interaction, and how the entire Esri ecosystem facilitates robust, multi-criteria stormwater management strategies.

In 2021 Auckland Transport’s GIS setup underwent a major overhaul with an almost complete reinvention of the team coinciding with a major migration to Azure. Add to this a directive that hardware and software deployments be automated as much as possible, and you have a landscape that was rather daunting for new staff who were still fairly new to system administration. This is the story of a journey that has covered one implementation, two major upgrades and multiple patches, traversed both Chef and Powershell DSC automation approaches and involved a lot of experimentation and learning, taking us from apprehensive to relatively assured and enthusiastic.

Waikato Region, a powerhouse of national forest GDP, boasts vast swathes of plantation forestry. However, ensuring sustainable practices requires vigilant monitoring of forest harvesting operations. Uncontrolled harvesting, particularly in erosion-prone areas or near waterways, can have a devastating environmental impact. To combat this challenge, the Waikato Regional Council (WRC), the region's environmental guardian, embarked on a pioneering initiative: an automated system for near real-time monitoring of plantation clearings.

Partnering with Indufor Asia Pacific Ltd, the WRC developed "Dwell," a revolutionary platform that leverages the power of freely available Earth observation satellite imagery. Dwell harnesses the eagle-eye view of Sentinel-2 satellites to detect any plantation clearing exceeding 1 hectare across the entire Waikato region. It acts as a tireless watchdog, constantly analysing changes in canopy cover within plantation forests with every new Sentinel-2 image. Upon detecting a potential violation, Dwell generates an instant alert with crucial details like the date, satellite image ID, and extent of the clearing. These alerts populate operational dashboards with clear visualizations, empowering the WRC's monitoring and compliance teams to take swift action. This system is capable of visualizing plantation harvesting clearings in near real-time and inspecting any unreported harvesting operations in a timely manner. As other regional councils also want to monitor their plantation clearings, and currently there aren't any other automated, frequent, and near real-time solutions adopted, this would be an interesting method to create a seamless monitoring platform for identifying plantation sites.