2023 SAG Award Winners

Statistics Centre Abu Dhabi-SCAD

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Project Goal

The GeoAI-driven Predictive Analytics project in Abu Dhabi Emirate aims to use geospatial data and artificial intelligence techniques to generate insights and foresights for decision-making in the region. The project utilizes various sources of data, such as satellite imagery, private, and public data, to develop predictive models and visualizations that can be used to inform policies related to urban planning, environmental management, and infrastructure development. The project leverages also spatial statistics and machine learning algorithms to identify patterns and relationships between different datasets, and develops predictive models for future events and trends. Ultimately, the goal of the project is to provide decision-makers with a comprehensive understanding of the complex social, economic, and environmental factors that shape the region, and help them make informed decisions that can lead to a sustainable future for Abu Dhabi Emirate
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Business Problem Solved

The GeoAI-driven Predictive Analytics project in Abu Dhabi Emirate solves a number of business problems related to decision-making and planning in the region. These include:
• Lack of data-driven insights: Traditional methods of data collection and analysis can be time-consuming and costly, leading to a lack of comprehensive data-driven insights. The project aims to use GeoAI techniques to analyze large volumes of data from various sources, and generate insights and foresights that can inform decision-making.
• Inefficient resource allocation: Limited resources can lead to inefficient resource allocation, where resources are not allocated to the areas that need them the most. By using predictive analytics, the project can identify areas that are likely to face challenges in the future, and help decision-makers allocate resources more efficiently.
• Environmental challenges: Abu Dhabi Emirate faces a number of environmental challenges, such as rising temperatures, desertification, an

Technology Implemented

By leveraging Esri technology, the GeoAI-driven Predictive Analytics project in Abu Dhabi Emirate benefits from advanced spatial analysis and visualization capabilities, as well as scalable and efficient big data processing capabilities. Some of the key Esri technologies utilized include:
• ArcGIS Pro: ArcGIS Pro is Esri's flagship desktop GIS application that allows users to create, analyze, and share spatial data. This tool can be used to perform geospatial analysis, such as spatial statistics, which are crucial for understanding the relationships between different data sets in the project.
• ArcGIS Insights: ArcGIS Insights is a data analysis and visualization tool that enables users to perform advanced analytics on their data. It can be used to create interactive dashboards and reports that provide insights into trends and patterns in the data, which can be crucial for decision-making.
• Portal for ArcGIS: Portal for ArcGIS is a web-based platform that allows organizations to cr

Development Team Biography

Building a spatial statistics platform requires a multidisciplinary team with expertise in various fields. Here are some of the team requirements for building the intended spatial statistics platform:
• Data Scientists: Spatial statistics platforms require a lot of data analysis, modeling, and visualization. Data scientists are needed to analyze large amounts of spatial data, build models, and create visualizations that can help users understand spatial relationships.
• GIS Analysts: Geographic Information System (GIS) analysts are needed to create, manage, and analyze geographic data. They are responsible for organizing spatial data and creating maps that show the spatial distribution of data.
• GIS Specialists: GIS Specialists are needed to design and implement the platform's infrastructure (Highly Available Enterprise GIS).
• Statisticians: Statisticians are required to develop statistical models that can handle spatial data. They can help with the design of experiments, samplin