Geomatics CIT

** A free training in ArcGIS Utility network
تدريب مجانى على حلول ايزرى فى بناء و ادارة شبكات المرافق

A utility network is the main component users work with when hashtag utility and telecom networks in ArcGIS, providing a comprehensive framework of functionality for hashtag utility hashtag such as electric, gas, water, stormwater, wastewater, and telecommunications. It is designed to model all of the components that make up your system—such as wires, pipes, valves, zones, devices, and circuits—and allows you to build real-world behavior into the network features you model.

The below free training from ESRI consists of 10 modules on how to build and manage a Utility network.
Training link: https://learn.arcgis.com/en/paths/get-started-with-the-arcgis-utility-network/
تقدم ايزرى تدريب على بناء و ادارة شبكات المرافق باستخدام احدث تقنيات نظم المعلومات الجغرافية

هندسة البيانات
هندسة البيانات هي عملية إعداد البيانات للاستخدام و التحليل فى نظم المعلومات الجغرافية. و تكون عملية تكرارية تتضمن استخدام مجموعة متنوعة من الأدوات لتقييم البيانات وتمثيلها و العمل عليها.

𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠

Data engineering is the process of preparing your data for analysis. It is an iterative process that involves using a variety of tools to evaluate, visualize, and prepare data for GIS workflows.

𝘥𝘢𝘵𝘢 𝘦𝘯𝘨𝘪𝘯𝘦𝘦𝘳𝘪𝘯𝘨 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸:

*𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐧𝐠 𝐲𝐨𝐮𝐫 𝐝𝐚𝐭𝐚: Data engineering begins with evaluating the quality of data in the context of standards as completeness, accuracy, containing specific attribute values. Data evaluation techniques enable you to ask and quickly answer questions about your data and its quality.

*𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐢𝐧𝐠 𝐝𝐚𝐭𝐚 𝐰𝐢𝐭𝐡 𝐜𝐡𝐚𝐫𝐭𝐬: After data evaluation, the next step of the data engineering workflow is to visualize data. Charts tell a story about your data, and visualizing data makes it more comprehensible to the human brain. Charts can reveal characteristics such as patterns, trends, relationships, and structure in your data.

*𝐂𝐚𝐭𝐞𝐠𝐨𝐫𝐢𝐞𝐬 𝐨𝐟 𝐝𝐚𝐭𝐚 𝐩𝐫𝐞𝐩𝐚𝐫𝐚𝐭𝐢𝐨𝐧 𝐦𝐞𝐭𝐡𝐨𝐝𝐬: The last step of the data engineering workflow is to prepare your data for use. Evaluating the quality of your data through statistics and visualization with charts can reveal ways that you need to prepare your data for analysis. You have likely performed data engineering tasks to prepare data as projecting data into a different coordinate system, deleting or filling null values, and calculating new fields.