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Extend your ArcGIS expertise by unlocking the world of Python programming. A fully hands-on guide that takes you through exercise after exercise using real data and real problems. Key Features Learn the core components of the two Python modules for ArcGIS: ArcPy and ArcGIS API for Python Use ArcPy, pandas, NumPy, and ArcGIS in ArcGIS Pro Notebooks to manage and analyze geospatial data at scale Integrate with ArcGIS Online using Python to publish and manage data Book Description Integrating Python into your day-to-day ArcGIS work is highly recommended when dealing with large amounts of geospatial data. Python for ArcGIS Pro aims to help you get your work done faster, with greater repeatability and higher confidence in your results. Starting from programming basics and building in complexity, two experienced ArcGIS professionals-turned-Python programmers teach you how to incorporate scripting at each step: automating the production of maps for print, managing data between ArcGIS Pro and ArcGIS Online, creating custom script tools for sharing, and then running data analysis and visualization on top of the ArcGIS geospatial library, all using Python. You'll use ArcGIS Pro Notebooks to explore and analyze geospatial data, and write data engineering scripts to manage ongoing data processing and data transfers. This exercise-based book also includes three rich real-world case studies, giving you an opportunity to apply and extend the concepts you studied earlier. Irrespective of your expertise level with Esri software or the Python language, you'll benefit from this book's hands-on approach, which takes you through the major uses of Python for ArcGIS Pro to boost your ArcGIS productivity. What you will learn Automate map production to make and edit maps at scale, cutting down on repetitive tasks Publish map layer data to ArcGIS Online Automate data updates using the ArcPy Data Access module and cursors Turn your scripts into script tools for ArcGIS Pro Learn how to manage data on ArcGIS Online Query, edit, and append to feature layers and create symbology with renderers and colorizers Apply pandas and NumPy to raster and vector analysis Learn new tricks to manage data for entire cities or large companies Who this book is for This book is ideal for anyone looking to add Python to their ArcGIS Pro workflows, even if you have no prior experience with programming. This includes ArcGIS professionals, intermediate ArcGIS Pro users, ArcGIS Pro power users, students, and people who want to move from being a GIS Technician to GIS Analyst; GIS Analyst to GIS Programmer; or GIS Developer/Programmer to a GIS Architect. Basic familiarity with geospatial/GIS syntax, ArcGIS, and data science (pandas) is helpful, though not necessary.

Geographic information systems. --- Graphical user interfaces (Computer systems) --- Python (Computer program language) --- ArcGIS.

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Data Science (DS) is gaining significant importance in the decision process due to a mix of various areas, including Computer Science, Machine Learning, Math and Statistics, domain/business knowledge, software development, and traditional research. In the business field, DS's application allows using scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data to support the decision process. After collecting the data, it is crucial to discover the knowledge. In this step, Knowledge Discovery (KD) tasks are used to create knowledge from structured and unstructured sources (e.g., text, data, and images). The output needs to be in a readable and interpretable format. It must represent knowledge in a manner that facilitates inferencing. KD is applied in several areas, such as education, health, accounting, energy, and public administration. This book includes fourteen excellent articles which discuss this trending topic and present innovative solutions to show the importance of Data Science and Knowledge Discovery to researchers, managers, industry, society, and other communities. The chapters address several topics like Data mining, Deep Learning, Data Visualization and Analytics, Semantic data, Geospatial and Spatio-Temporal Data, Data Augmentation and Text Mining.

crisis reporting --- chatbots --- journalists --- news media --- COVID-19 --- textbook research --- digital humanities --- digital infrastructures --- data analysis --- content base image retrieval --- semantic information retrieval --- deep features --- multimedia document retrieval --- data science --- open government data --- governance and social institutions --- economic determinants of open data --- geoinformation technology --- fractal dimension --- territorial road network --- box-counting framework --- script Python --- ArcGIS --- internet of things --- LoRaWAN --- ICT --- The Things Network --- ESP32 microcontroller --- decision systems --- rule based systems --- databases --- rough sets --- prediction by partial matching --- spatio-temporal --- activity recognition --- smart homes --- artificial intelligence --- automation --- e-commerce --- machine learning --- big data --- customer relationship management (CRM) --- distracted driving --- driving behavior --- driving operation area --- data augmentation --- feature extraction --- authorship --- text mining --- attribution --- neural networks --- deep learning --- forensic intelligence --- dashboard --- WebGIS --- data analytics --- SARS-CoV-2 --- Big Data --- Web Intelligence --- media analytics --- social sciences --- humanities --- linked open data --- adaptation process --- interdisciplinary research --- media criticism --- classification --- information systems --- public health --- data mining --- ioCOVID19 --- n/a

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This special issue aims to contribute to the climate actions which called for the need to address Greenhouse Gas (GHG) emissions, keeping global warming to well below 2°C through various means, including accelerating renewables, clean fuels, and clean technologies into the entire energy system. As long as fossil fuels (coal, gas and oil) are still used in the foreseeable future, it is vital to ensure that these fossil fuels are used cleanly through abated technologies. Financing the clean and energy transition technologies is vital to ensure the smooth transition towards net zero emission by 2050 or beyond. The lack of long‐term financing, the low rate of return, the existence of various risks, and the lack of capacity of market players are major challenges to developing sustainable energy systems.This special collected 17 high-quality empirical studies that assess the challenges for developing secure and sustainable energy systems and provide practical policy recommendations. The editors of this special issue wish to thank the Economic Research Institute for ASEAN and East Asia (ERIA) for funding several papers that were published in this special issue.

Research & information: general --- Physics --- industrial energy intensity --- pollution emission intensity --- quantile DID method --- Beijing–Tianjin–Hebei coordinated development --- China --- environmental Kuznets curve --- CO2 emission --- energy efficiency --- economic growth --- panel ARDL --- DEA --- energy transition --- renewables --- hydrogen --- fossil fuels --- emissions --- FDIA --- blockchain --- data exchanging --- under-operating agents --- ISO --- electricity market --- Saudi Arabia --- energy sustainability --- world energy trilemma index --- Bayesian Belief Network --- green technology --- sustainability --- climate change --- Southeast Asia --- energy policy --- high-efficiency --- low-emission --- carbon dioxide emissions --- carbon pricing --- subcritical --- desulphurization --- denitrification --- cost–benefit analysis --- levelized cost of electricity --- energy supply security --- energy dependence --- energy diversity --- business as usual (BAU) --- Alternative Policy Scenarios (APSs) --- clean technologies --- and resiliency --- multi plant firms --- environmental assessment --- local-global performance --- wind energy --- power trade --- counterfactual scenario --- ASEAN --- natural gas --- multi-objective --- goal programming --- optimization --- allocation --- connectivity --- energy infrastructure --- Mekong Subregion --- green bonds --- post-COVID-19 era --- Asia and the Pacific --- green finance --- sustainable development --- thermal energy storage (TES) --- latent heat thermal energy storage (LHTES) --- circular economy --- environmental sustainability --- life cycle assessment (LCA) --- physico-chemical characterization --- Coats–Redfern model --- flammability --- integral model --- iso-conversional --- wind farm site selection --- multi-criteria decision-making system --- Analytic Hierarchy Process --- Semnan province --- ArcGIS