Leafmap is a Python package for interactive mapping and geospatial analysis with minimal coding in a Jupyter environment. It is built upon several open-source packages, such as ipyleaflet and kepler.gl (for creating interactive maps), WhiteboxTools (for analyzing geospatial data), and ipywidgets (for designing interactive graphical user interface). Leafmap provides many convenient functions for loading and visualizing geospatial data with only one line of code. Users can also use the interactive user interface to load geospatial data without coding. Anyone with a web browser and Internet connection can use leafmap to perform geospatial analysis and data visualization in the cloud with minimal coding. This workshop will introduce the key features of leafmap for interactive mapping and geospatial analysis in a Jupyter environment. Attendees will learn how to leverage open-source Python packages and free cloud computing platforms for geospatial analysis and data visualization.
{"title":"Interactive mapping and geospatial analysis with Leafmap and Jupyter","authors":"Qiusheng Wu","doi":"10.1145/3486189.3490015","DOIUrl":"https://doi.org/10.1145/3486189.3490015","url":null,"abstract":"Leafmap is a Python package for interactive mapping and geospatial analysis with minimal coding in a Jupyter environment. It is built upon several open-source packages, such as ipyleaflet and kepler.gl (for creating interactive maps), WhiteboxTools (for analyzing geospatial data), and ipywidgets (for designing interactive graphical user interface). Leafmap provides many convenient functions for loading and visualizing geospatial data with only one line of code. Users can also use the interactive user interface to load geospatial data without coding. Anyone with a web browser and Internet connection can use leafmap to perform geospatial analysis and data visualization in the cloud with minimal coding. This workshop will introduce the key features of leafmap for interactive mapping and geospatial analysis in a Jupyter environment. Attendees will learn how to leverage open-source Python packages and free cloud computing platforms for geospatial analysis and data visualization.","PeriodicalId":258964,"journal":{"name":"Proceedings of the 3rd ACM SIGSPATIAL International Workshop on APIs and Libraries for Geospatial Data Science","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124490278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E. Zimányi, M. Sakr, Mohamed S. Bakli, Maxime Schoemans, Dimitris Tsesmelis, Robin Choquet
MobilityDB is an open source moving object database. It extends PostgreSQL and PostGIS with types and operations for managing continuous geospatial trajectories. This hand-on tutorial will introduce the attendees to: (1) trajectory data management in MobilityDB, (2) visualization of moving object data in QGIS, and (3) distributed spatiotemporal query processing using MobilityDB. All the tutorial queries will be in SQL.
{"title":"MobilityDB: hands on tutorial on managing and visualizing geospatial trajectories in SQL","authors":"E. Zimányi, M. Sakr, Mohamed S. Bakli, Maxime Schoemans, Dimitris Tsesmelis, Robin Choquet","doi":"10.1145/3486189.3490016","DOIUrl":"https://doi.org/10.1145/3486189.3490016","url":null,"abstract":"MobilityDB is an open source moving object database. It extends PostgreSQL and PostGIS with types and operations for managing continuous geospatial trajectories. This hand-on tutorial will introduce the attendees to: (1) trajectory data management in MobilityDB, (2) visualization of moving object data in QGIS, and (3) distributed spatiotemporal query processing using MobilityDB. All the tutorial queries will be in SQL.","PeriodicalId":258964,"journal":{"name":"Proceedings of the 3rd ACM SIGSPATIAL International Workshop on APIs and Libraries for Geospatial Data Science","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123792639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Akil Sevim, Mehnaz Tabassum Mahin, Tin Vu, Ian Maxon, A. Eldawy, M. Carey, V. Tsotras
There is immense potential with spatial data, which is even more significant when combined with temporal or textual features, or both. However, it is expensive to store and analyze spatial data, and it is even more challenging with the combined features due to the additional optimization requirements. There are numerous successful solutions for big spatial data management, but they do not well support non-spatial operations. The options for the systems are even smaller for the open sources systems, and there are not a handful of options that provide good coverage of care about the spatial and non-spatial operations. This tutorial introduces Apache AsterixDB, a scalable open-source Big Data Management System, which supports standard vector spatial data types as well as non-spatial attributes, e.g., numerical, temporal, and textual. The participants will get hands-on experience on how Apache AsterixDB can efficiently process complex SQL++ queries that require multiple special handling by a team from its kitchen.
{"title":"A brief introduction to geospatial big data analytics with apache AsterixDB","authors":"Akil Sevim, Mehnaz Tabassum Mahin, Tin Vu, Ian Maxon, A. Eldawy, M. Carey, V. Tsotras","doi":"10.1145/3486189.3490018","DOIUrl":"https://doi.org/10.1145/3486189.3490018","url":null,"abstract":"There is immense potential with spatial data, which is even more significant when combined with temporal or textual features, or both. However, it is expensive to store and analyze spatial data, and it is even more challenging with the combined features due to the additional optimization requirements. There are numerous successful solutions for big spatial data management, but they do not well support non-spatial operations. The options for the systems are even smaller for the open sources systems, and there are not a handful of options that provide good coverage of care about the spatial and non-spatial operations. This tutorial introduces Apache AsterixDB, a scalable open-source Big Data Management System, which supports standard vector spatial data types as well as non-spatial attributes, e.g., numerical, temporal, and textual. The participants will get hands-on experience on how Apache AsterixDB can efficiently process complex SQL++ queries that require multiple special handling by a team from its kitchen.","PeriodicalId":258964,"journal":{"name":"Proceedings of the 3rd ACM SIGSPATIAL International Workshop on APIs and Libraries for Geospatial Data Science","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127258231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The concept of coverages, generally grasping multi-dimensional space/time varying phenomena, has received impressive attention among service implementers and operators. The biggest reason for this success is that coverages conveniently model datacubes, specifically in combination with powerful APIs such as the Web Coverage Service (WCS) with its datacube analytics language, Web Coverage Processing Service (WCPS). OGC, ISO, and EU INSPIRE capitalize on coverages, and leading tools implement them. In this tutorial, we first briefly recapitulate the coverage model and simple access with WCS Core, address the status of OAPI-Coverages, and then proceed to datacube analytics with WPCS. Practical demos based on the EarthServer datacube federation serve to illustrate; participants can recap and modify most of the demos. Altogether, this workshop constitutes a unique opportunity for getting up to speed with coverages and datacubes.
覆盖的概念，通常是对多维空间/时间变化现象的把握，已经受到了服务实现者和运营者的极大关注。这种成功的最大原因是覆盖方便地建模数据集，特别是与强大的api(如Web Coverage Service (WCS)及其数据集分析语言Web Coverage Processing Service (WCPS))相结合。OGC、ISO和EU INSPIRE利用覆盖范围，并使用领先的工具实现它们。在本教程中，我们首先简要概述覆盖模型和使用WCS Core的简单访问，讨论OAPI-Coverages的状态，然后继续使用WPCS进行数据分析。基于EarthServer数据集联盟的实际演示可以用来说明;参与者可以重述和修改大部分演示。总之，本次研讨会为快速了解覆盖率和数据集提供了一个独特的机会。
{"title":"The OGC/ISO coverage API standards: Heavy-lifting APIs for massive multi-dimensional data","authors":"P. Baumann","doi":"10.1145/3486189.3490019","DOIUrl":"https://doi.org/10.1145/3486189.3490019","url":null,"abstract":"The concept of coverages, generally grasping multi-dimensional space/time varying phenomena, has received impressive attention among service implementers and operators. The biggest reason for this success is that coverages conveniently model datacubes, specifically in combination with powerful APIs such as the Web Coverage Service (WCS) with its datacube analytics language, Web Coverage Processing Service (WCPS). OGC, ISO, and EU INSPIRE capitalize on coverages, and leading tools implement them. In this tutorial, we first briefly recapitulate the coverage model and simple access with WCS Core, address the status of OAPI-Coverages, and then proceed to datacube analytics with WPCS. Practical demos based on the EarthServer datacube federation serve to illustrate; participants can recap and modify most of the demos. Altogether, this workshop constitutes a unique opportunity for getting up to speed with coverages and datacubes.","PeriodicalId":258964,"journal":{"name":"Proceedings of the 3rd ACM SIGSPATIAL International Workshop on APIs and Libraries for Geospatial Data Science","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128951837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anand Padmanabhan, Ximo Ziao, Rebecca Vandewalle, Furqan Baig, Alexander Michels, Zhiyu Li, Shaowen Wang
Geospatial research and education have become increasingly dependent on cyberGIS to tackle computation and data challenges. However, the use of advanced cyberinfrastructure resources for geospatial research and education is extremely challenging due to both high learning curve for users and high software development and integration costs for developers, due to limited availability of middleware tools available to make such resources easily accessible. This tutorial describes CyberGIS-Compute as a middleware framework that addresses these challenges and provides access to high-performance resources through simple easy to use interfaces. The CyberGIS-Compute framework provides an easy to use application interface and a Python SDK to provide access to CyberGIS capabilities, allowing geospatial applications to easily scale and employ advanced cyberinfrastructure resources. In this tutorial, we will first start with the basics of CyberGIS-Jupyter and CyberGIS-Compute, then introduce the Python SDK for CyberGIS-Compute with a simple Hello World example. Then, we will take multiple real-world geospatial applications use-cases like spatial accessibility and wildfire evacuation simulation using agent based modeling. We will also provide pointers on how to contribute applications to the CyberGIS-Compute framework.
{"title":"CyberGIS-compute for enabling computationally intensive geospatial research","authors":"Anand Padmanabhan, Ximo Ziao, Rebecca Vandewalle, Furqan Baig, Alexander Michels, Zhiyu Li, Shaowen Wang","doi":"10.1145/3486189.3490017","DOIUrl":"https://doi.org/10.1145/3486189.3490017","url":null,"abstract":"Geospatial research and education have become increasingly dependent on cyberGIS to tackle computation and data challenges. However, the use of advanced cyberinfrastructure resources for geospatial research and education is extremely challenging due to both high learning curve for users and high software development and integration costs for developers, due to limited availability of middleware tools available to make such resources easily accessible. This tutorial describes CyberGIS-Compute as a middleware framework that addresses these challenges and provides access to high-performance resources through simple easy to use interfaces. The CyberGIS-Compute framework provides an easy to use application interface and a Python SDK to provide access to CyberGIS capabilities, allowing geospatial applications to easily scale and employ advanced cyberinfrastructure resources. In this tutorial, we will first start with the basics of CyberGIS-Jupyter and CyberGIS-Compute, then introduce the Python SDK for CyberGIS-Compute with a simple Hello World example. Then, we will take multiple real-world geospatial applications use-cases like spatial accessibility and wildfire evacuation simulation using agent based modeling. We will also provide pointers on how to contribute applications to the CyberGIS-Compute framework.","PeriodicalId":258964,"journal":{"name":"Proceedings of the 3rd ACM SIGSPATIAL International Workshop on APIs and Libraries for Geospatial Data Science","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133376249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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