2022 QUARTER 04

A B C D E F G H I J K L M N O P R S T U V W
PD-31 - PySAL and Spatial Statistics Libraries

As spatial statistics are essential to the geographical inquiry, accessible and flexible software offering relevant functionalities is highly desired. Python Spatial Analysis Library (PySAL) represents an endeavor towards this end. It is an open-source python library and ecosystem hosting a wide array of spatial statistical and visualization methods. Since its first public release in 2010, PySAL has been applied to address various research questions, used as teaching materials for pedagogical purposes in regular classes and conference workshops serving a wide audience, and integrated into general GIS software such as ArcGIS and QGIS. This entry first gives an overview of the history and new development with PySAL. This is followed by a discussion of PySAL’s new hierarchical structure, and two different modes of accessing PySAL’s functionalities to perform various spatial statistical tasks, including exploratory spatial data analysis, spatial regression, and geovisualization. Next, a discussion is provided on how to find and utilize useful materials for studying and using spatial statistical functions from PySAL and how to get involved with the PySAL community as a user and prospective developer. The entry ends with a brief discussion of future development with PySAL.

PD-11 - Python for GIS

Figure 1. PySAL within QGIS Processing Toolbox: Hot-spot analysis of Homicide Rates in Southern US Counties.

 

Python is a popular language for geospatial programming and application development. This entry provides an overview of the different development modes that can be adopted for GIS programming with Python and discusses the history of Python adoption in the GIS community. The different layers of the geospatial development stack in Python are examined giving the reader an understanding of the breadth that Python offers to the GIS developer. Future developments and broader issues related to interoperability and programming ecosystems are identified.

PD-15 - R for Geospatial Analysis and Mapping

R is a programming language as well as a computing environment to perform a wide variety of data analysis, statistics, and visualization. One of the reasons for the popularity of R is that it embraces open, transparent scholarship and reproducible research. It is possible to combine content and code in one document, so data, analysis, and graphs are tied together into one narrative, which can be shared with others to recreate analyses and reevaluate interpretations. Different from tools like ArcGIS or QGIS that are specifically built for spatial data, GIS functionality is just one of many things R offers. And while users of dedicated GIS tools typically interact with the software via a point-and-click graphical interface, R requires command-line scripting. Many R users today rely on RStudio, an integrated development environment (IDE) that facilitates the writing of R code and comes with a series of convenient features, like integrated help, data viewer, code completion, and syntax coloring. By using R Markdown, a particular flavor of the Markdown language, RStudio also makes it particularly easy to create documents that embed and execute R code snippets within a text and to render both, static documents (like PDF), as well as interactive html pages, a feature particularly useful for exploratory GIS work and mapping.

CV-20 - Raster Formats and Sources

Raster data is commonly used by cartographers in concert with vector data. Choice of raster file format is important when using raster data or producing raster output from vector data. Raster formats are designed for specific purposes and have limitations in color representation and data loss. The simplest raster formats are just a single two-dimensional array of pixels, where multi-band raster datasets use additional data values to represent color or other data. The article covers considerations for the intended use of raster formats. Formats and resolutions appropriate for the web may not be appropriate for print or higher resolution devices. Several types of raster sources are available including single band measures, imagery, and existing raster maps or basemaps. The future of raster will evolve as more formats, sources, and computational improvements are made.

DM-87 - Raster resampling
  • Evaluate methods used by contemporary GIS software to resample raster data on-the-fly during display
  • Select appropriate interpolation techniques to resample particular types of values in raster data (e.g., nominal using nearest neighbor)
  • Resample multiple raster data sets to a single resolution to enable overlay
  • Resample raster data sets (e.g., terrain, satellite imagery) to a resolution appropriate for a map of a particular scale
  • Discuss the consequences of increasing and decreasing resolution
PD-20 - Real-time GIS Programming and Geocomputation

Streaming data generated continuously from sensor networks, mobile devices, social media platforms and other edge devices have posed significant challenges to existing computing platforms for achieving both high throughput and low latency data processing in addition to scalable computing. This entry introduces a real-time computing and programming platform for time-critical GIS (Geographic Information System) applications. In this platform, advanced streaming data processing software, such as Apache Kafka and Spark Streaming, are integrated to enable data analytics in real-time. This computing platform can also be extended to integrate GeoAI (Geospatial Artificial Intelligence) based machine learning models to leverage both historical and streaming data to achieve real-time prediction and intelligent geospatial analytics. Two real-time geospatial applications in terms of flood simulation and climate data visualization are introduced to demonstrate how real-time programming and computing can help tackle real-world problems with important societal impacts.

DM-03 - Relational DBMS and their Spatial Extensions

The relational Database Management System (DBMS) is widely used in modern business systems. Entities and relationships from a data model are presented as relational tables. To store data in a relational database, a relation schema should be defined to specify the design and structure of relations. The schema design generally uses database normalization to reduce data redundancy and maintain data integrity. Users can retrieve and manage data in a relational database using Structured Query Language (SQL). To make spatial data fit the relational model, spatial vector geometry or raster data type can be customized by extending basic data types in relational databases. This further helps derive the so-called spatial object-relational DBMS, by manipulating vector geometry and/or raster data types as spatial objects using SQL queries. The performance of queries is improved by adding spatial indexes in relational databases.

FC-09 - Relationships Between Space and Time

Relationships between space and time evoke fundamental questions in the sciences and humanities. Many disciplines, including GIScience, consider that space and time extend in separate dimensions, are interchangeable, and form co-equal parts of a larger thing called space-time.  Our perception of how time operates in relation to space or vice verso influences how we represent space, time, and their relationships in GIS. The chosen representation, furthermore, predisposes what questions we can ask and what approaches we can take for analysis and modeling. There are many ways to think about space, time, and their relationships in GIScience. This article synthesizes five broad categories: (1) Time is independent of space but relates to space by movement and change; (2) Time collaborates with space to probe relationships, explanations, and predictions; (3) Time is spatially constructed and constrained; (4) Time and space are mutually inferable; and (5) Time and space are integrated and co-equal in the formation of flows, events, and processes. Concepts, constructs, or law-like statements arise in each of the categories as examples of how space, time, and their relationships help frame scientific inquiries in GIScience and beyond.

DC-26 - Remote Sensing Platforms

Remote sensing means acquiring and measuring information about an object or phenomenon via a device that is not in physical or direct contact with what is being studied (Colwell, 1983).To collect remotely sensed data, a platform – an instrument that carries a remote sensing sensor – is deployed. From the mid 1800’s to the early 1900’s, various platforms such as balloons, kites, and pigeons carried mounted cameras to collect visual data of the world below. Today, aircraft (both manned and unmanned) and satellites collect the majority of remotely sensed data. The sensors typically deployed on these platforms include film and digital cameras, light-detection and ranging (LiDAR) systems, synthetic aperture radar (SAR) systems, and multi-spectral and hyper-spectral scanners. Many of these instruments can be mounted on land-based platforms, such as vans, trucks, tractors, and tanks. In this chapter, we will explore the different types of platforms and their resulting remote sensing applications.

CV-18 - Representing Uncertainty

Using geospatial data involves numerous uncertainties stemming from various sources such as inaccurate or erroneous measurements, inherent ambiguity of the described phenomena, or subjectivity of human interpretation. If the uncertain nature of the data is not represented, ill-informed interpretations and decisions can be the consequence. Accordingly, there has been significant research activity describing and visualizing uncertainty in data rather than ignoring it. Multiple typologies have been proposed to identify and quantify relevant types of uncertainty and a multitude of techniques to visualize uncertainty have been developed. However, the use of such techniques in practice is still rare because standardized methods and guidelines are few and largely untested. This contribution provides an introduction to the conceptualization and representation of uncertainty in geospatial data, focusing on strategies for the selection of suitable representation and visualization techniques.

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