For many years, collaboration has been a key cornerstone in the success of efforts achieved by the geospatial community.  When paired with governance, collaborative efforts often lead to sustainability and have the effect of broadening the benefits that can be achieved.  The following text shares how the geospatial community uses collaboration and governance as tools to achieve benefits across the community.  Case studies are provided to illustrate the process and the outcomes achieved. 

Maps are powerful storytellers. Maps have a long history combining spatial relations with cartographic language to locate, analyze, ground, and express stories told across time and space. Today, “story maps” are increasingly visible in cartography, GIScience, digital humanities, data visualization, and journalism due to the volume of available data and increasingly accessible mapping tools. Perhaps, most importantly, maps present world views and much larger (often hidden) stories or “meta-narratives.” These underlying stories often emerge from dominant perspectives that are deeply informed by power structures like racism, patriarchy, ableism, etc. and further generate uneven geographies. Attention to power in narrative and storytelling reveals and gives voice to alternative storylines and perspectives that can be woven together across time and space. In this entry, I introduce multiple conceptualizations of maps and stories from cartography and data journalism to feminist mapping, Black geographies, and decolonial mapping to illustrate the power of narrative and storytelling in mapping. I argue that understanding the power of narrative and storytelling in mapping is an essential skillset for students and professionals alike.

A multispectral image comprises a set of co-registered images, each of which captures the spatially varying brightness of a scene in a specific spectral band, or electromagnetic wavelength region. An image is structured as a raster, or grid, of pixels. Multispectral images are used as a visual backdrop for other GIS layers, to provide information that is manually interpreted from images, or to generate automatically-derived thematic layers, for example through classification. The scale of multispectral images has spatial, spectral, radiometric and temporal components. Each component of scale has two aspects, extent (or coverage), and grain (or resolution). The brightness variations of an image are determined by factors that include (1) illumination variations and effects of the atmosphere, (2) spectral properties of materials in the scene (particularly reflectance, but also, depending on the wavelength, emittance), (3) spectral bands of the sensor, and (4) display options, such as the contrast stretch, which affect the visualization of the image. This topic review focuses primarily on optical remote sensing in the visible, near infrared and shortwave infrared parts of the electromagnetic spectrum, with an emphasis on satellite imagery.  

A network is a widely used term with different definitions and methodologies depending on the applications. In GIS, a network refers to an arrangement of elements (i.e., nodes, links) and information on their connections and interactions. There are two types of networks: physical and logical. While a physical network has tangible objects (e.g., road segments), a logical network represents logical connections among nodes and links. A network can be represented with a mathematical notion called graph theory. Different network components are utilized to describe characteristics of a network including loops, walks, paths, circuits, and parallel edges. Network data are commonly organized in a vector format with network topology, specifically connectivity among nodes and links, whereas raster data can be also utilized for a least-cost problem over continuous space. Network data is utilized in a wide range of network analyses, including the classic shortest path problem.