Chapter 5 focuses on caching and on-premises in WebGIS. This focuses on making a secure and private WebGIS using ArcGIS enterprise. This chapter explains tile caching, which enhances map performance by rendering tiles at different scales rather than just drawing maps when it is requested. By caching the maps being used, they load much faster. This is especially the case when large datasets are being used. I found this to be a super cool feature and would help not crash your personal device. This is helpful in the web version because a large desktop computer that is more efficient is not the device being handled. Although the performance is much faster there are still tradeoffs. This tradeoff being that it gets harder to update data dynamically. One question I have after this chapter is how organizations decided to used cached data rather than real time data that we previously learned about. On-premises is when the GIS infrastructure is hosted on your own private server. This additionally adds to the map being more secure and customizable. The catch to this feature is that it requires more knowledge on IT compared to the typical GIS server. These are helpful for organizations to create private maps and have full control over over the infrastructure. This also can assist companies in following their strict regulations for the GIS maps. The tutorial explains how to create a portal and compare raster and vector tiles on top and side by side. This chapter was much more confusing compared to the last few weeks, but it was explained well throughout the textbook.
An application that could be created using this chapter is a map of a local park. This map would include all the features, including where each individual tree, bench, bush, etc is located. By using the tile caching explained in this chapter the map would not need to be re-rendered each time a user zooms in and out. This is very similar to how Google Maps works, but would be smaller and have more specific data for the location being mapped.
Chapter 6 is focused on spatiotemporal data and real-time GIS. Spatiotemporal data is data that combines time and location. This allows for users to analyze how things change over time. With how long the term is, I was pleasantly surprised with how simple the concept of it was to grasp. Real-time GIS systems create live data streams. It explains IoT, which is a network of things (a variety of sensors on things such as cars, planes, biochips, and security cameras). IoT is utilized for smart cities, infrastructure management, environmental quality monitoring, management, and precise agriculture. This also includes connected cars, health, and smart homes. This was interesting to see how random objects that many people have acquire data and transform it into useful information. A specific example the book provided was extensive routing through smart cars. This led me wondering how much of our world and technology we use is a part of GIS. However, like the last chapter there are additionally many challenges with real-time data. This include managing the large volume of data, fast processing and visualization, and making sure the data continues to stay accurate. The tutorials of this chapter take you through finding data and fitting it into the functional requirements they want the map to include. It also includes how to create a dashboard app based off the web map created.
An application idea for this chapter is tracking deforestation with spatiotemporal data. This could be done in an urban area, for example Columbus due to it being the closest large city. The real-time data could be utilized to alert city planners when too many trees are being removed rather than replanted. This can ensure further construction in the area considers parks and small gardens within the city to keep the nature alive in the area.