# Review of Web Mapping: Eras, Trends and Directions
## 4 Trends and Directions in Web Mapping
### Trends in Web Map Usage Components
#### 4.1.1 Web Mapping Data Trends  
In the early eras of web mapping, geospatial data and maps were generated by professionals (e.g., GIS digitizing, surveying, photogrammetry, and remote sensing) and the relatively smaller volumes of data captured were limited by low computer network bandwidths and infrastructure. However, data volumes increased immensely through subsequent eras (e.g., Collaborative, Digital Globe, Mobile- and Location-Based, etc.) along with the availability of online satellite imagery web services and the proliferation of users and sensors contributing content. Through the Cloud era, increasingly more data is being captured and stored somewhere online in the cloud and the increasing number of devices online via the IoT is beginning to contribute substantially more data, including location based data, to the web. According to a Cisco White Paper [101], the amount of data available on the Internet has increased to more than a staggering one billion websites by the end of 2016 [102] with traffic of more than 1 zettabyte (1000 exabytes) per year. The amount of traffic is expected to increase to 2.3 ZB per year by 2020. This includes the trend of data being moved from desktop environments into the cloud as well as the increasing volume of data being captured through mobile, sensor and social media/communication platforms. Of these data, a large proportion is geospatial imagery and features. For example, the amount of Google Earth imagery grew from an estimated 150 TB in 2006 to 3 petabytes in 2016, including aerial, satellite, 2D, 3D and historical imagery [103]. The proliferation of data [104] has led us into the big data era with all the consequent challenges [105]. Big data are characterized by huge volumes, in order of petabytes, huge variability, from unstructured to differently structured data, and huge velocity, having sensors continuously operating and measuring at high temporal frequencies. Some examples, which give some impression of their dimension but do not exhaust the list of possible data sources, are: imagery from unmanned aerial vehicles (UAVs), aircraft, helicopters, satellites, laser scanning, global navigation satellite  
Figure 5: Component dimensions influencing web map use ([100]; Adapted from [99]). system (GNSS) tracking, mobile mapping, geotagged web content, geolocated communication data, volunteered geographic information (VGI) and geolocated sensors (geoloT). The European Space Agency with its Sentinel Open Data [106] is complementing the huge amount of free and open available imagery, while some private companies are investing much effort in producing satellite data [107]. A relevant example of the latter case is Planet [108] which currently operates 149 satellites in orbit, with the ability to image all of Earth's landmass every day. Even if we consider only the optical satellite imagery, the Sentinel 2 produces 2 terabytes of data per day, while PlanetScope generates 60 terabytes per day [109]. On the web, not only the quantity of data but also the quality and accessibility to data are influential factors [110]. The quality of data determines whether it is sufficient for purpose, relevant and fit for use. The masses of data must be turned into useful and relevant information from which decision-making occurs. Apparently, the cloud represents the only realistic possibility of dealing with such a data flood and cloud web mapping is rapidly emerging. As illustrated in the Data, Information, Knowledge, Intelligence and Wisdom (DIKIW) pyramid in Figure 6, to deliver that useful and relevant information, there must be a discovery and integration of all necessary information resources. There needs to be access to appropriate geo-processing services to manipulate and filter the data to suit user or application profiles and contexts.