Issue link: https://read.dmtmag.com/i/50311
Innovation Geospatial systems leveraging real-time sensor data are well suited as the core decision-support tools to integrate traditional and environmental factors. GIS has long been invaluable for socioeconomic and environmental analysis, and it can be leveraged for the analytics required to support the triple bottom line. Many organizations are moving to integrate business analytics with geospatial systems for an integrated picture. Sensor-based platforms have some key benefits. Data collection has historically been prohibitively expensive, requiring considerable resources for manual field collection or costly air flight or satellite time. Sensors enable data collection at a fraction of the cost; for a simple platform, the cost can be as little as 10 cents per sensor. Of course, sensors are mass deployed, and costs therefore aggregate. And the piper must be paid; a field trip to deploy them still is required. But after sensors are deployed, they require little maintenance and collect far more data than could a single field trip. From a total cost-of-ownership per- spective, for the types of data for which sensors are suited, they're far more cost-effective than manual field collection. The concept of a sensor web emerged in the late 1990s at NASA, consisting of an interoperable web of interconnected, mass-deployed sensors. The data collected from these distributed sensors can be more fine-grained than can be manually collected, making analytical results more accurate. The interconnected nature of the sensor web means that sensor data can be combined with nearby measurements to interpolate and filter outliers. To paraphrase Waldo Tobler, a measurement of a variable is related to every other measurement of the same variable, but nearby measurements are much more interrelated. Enter spatial autocorrelation. Geospatial expertise is required to effectively analyze these data as well as effectively integrate them into existing workflows and information streams. Sensors Everywhere Sensors in many forms have become ubiquitous in society. One of the most obvious sensor platforms— producing data that vendors such as Google are clambering for—is the always-on, constantly recording device carried by many people virtually everywhere. Smartphones are constantly recording users' loca- tions as well as what they're interested in. Search for a pizza, and a smartphone indicates which pizza places are within five miles. This is pos- sible because the phone is a sensor, and it is spinning up an entirely new opportunity for revenue. Location- based services are predicated on knowing what we want, when and where we want it. By recording human activities, an important data stream is provided to advertisers that allows them to tailor messages and improve their bottom line. Sensors also are driving improvements in civil engineering. Transportation planning records traffic counts at locations where volume is known to be high, congestion problems are an issue or accidents are frequent. These sensors also can detect vehicle speeds to determine if vehicles are following posted speed limits or whether this may be a factor leading to the frequent accidents. Sensors also monitor water quality, capturing levels lSensors are driving improvements in transportation planning and water quality, and they're fundamental to improving power quality and reliability in the Smart Grid. 28 GEO W ORLD / D E C EMBER 2O11 of chemicals or pesticides for subsequent analysis and treatment options, or for analysis of point-source contamination. The granularity of such sensor data allows for sensitivity analyses and simulation, so plan- ners can assess the impact of changes prior to mak- ing costly investments. The frequency with which data are collected allows these investment outcomes to be monitored in real time and fine-tuned to maximize