Getting Started With IoT Geospatial Data
Every day the number of devices connected to the internet keeps growing. As more smart devices and advanced sensors come online this number will keep rising exponentially. Some estimates suggest that by 2025 there will be about 41.6 billion connected devices, which can generate 79.4 zettabytes (ZB) of data [Source: IDC]. Some of these will be sensors and devices that can provide geographic data and other information that can help businesses perform geospatial analytics and visualizations using graphs and charts or by plotting the data on interactive maps. There’s significant opportunity in such data. For example, businesses can use geospatial analytics to increase the reach of their products, obtain meaningful insights to their operations, better assist their customers, provide interesting features as enhancements to their products based on regional preferences, improve product quality and reliability, and so on. The challenge is processing IoT geospatial data in a way that extracts maximum value. But with a few best practices, you can start using IoT geospatial data to deliver meaningful insights for your business.
Industry benefits of IoT geospatial data
Many industries – such as health care, insurance to manufacturing, construction, tourism, travel and hospitality, and many more – are starting to use geospatial analysis on IoT data. Here are a few examples of how specific industries can benefit.
Airlines can use the sensors in airplanes to understand a plane’s exact location, better prepare for delays, divert flights in case of weather or airport issues, better plan the routes to maximize usage and reduce costs, and more. Geographical data from various sources can also provide real-time statistics on popular destinations, identify regions with the most delays, or even customize experiences based on specific regions.
Manufacturing companies can use real-time data with geospatial components to better understand demands based on regions and use those insights to reduce logistic costs and improve delivery. For example, an organization that manufactures vehicles can use geospatial data obtained from sensors in their vehicles to identify the right regions to establish service centers to better serve their customers.
Retail businesses can use geospatial data to analyze which products sell better based on specific regions and channelize their marketing in those regions based on popular demand. This insight can also help retail companies understand the likes and dislikes of the market in that region and predict demand to manage inventory.
Healthcare can be improved using geospatial data from sensors in several ways. Geospatial data can help identify the exact location of a patient and thereby help direct quick medical care. It can also be used to identify and prepare for epidemics, track staff-to-bed proximity to enhance patient care, and more.
Agriculture can benefit from weather pattern analysis, insights that help farmers identify the right crop to grow in specific regions, or preparation for disasters like droughts or floods. Geospatial data can help agricultural businesses protect the yield, create more efficient distribution networks to maximize profits, and improve logistic capabilities to avoid food waste.
Major challenges for processing IoT geospatial data
Companies looking to take advantage of their geospatial and IoT data typically want to process this data in real time or near real time. Doing so allows them to generate and update charts, reports, and maps as quickly as possible without having to incur huge infrastructure costs or the operation costs of maintaining a dedicated team. At the same time, they need to be able to make changes to these processes with relative ease as more data types or logic need to be incorporated. Here are some of the specific challenges you need to overcome when you’re working with this type of data:
- Volume: Geospatial data analytics can include huge amounts of data that need to be processed in order to retrieve useful information from them. You need to process huge volumes of data in shortest possible amount of time and perform complex calculations on them while keeping the costs for processing reasonable.
- Variety: Geospatial data is available in a variety of formats. Depending on the nature of the data, you may need to define a process that reads in data from all these different source formats, cleans the data to extract only relevant information so it can be consumed by open-source tools and libraries, and perform conversions if needed to simplify the analytics and maintain a simple logical layer.
- Storage and retrieval: Efficiently storing and retrieving geospatial data can be another challenge, but it’s essential to reducing the cost involved in performing complex operations. Indexing geospatial data is also vital, since it helps simplify operations like constructing hierarchical data types out of simpler data structures or filtering data at an earlier stage to reduce the amount of data needed for analysis. It’s also important to optimize the storage medium to improve search capabilities within geometric constructs, like search for points that represent a city within a multi-polygon that represents a continent.
- Representation: Representation of geospatial data can also be a major challenge. You need to define a geodetic datum to convert the three-dimensional location information of earth into a more easily readable and processable two-dimensional format.
- Scale and accuracy: The scale and precision on which geographic data needs to be represented also plays a vital role in determining the level of accurate analysis that can be derived from the data. Some algorithms need extremely accurate geographic data to provide precise results, and collecting and representing this data can prove to be a major challenge.
3 capabilities you need to process IoT geospatial data
IoT data in general can be quite challenging to process and maintain. (This Informatica article describes some must-have capabilities for processing IoT data in general.) In addition to those capabilities, here are three specific capabilities you should have to address geospatial data embedded within your IoT data. You want a solution that enables you to:
Process different data types: The solution you choose for handling geospatial data needs to handle different data types from varied sources with relative ease. As with IoT data, the sources for geospatial data can be both structured and unstructured making the data types varied and dynamic.
Perform complex computations effectively on huge volumes of data: Another striking characteristic of IoT data is the sheer size of the data that needs to be processed in a fast and automated fashion. The data management solution you choose must be able to process huge volumes of data while performing complex computation on geometric structures in a fast and efficient manner.
Easily blend geospatial data within IoT: Your solution should allow you to blend in the geospatial and non-geospatial aspects of IoT data to create unified dashboards. Geospatial data enhances the dimensionality of IoT data. The solution should provide capabilities to process both these data sets with relative ease and reduce overhead for dedicated pipelines based on data type.
Tips for processing IoT geospatial data with Informatica
Here’s how you can get started on geospatial and IoT data with Informatica solutions.
Informatica Intelligent Cloud Services (IICS) Cloud Data Integration Elastic (CDI-E) service helps process large volumes of data using Sparks distributed processing capabilities. CDI-Elastic uses a secure agent to spin up an ephemeral Kubernetes cluster and submits a Spark job to be executed in parallel across your data loads, reducing the total time required to process huge volumes of data. With our new Advanced Serverless model, you don’t need to manage the infrastructure in which these clusters run, simplifying the processing for huge workloads.
CDI-Elastic can help provide the ideal capabilities needed from a solution listed above and mitigate the challenges involved in processing geospatial data.
- CDI-Elastic provides ideal capabilities to process huge data volumes associated with any geospatial workload stored in various data sources including IoT sources by integrating with Informatica’s cloud-native schema-agnostic ingestion solution. It allows users to perform complex computations using its easy-to-use cloud user interface, leveraging distributed computing and parallel processing techniques to create an optimized and cost-effective pipeline.
- CDI-Elastic supports several datatypes and also allows users to import open-source packages using transformations like Python Tx for added data types supports thus allowing users to handle complex data type needs with any geospatial data.
- The mapping logic allows us to create simple drag and drop pipelines and workflows that will integrate the geospatial data with other non- geospatial data in the same common platform and provide various transformations to read, clean, prosses and write the geospatial and non-geospatial data together to variety of targets. These targets can then act as sources to generate reports, create dashboards or populate maps with geographic data and its associated metadata.
For example, using CDI-Elastic Python Tx, you can upload a custom Python installation with the Python library you need for your use case, such as geopandas, moving pandas, or scikit-mobility, to name a few. The code you need for computations like finding a centroid among data points or creating geometric constructs like points, polygons, or multi-polygons can be added to Python transformations, which use the libraries installed in the custom Python binaries. CDI-E bundles this Python code, packages it along with the custom Python installation, and pushes it to a Kubernetes cluster to execute in the same way as a Spark application.
You can use the Python Tx reference file feature to link in any number of files that can be referenced in Python code to solve more complex use cases like loading a geojson file directly in Python or uploading a machine learning model using Pickle. Besides Python Tx, you can use several available CDI-E transformations to avoid writing excess code and solve the use case with relative ease. The mapping structure in CDI-E also allows you to link data from other sources with the geometric data in a single pipeline, so you can take advantage of the parallelism that the Spark engine has to offer.
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