Design of Web based Geospatial Data Services Framework – An Implementation of Web-based Climate Services

Abstract

Spatial Data has increased tremendously in the last two decades from various data sources including satellite sensor data, Internet of Things data, data collected from remote sensing instruments and crowd sourced data. With the advent of mobile phones, devices and internet applications, a vast amount of spatial data is also available from various mobile and web applications. These datasets have lots of applications in different fields like Urban planning, waste management, Traffic monitoring and planning, Climate science studies, Climate resilience, Agriculture, Forest survey, disaster management, smart city planning etc. These datasets have huge volume, variety, veracity, and velocity, making the problem of spatial data analysis a big data problem. To convert these datasets to usable applications in timely manner will ensure the effective utilization of the application. A unified framework that collects, processes, and helps users analyze the output over internet will be beneficial to the geospatial domain researchers and policy makers for enabling faster decision making. Apart from these, web based geospatial applications also has applications for general citizens like smart homes, transportation etc. Climate change has been significantly affecting our environment and society since ages. Another important application of web based geospatial framework is climate change study. This is one of the urgent needs for which informed action has to be taken immediately. Sustainable Development Goal 13 (SDG-13) calls for urgent climate action to combat climate change [1]. This calls for focused study to create mitigation plans at global, country and regional levels. For this, climate research should be carried out in faster way. Also, along with climate science expertise, traditional climate research involves significant data analysis and knowledge in computing is essential. Considering this, there is a need for common platform that provide web-based climate services for climate researchers, decision makers and policy makers that enable faster research and availability of research output results. It is also required for these services to be discoverable, available, interoperable, and reusable. Hence, using published and widely used standards for this platform is also essential for enabling wider use. Open geospatial Consortium (OGC) has been developing and maintaining the standards needed for Geospatial data management, publishing, sharing, and processing such datasets. OGC Web Services standards WMS, WFS, and WPS are suitable for providing data access to users in the form of maps and data. OGC Web Services standard WPS, allows users to utilize the geospatial data processes that are published. Hence OGC Web Services standards are suitable for implementing this framework. Also, these services are widely used and available as open-source solutions. However, OGC Web Services are implemented in Remote-Procedure-Call (RPC) architectural style using Extensible Markup Language (XML) over HTTP protocol. Currently, resource-oriented architecture like RESTful API is being used by various modern web applications and are competing against traditional service-oriented architectures like RPC. Hence, standards that implement RESTful APIs like OGC API standards are more suitable for this framework. OGC API components maps, tiles, features, coverages, and processes offer similar services to OGC Web services. Additionally, OGC API records implements a mechanism for the available datasets and tools to be discoverable. Considering this, OGC APIs are more suitable for this framework. Extensive work is being carried out by the OGC community to publish all the standards of OGC APIs and many opensource software like pygeoap, GeoServer and MapServer are also implementing these standards. Considering this, we chose both OGC API and OGC Web Services standards for the design of this framework. Also, most of the geospatial software worldwide offers tools and services compatible with OGC Web Services currently. By implementing both standards, our design will follow the W3C best practices for sharing data as well as being in compliant with most available and widely used geospatial software. Phasing out the OGC web services can be carried out when the OGC APIs are published and implemented by major geospatial software and service providers. SensorThings API is another standard that is used in this design for ingesting sensor data into the portal. After ingestion, this data is designed to be served using OGC API Features, Maps and Environmental Data Retrieval components making sensor data useful to different sectors of users. Considering the current requirements and the above-mentioned standards, in our research work, we identify the challenges involved in developing a unified web based spatial data framework and design a web based spatial data framework that will address these challenges. Further, we chose climate science as a use case to demonstrate this framework. We designed this framework to accept data from multiple sources like Satellite derived products, Internet of Things datasets, Instrument datasets and citizen datasets in various geospatial formats by using open-source solutions that implement the OGC Web Services, OGC API and SensorThings API standards. We designed the modules and the workflows to address the functional requirements of this framework. Apart from the functional requirements, we also identified the need for the framework to be scalable and highly available for the framework to be effectively used by larger audience. We added big data solutions Elasticsearch and open data cube that make the framework scalable and handle big data. We implemented three use cases in climate science based on the framework principles and successfully demonstrated handling of Internet of things data, scalability of IoT data, development of on-the-fly dashboards and the usability of publishing geospatial data processes for faster research. Although, we demonstrated the usage of framework for IoT and satellite derived data sets, further work needs to be carried out to implement other geospatial datasets and formats. Usage of Big Data solutions for large datasets will also be beneficial to handle the processing of large spatial datasets and provides future scope for development.