Applications of Mobile Data Collection Apps in Ecosystem Health and Spatial Planning

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 Historically computer-aided statistical tools were used to conduct census surveys . Over the years, advances in the study of mobile data collection technologies have revolutionalized mobile research. Nowadays, there are numerous tools and apps for mobile GIS. These include, inter alia, Esri data collector, ArcGIS Survey123, QField, Teamscope, Open Data Kit(ODK), KoboToolbox, REDcap, SurveyCTO, Qualtrics, Zoho forms, Enketo webforms and Ona to name a few. These tools are used in a number of fields including the environment. They may be used for ecosystem health data collection in preparation for spatial planning processes. Mobile survey apps improve data quality and consistency, reduce the costs of hiring data entry clerks and printing, save time, and ensure centralized data storage. However, there are costs associated with purchasing hardware(e.g. tablets, smartphones, and/or computers) and internet connection for data management.


Like in various fields, these tools may be deployed for a number of reasons including data collection for ecosystem health and spatial planning purpose. Data so collected can be used to develop web apps and dashboards that provide interactive visualization for decision-making. These platforms can be used for data-driven policymaking. There is a large number of platforms that may be used for housing these data sets. Some of these platforms are open and free while others are proprietary. For instance, in the case of a web app developed with R Shiny framework, the app may be published on the shinyapps.io platform while in the case of ArcGIS, ArcGIS server may be used. There are other platforms such as tableau or PowerBI that may be used for data visualizations. In addition, Google Earth Engine may also offer a competitive advantage for geospatial data analysis and visualizations through apps.


The choice of each tool will depend on specific organizational requirements. It may be influenced by budgetary constraints, availability of human resources with requisite skills and knowledge, analysis scenarios, soft and hard infrastructure, and willingness to undergo digital transformation. In the case of ecosystem health diagnostics, various AI tools including machine learning(ML) models may be deployed using in-situ data collected with mobile devices and freely available satellite imagery data from different space agencies. These techniques may be used to produce a map depicting particular ecosystem health properties including yield, productivity, and all aspects of soil health. Commonly used programming languages in these tasks include R, Javascript, and Python to name a few. Knowing various indicators of ecosystem health, planners will find it much easier to plan spatially targeted ecosystem restoration interventions. 

In the era of the 4th Industrial revolution, it is up to anyone to decide whether to keep abreast with technological innovations in place or not. 

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