第29回日本情報地質学会講演会 講演要旨集 023-024頁 2018年

Development of Service Oriented Framework for Geospatial Field DataManagement and Analysis
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Niroshan BANDARA* and Venkatesh RAGHAVAN**

空白* 大阪市立大学大学院創造都市研究科, Graduate School for Creative Cities, Osaka City University,
3-3-138 Sugimoto, Sumiyoshi-ku, Osaka 558-5858, Japan. E-mail: niroshansnj@gmail.com** Graduate School of Engineering, Osaka City University, 3-3-138 Sugimoto, Sumiyoshi-ku,
Osaka 558-5858, Japan.

Key words: Filed Data, Open Data Kit (ODK), Sensors, Web services, WPS

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１．Introduction
Field data collection is one of the important and basic
tasks for any geoscientific investigation. Geospatial fielddata can be collected based on human observation as fieldsurveys. Also, field data can be collected by utilizing fieldsensors. With the development of technologies such as
GNSS and proliferation of GPS enabled smartphones,
geospatial field data aggregation has become moreconceivable than ever before. Moreover, there are various
ready-to-use software tools to facilitate collection,
management and sharing of field data using mobiledevices. On the other hand, nowadays mobile applications
which are running in mobile platforms are capable ofconnecting to variety of external sensors over wirelesschannels such as Bluetooth. However, there is a lack ofproper implementation in field data collection, storage,
sharing and analysis with human and external sensor
gathered data. Therefore, this research aims demonstrateon how the geospatial ecosystem comprising of Open
Source Software, Open Data and Open Standards can beleveraged for deploying geospatial field data management
and analysis platform with human and sensor inputs.

２．System and Methodology
ODK that provides tools to facilitate collection ofgeoreferenced data and transmit to a centralized server innear real time is used to deploy the proposed system alongwith Android Sensor applications such as Ardui2Androand ODK Sensor app. Moreover, human and sensor
integrated data collection method, data sharing andgeoprocessing are the main enhancement implemented inorder to provide a comprehensive framework for
Geospatial field data management and analysis. Bothdata sharing and geoprocessing tasks are compliant with
Open Geospatial Consortium Web Services (OWS)
standards allowing for interoperability and scalability. In
the present system, Web Map Service (WMS) and WebFeature Service (WFS) have been used to offer data
oriented interoperability. Further, Web Processing
Services (WPS), which is designed to standardize the waythat geoprocessing algorithms are made available on theInternet has been used to facilitate instantaneous andremote processing of geospatial field data. ZOO-Project
which enables existing open source libraries to interact
through its WPS framework has been used to facilitate

WPS in this system. The system framework demonstrates
the seamless integration of field data with desktop as wellas Web GIS software. The various Open Source tools usedto deploy the framework is shown in Figure 1.

ODK Sensors app

Figure 1 System architecture

３．Work-flow and System Implementation
The workflow for geospatial field data management andanalysis system is depicted in Figure 2, consists of aserver-side
andclient-side
component.
Firstly,administrator has to create forms which can be designed
using LibreOffice and upload it to ODK Aggregate. Next,
users download the forms to their mobile devices and start
to collect/aggregate field data. Most importantly, ODK
facilitates gathering of georeferenced and multimedia
data such as photo of the location, also text and numbervalues. In addition, special row/parameter needs to bedefined in the form when user wanted to enable the datacollection from external sensors via Bluetooth. Then user
can add human observation data along with externalsensor data in separate pieces. The collected forms can beuploaded to ODK Aggregate server when the internet isavailable and store them in the spatial database. The
stored data is automatically published as WFS and WMS.
Moreover, it can be visualized or further processed.
Besides, visualization facilities are extended via mobile
application as well. The process can be done either byusing desktop application or Web application and user canvisualize the WPS results using bowser client for
rendering dynamic map on a web browser.

24But, the field party or decision makers may need to view
the processed results instantaneously. Access to softwareand knowledge regarding operation of desktop GIS could
be lacking. In order to overcome these limitations andfacilitate geoprocessing and visualization on a web
browser, WPS for spatial interpolation using gdal_grid
function is deployed using ZOO-Project. Collected data is
automatically processed the water depth model is shownin the same web interface (Figure 6).

Figure 2 Workflow

4. Results and Discussion

Firstly, a form has to be created for the field survey andthe parameters need to be identified based on the purposeand study. The system has been deployed for an imaginary
flood mapping survey in order to evaluate anddemonstrate the functions of the system. After collecting
data, it can be seen in ODK Aggregate and also be directly
displayed on Web Viewer as described in Bandara et al,
2016. This study tries to emphasize the furtherdevelopment. As of, data can be imported to QGIS in two
different ways. First method is to connect the PostgreSQL
database directly (Figure 3) to QGIS, the second way is toimport as WFS (Figure 4). It allows user to save layers as
ESRI Shapefile.

Figure 5 Water level interpolation map processed in QGIS

Figure 6 Web processing result

Similarly, functions such as visualizing sharing andanalysis (Figure 7) can be made with the data which arecollected from external sensors.

Figure 3 Data import to QGIS from PostgreSQL

Figure 7 Data analysis from external sensors

5. Conclusion

The system described in this article provides
functionality for simple visualization, archiving andmanaging, seamlessly exchange of geospatial data withdesktop GIS and online geoprocessing. The result
demonstrates the efficacy of the system to deliver solution
for varies geospatial data collection, management andanalysis needs, and it can be utilized in varies disciplines.

6. Reference
Figure 4 data import to QGIS as WFS

As a demonstration of further analysis in QGIS, theflood depth levels are interpolated water surface
elevations using Inverse Distance Weighting (IDW)
algorithm for generating the water depth map (Figure5).

Bandara, N., Raghavan, V., Yoshida, D. (2016)

Development of Field Data Monitoring and Evaluation
Platform using Customizable Mobile Application and
Web-Mapping Tool, Geoinformatics., vol.27, no.2, pp.114115