USING SDI AND WFS FOR QUALITY ASSURANCE 
ON FIELD DATA COLLECTION 
Mohammad Hosseinpour 1 , Ali Asghar Alesheikh 2 , Majid Hamrah 2 
'GIS M.Sc student at KNT University of Technology Hosseinpour. - mhp@gmail.com 
Associate Professor at KNT University of Technology 
- Alesheikh@kntu.ac.ir 
KEY WORDS: SDI, Web Feature Services, Data Quality, Field Data Collection 
ABSTRACT: 
Nowadays, various organizations collect new spatial data or update existing ones. If a specific information community collects 
spatial data with regards to its own requirements, purposes and applications then other information communities may not be able to 
use the data easily. In addition to quality issues of the collected data, the semantic heterogeneity plays a major role in spatial data 
sharing. An important objective of establishing spatial data infrastructure (SDI) is to share data between diverse information 
communities through exerting policies and standards. As such, SDI can provide a suitable framework to for collecting, sharing and 
using data.Web Feature Services (WFS) are suitable tools for achieving this aim because they consist of information about features 
definitions and their complete attributes. This leads to solve the problem of different understandings from a specific feature. In 
addition to this, it is possible to formulate integrity constraints using ontology programming languages and implement the rules 
during field data collection.This paper aims to investigate the way of imposing quality controls on acquired data during field data 
collection. As a prototype, a mobile data acquisition system has been designed that warns users when any data inconsistency occurs 
and users can remove the error from database with correcting the occurred error instantly. 
1. INTRODUCTION 
Nowadays, various organizations collect new spatial data or 
update existing ones. If a specific information community 
collects spatial data with regards to its own requirements, 
purposes and applications then other information communities 
may not be able to use the data easily. In addition to quality 
issues of the collected data, the semantic heterogeneity plays a 
major role in spatial data sharing. An important objective of 
establishing spatial data infrastructure (SDI) is to share data 
between diverse information communities through exerting 
policies and standards. As such, SDI can provide a suitable 
framework for collecting, sharing and using data. 
At the other side, in the field of data acquisition, using mobile 
geographic information system (Mobile GIS) has facilitated the 
task of collecting or updating data and whereas these data are 
mostly transferred directly to database so there is need to assess 
them from the quality viewpoint and semantic accuracy to avoid 
inconsistency in database. The best way for this work is to 
control data during their collection in the field. 
The aim of this paper is to address the way of exerting these 
quality controls on data during their gathering in the field. In 
order to achieve the mentioned scope, the basic concepts like 
SDI, WFS and Mobile GIS are described at first and then in the 
following sections these concepts and technologies are used to 
impose quality controls from the early stages of data acquiring 
and in the last section an implemented mobile data acquisition 
system is presented as a prototype. 
2. SPATIAL DATA INFRASTRUCTURE 
Spatial data refers to the subset of data that is related to the 
earth such as: topographic data, geographic features and height 
information. Approximately 80% of used data in government 
decisions are spatial or at least related to the earth [1]. For 
using spatial data, they must be accessible and easily usable. 
These data have a major role in spatial decisions and people can 
have a better decision with using suitable spatial 
data. 
Many private and public organizations are acting in the field of 
producing and maintaining spatial data and their products 
perhaps are the same. Then for avoiding these extra works and 
saving money and time, data sharing will be the crucial issue in 
the field of spatial data. That means each organization that 
produces a dataset, should inform users and other organizations 
that may need that data. 
1.1 SDI Definition 
There are some definitions for spatial data infrastructures but 
the thing that is common between them is creating the area that 
all could collaborate with each other to reach their purpose in all 
organizational and political levels. Regarding the different 
existing branches, there are different definitions for SDI but in 
this paper Groot and McLaughlin’s definition of Spatial Data 
Infrastructure has been adopted [2]: 
“Spatial Data Infrastructure encompasses the networked spatial 
databases and data handling facilities, the complex of 
institutional, organizational, technological, human, and 
economic resources which interact with one another and 
underpin the design, implementation, and maintenance of 
mechanisms facilitating the sharing, access to, and responsible 
use of spatial data at an affordable cost for a specific application 
domain or enterprise.” 
2.2 Core components of SDI 
With reference to existing definitions for SDI, the main 
components of SDI can be recognized. As shown in Figure 1