International Professional Master in Management of Eco-Innovation 2011-2012
Module M2: Science & Technology
INDIVIDUAL ESSAY
LOMTEVA ANNA
GEOGRAPHIC INFORMATION SYSTEM (GIS) FOR ENVIRONMENTAL RISK
MANAGEMENT
EXECUTIVE SUMMARY
Recent studies show the growing interest in implementation of geospatial information technologies for
environmental risk assessment and modeling. Due to diverse functions of data processing, analyzing and
modeling Geographical Information Systems have found implementation in floods, droughts, cyclones,
tsunamis, landslides and earthquakes risk management. Still there are some constrains of this approach
that should be taken into consideration during decision-making process.
KEY WORDS
GIS, risk management, hazard modeling, spatial decision-making
Introduction
Within geographical regions where natural hazards frequently occur brining people’s death and injury,
property damage and economical activity destruction it is a question of great importance to have an
effective system of forecasting, assessment and management of danger risks. Natural perils are complex
phenomena varying in magnitude, temporal and spatial dimensions, social and economic consequences,
therefore monitoring of such events is a difficult process, requiring handling of a large amount of data
and considering a broad set of feasible alternative decisions. Significant volumes of multi-disciplinary
and technical information have to be collected, processed, analyzed, and finally communicated to a
broad range of users under quite different conditions, ranging from planning and regulatory activities to
emergency management (Van Westen C.J.2004).
Recent studies show the growing interest in implementation of geospatial information technologies for
environmental risk assessment and modeling (Van Westen C.J. 2004, Chen K.et al 2003, Türköz M. and
Tosum H. 2011).
Geographical Information Systems (GIS) due to their capability of extracting appropriate data from large
stores of information, integrating data from diverse sources (Global Positional System, remote sensing,
cartography, geodesy, statistics etc.) with different formats (vector, raster, tables), combined with
powerful analytic tools and visual representation of results became an important instrument for support
and facilitation of decision-making process in natural risk management (Cova TJ 1996). GIS approach is
applied now for example for floods, droughts, cyclones, tsunamis, landslides and earthquakes modeling
(Pradhan B. and Youssef A.M. 2010, Lin M-L. and Chen C-W. 2011, Merati N. et al 2010). It is broadly
used by government structures and corporate agencies (Chen K. et al 2003).
But besides multiple advantages of the method in terms of hazard events simulation and analysis number of authors observe some limitations and obstacles on the way toward wide adoption of GIS-based
risk planning (Cai G. et al 2005, Zerger A. and Smith D.I. 2003).
The objectives of this essay are to review the current practices of GIS-based environmental risk modeling, identify the existing problems of current efforts to link GIS with natural hazard management and
look for possible future development in field of eco-innovation.
Materials and Methods
In order to assess potential of GIS implementation in hazard risk management the author of this essay
conducted research of up to date articles in environmental, business and technology academic journals
and books. Publications considered include the following example journals: Environmental Management, Environmental Earth Science, International Journal of Risk Assessment and Management, Environment and Urban Systems – and present both overviews of the issue and particular case-studies. Author tried to take into account various aspects of GIS-based risk modeling, searching answers for these
questions: Which phases of risk management can be covered by GIS approach? What kind of information is needed and what are the requirements for input data? Which characteristics of GIS software
are the most valuable for the process? And is GIS seen as effective tool for support and facilitation of
decision-making in natural hazard risks management?
Results and discussions
GIS is regarded as technology for handling geographical data in digital form, satisfying the following
needs:
-
ability to extract data from large stores and transform it into form suitable for analysis, including
such operations as reformatting, change of projection and generalization;
direct support for analysis and modeling;
possibility to have multiple attributes for geographic entities;
postprocessing of results, including such operations as tabulation, report generation and mapping.
And according to Goodchild M.F. such features as:
-
support of efficient data input methods, including importing from other digital systems like
CAD;
support of continuous spatial variations modeling due to effective methods of spatial interpolation;
ability to compute relationships between objects based on geometry(interaction, inclusion) and
to handle attributes of pairs of objects;
ability to carry out a range of standard geometric operations (calculate area, perimeter, length);
ability to generate new objects on request, including objects created by simple geometric rules
from existing objects (Voronoi polygons from points, buffer zones);
ability to assign new attributes to objects based on existing attributes and complex arithmetic
and logical rules;
support for transfer of data to and from analytic and modeling packages (statistical packages,
simulation packages) in particular turn GIS into useful tool for environmental modeling (Goodchild M.F. 1993).
GIS play an important role during all stages of emergency management including mitigation, preparedness, response and recovery (Cova TJ 1996), assisting in solving problems typical for each of the phases.
One the most active function of GIS in mitigation relates to analytical modeling. This step gives the opportunity to conduct long-term assessment, planning, forecasting, and management. The modeling process reveals inherent spatial variation in hazard, vulnerability, and ultimately risk. (Cova TJ 1996)
In the preparedness and response phase, GIS is primarily utilized to help formulate and implement
emergency response plans. Emergency managers take centre stage in this phase, which is frequently
characterized by urgent, mission-critical decision-making. The tremendous demand for timely, accurate
answers to geographical queries makes this GIS application area unique. The primary benefits of GIS in
this phase lie in spatial information integration and dissemination. Emergency management personnel
need to know where an event is occurring in order to minimize further loss and effectively deploy relief.
GIS development activity in this phase currently focuses on designing comprehensive disaster management systems to serve the information needs of emergency management personnel under various disaster scenarios. Innovations in real-time GIS, remote sensing, interoperable GIS, and the Internet are
having a significant and beneficial impact on research and development in this phase. (Cova TJ 1996)
One of the hallmark applications in the preparedness and response phase is automated mapping. The
value of maps during this phase include:
- coordinating the efforts of emergency groups;
- providing the public with guidance;
- aiding the flow of resources during and after a disaster;
- revealing the elements at work in a geographical area;
- visualizing the physical constraints of the incident site;
- producing public relations information.
Another GIS role in the preparedness and response phases relates to hazard modeling, which differs
slightly from the hazard modeling in risk assessment. In this context the disaster is occurring, and it is
possible to gather many of the environmental parameters to aid in short-term prediction (Cova TJ
1996).
In the recovery phase after the initial relief has been provided and the goal is returning life to normal or
improved circumstances, a GIS can serve as a spatial inventory system for coordinating recovery activities. Government agencies, policy makers, and civil engineers figure prominently in this phase. Some of
the challenges during recovery include assessing the damage, assuaging and educating the public, rebuilding, and preventing reoccurrence (Cova TJ 1996).
One of the key advantages of using GIS for decision support is the possibility of varying different parameters and generation alternative scenarios in spatial context (Chen K.et al 2003). These tasks demand an
integration of different categories of data – geographic, socioeconomic and management related. Physical environmental data is needed to complete topography, land use, land cover, meteorology, geology,
vegetation and other layers of the GIS project while socioeconomic figures are used to assess community vulnerability and include population and housing census data.
Process of information collection is very important and difficult phase. Data must meet several requirements such as availability and quality, spatial suitability, clarity, applicability and decision utility (Chen
K.et al 2003). Lack of information and the weakness of existing data can be a significant limitation for
GIS-based risk assessment. For example it is essential for emergency management to known where exactly people are in a city at a given time period, but daytime population fluctuations are difficult to
model, and the approaches that exist tend to model these fluctuations at aggregated level (Cova TJ
1996).
Issues that arise more often in terms of integration are geographical scale and type of data model. Fist
problem is that information is collected at particular geographical scales what defines level of spatial
resolution and aggregation of data. This may become an obstacle if there is a need to take into account
global, national and local aspects of risk management. Most of all modifiable areal unit problem concern area-based socioeconomic data. In order to integrate it with different geographic information for
effective risk assessment, the selection of spatial analysis units that are compatible with these two data
sources is important. Due to the lack of relevant detailed socioeconomic data and the difficulty of their
effective spatial representation for integration with physical environmental data, vulnerability assessment is one of the least investigated tasks (Chen K.et al 2003).
Another limitation is mathematical simplification of real-world processes for model. GIS use two major
data models - vector and raster. First type is usually used for discrete objects which can be displayed as
points, lines and areas (dwellings, utilities for vulnerability analysis), second - for natural hazards and
evens of continuous type. But even using raster model it is impossible to reach an ideal representation
of natural processes since there is always a certain kind of discretization due to associating values by
cells. Some variables for hazard behavior could be poorly understood and therefore difficult to model
(Chen K.et al 2003). Moreover Cova T.J. notes difficulties in understanding the errors in GIS models
(Cova TJ 1996).
So GIS models often carry assumptions that may or may not be realistic for actual situations and these
assumptions are rarely communicated to the users of such scientific information. The models may run
on imperfect data that leads to a large degree of uncertainty in risk prediction. Even when the analysis is
done correctly, the result of scientific models is often not directly interpretable, but needs to be translated into a form relevant to decision-makers’ immediate concerns. Second, decision-makers in the early
phases of crisis response also face the challenge of asking the right questions, making the right assumptions to compensate for the lack of knowledge (or information), and incorporating the perspectives and
judgments of all the interested and affected parties (Cai G. et al 2005).
Some authors also see constrains for implementation of GIS for real-time risk management (Cai G. et al
2005). Real-time disaster applications of GIS have very specific requirements which are significantly different from long-term decision making for disaster planning (Zerger A. and Smith D.I. 2003). One of the
obstacles here is seen in complexity of GIS software, what demands a special training for personal or
employment of GIS analyst. Then in second case in order to get an updated map, a crisis manager currently has to communicate such needs (together with requirements) to a geospatial analyst who will
interpret and translate the needs into database queries and user interface operations. With this arrangement, immediacy is hardly possible since the human mediator (i.e. the GIS analyst) often becomes
a barrier when quick information transfer is needed. It usually takes a long time for the geospatial analyst to collect the relevant data, to extract, integrate and analyze them, and to display or print the information products before they can be used by crisis managers. Such delays interrupt human cognitive
and communicative processes, and reduce the value of geographical information and the effectiveness
of the decisions made (Cai G. et al 2005). Because in dealing with natural hazard decisions must be taken
fast.
According to the research conducted by Zerger A. and Smith D.I. GIS was unable to provide the answers
asked of it in real-time also due to technical constraints that included limited computer processing power and the size of building database. The building database size in particular prevented GIS operators
from providing answers to fundamentally spatial questions in a time frame suitable for decision making.
(Zerger A. and Smith D.I. 2003)
Besides GIS were designed without considering the operational requirements of emergency management personnel in a response situation. Specifically, the operational requirements in such scenarios appear to require temporal resolution rather than spatial resolution. For instance, emergency managers
were more concerned with the general movement of people and vehicles rather than spatial precision
and high spatial resolution.
Hardware issues also influence potential of using GIS for environmental risk assessment. For example
the size of computer screens may not provide necessary detail for regional scale emergency management decision-making. This makes it difficult to relate computer-based output to broad scale decisionmaking. Difficulties as well can be found in collaborative decision-making, to be more precise in sharing
information via a single computer terminal to multiple users (Zerger A. and Smith D.I. 2003).
Concluding remarks
GIS with its wide range of spatial analysis techniques and tools have found use in identifying, measuring,
diagnosing various aspects of natural hazards and their consequences. They became an essential instrument for proceeding large amounts of data due to possibility of procedure automation and formats
integration. Moreover capability of adjusting pre-defined functions of GIS for solving specific modeling
problems through the range of programming tools widens sphere of possible GIS implementation in environmental risk management.
In spite of some limitations in existing use of software GIS-based risk and disaster management is seen
as a feature of state and local government’s natural hazard risk management procedures (Zerger A. and
Smith D.I. 2003) and necessary instrument for development of intelligent emergency preparedness and
response systems (Cova TJ 1996).
References
Cai G., Sharma R., MacEachren A. M., Brewer I., 2005, Human-GIS Interaction Issues in Crisis Response.
International Journal of Risk Assessment and Management special issue on GIS in Crisis management (in
press)
Chen K., R. Blong, and C. Jacobson (2003) Towards an integrated approach to natural hazards risk assessment using GIS: With reference to bushfires, Environmental Management, 31, 546– 560.
Cova TJ (1999) GIS in emergency management. Pages 845-858 in P.A. Longley, M. F. Goodchild, D.J.
Manguire and D.W. Rhind (eds.), Geographical information systems: principles, techniques and management. John Wiley & Sons, New York.
Goodchild M.F. (1993) The State of GIS for Environmental Problem-Solving. Pages 8-16 in M.F. Goodchild, B.O. Parks, L.T. Steyaert, Environmental modeling with GIS. Oxford University Press, New York.
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Merati N., Chamberlin C., Moore C., Titov V., Vance T.C. (2010) Integration of Tsunami Analysis Tools
into a GIS Workspace – Research, Modeling, and Hazard Mitigation efforts Within NOAA’s Center for
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and Disaster Analysis, Geotechnologies and the Environment.
Pradhan B., Youssef A.M. (2010) Manifestation of remote sensing data and GIS on landslide hazard analysis using spatial-based statistical models, Arab J Geosci, 3, 319-326.
Türköz M., Tosum H. (2011) A GIS model for preliminary hazard assessment of swelling clays, a case
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Van Westen, C.J (2004) Geo-information tools for landslide risk assessment — an overview of recent
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