Integrating GIS with Models: A Bibliography
“Efficient Noise Mapping using ArcGIS and detailed Noise Propagation Simulation.”Kucas, A., Hoej, J., and Frederiksen, R.. 2007 ESRI European User Conference.
Abstract: “As EU directives increasingly requires larger cities to undertake noise mapping projects, the demand for cost-effective noise mapping tools and methods have increased. ArcGIS provides an excellent platform for managing and editing the data necessary to run a noise mapping simulation. Through the use of a dedicated noise mapping extension, seamless integration with the necessary calculation tools (based on the Common Nordic Noise Model) is provided. The end result is the efficient production of detailed and realistic noise mapping results. An example of this approach, the noise mapping project for the city of Kaunas is presented. This is one of several projects carried out by HNIT-BALTIC, where large scale (the noise mapping of a major city) is combined with high detail (results along building facades as well as a surface noise map). Finally, the implications of the new revision of the common Nordic noise mapping methodology, Nord2000, are discussed. Nord2000 brings new features (more detail, handling of new noise sources) but also new requirements (more detailed model data, more demanding calculations).”
“Simple GIS Tools for Traffic and Transit Noise Studies.” Keller, K. 2002 ESRI International User Conference.
Abstract: “Many companies are marketing complex programs for integrating ArcView and noise modeling software. While these programs are impressive, they require the user to have expansive knowledge in both the noise and computer software fields. This paper will demonstrate simple, out of the box ArcView tools that can improve noise modeling for both traffic and transit studies. Techniques for improving visual presentation for the results will also be shown.”
“A GIS-Based Approach for 3D Noise Modelling Using 3D City Models.” Kurakula, K. March 2007.
Abstract: “Noise pollution of urban areas is one of serious factors that the local agencies and state authorities have to consider in decision making processes. The spatial analysis and geostaticstical methods of GIS can play an important role to control noise pollution. GIS provide framework to integrate noise calculation models with spatial data that can be used for building noise maps. Noise maps can be used to assess and monitor the influence of noise effects. Noise maps within GIS have been developed in most of the European Countries. European Commission has approved the Directive called ‘Environmental Noise Directive 2002/49/EC’ for noise mapping. Most of the noise maps that are available today and also that suggested by EU are in two dimensional (2D) in which noise effect is presented in x,y plane. 2D noise maps are built with the noise levels of one particular height. In the reality, noise travels in all direction. Residents living in high rise buildings are also severely affected by traffic noise. It is therefore important to develop 3D noise maps that can show influence of noise in all direction. 3D city model can be used to build 3D noise maps. This research work developed a methodology to build 3D noise models to analyse the three dimensional effect of noise pollution within a GIS. A case study was illustrated using a 3D city model of Delft, the Netherland. This involved building simple 3D city model, generation of 3D observation points (that represent the virtual microphones) and noise calculation using standard noise calculation models. Fictitious data was used to calculate the noise levels of study area. Appropriate spatial interpolation methods (TIN, IDW, Natural Neighbourhood and Kriging) were used to develop noise surface. 2 ½ D noise models were built using 2D interpolation technique. In this research two 2 ½ D noise models were built: one using complex 3D city model and the other using simple 3D city model. 3D noise models were built by using 3D IDW interpolation method. The results showed that the quality and accuracy of noise models can be improved with high density of observation points. The observation points selected in straight line with evenly spacing showed good visualization of acoustic situation. The results also demonstrate that TIN and IDW performs as well or better than the other interpolation methods. 2 ½ D noise models can be used for decision making process to control noise effect and to take appropriate noise mitigation measures. 3D noise models can be used for scientific studies such as to study the phenomena of noise behaviour. The 2 ½ D and 3D noise models can be published via the internet so that public can easily understand the effect of noise pollution and can get involved in noise management process.”
“Integrating NOISEMAP with the Geographic Resource Analysis Support System (GRASS) to enhance environmental impact assessments and land use compatibility studies.” Shepardson, K. Proceedings of the 1994 National Conference on Noise Control Engineering.
Abstract: “The integration of the Geographic Resources Analysis Support System (GRASS) with noise models such as NOISEMAP and the Integrated Noise Model (INM) enhances the ability to calculate the impacts of flight operations on the surrounding community and the environment. A geographic information system (GIS) such as GRASS provides the tools necessary to perform complex noise analyses in a short period of time which in turn saves money. Additionally, because impact of noise levels on a study area can be run in a short time, alternative noise cases can be compared to see how modification in flight operations change noise impacts. This approach allows the military installation to adjust operations in order to decrease noise impacts and still fulfill its mission.”
“Environmental noise contouring in the 21st century.” Stewart, C. and Luz, G. 2006. Federal Facilities Environmental Journal, Volume 9 Issue 1.
Abstract: “Environmental Noise Contouring is used by the military to mitigate noise generated by training missions. More accurate noise prediction models are being developed to improve noise contouring. These models can be used for planning by the military and local governments to ensure the health and welfare of troops and people in surrounding communities. Current noise modeling software used by the Army does not account for the varying terrain surrounding airstrips and weapons ranges. New noise models will utilize digital data, such as topography and vegetation, to create more accurate noise contours that will be displayed with Geographic Information Systems. By using GIS to its fullest potential, military installations will be able to maximize property use and minimize noise impacts to surrounding communities.”
“Integration of the Traffic Noise Model with ArcView GIS.” Schlenker, G. 2005. Kleinfelder Professional Development and Technical Training Seminar.
Overview: “ArcView GIS and FHWA Traffic Noise Modeling TNM Applications. Optimizing and Integrating GIS Software to Streamline Analysis and Production. Producing Technically and Visually Improved Deliverables Using Integrated Strategies.”
“Integration of Storm Water Runoff and Pollutant Model and BMP Assessment Model Using ArcView GIS.” Xue, R., 1997. 1997 ESRI International User Conference.
Abstract: “Storm water management modeling has been an important task for engineers, scientists, researchers, consultants, and municipal personnel in the field of storm water treatment and management. There are two categories of storm water management modeling (1) watershed modeling and (2) best management practices (BMP) modeling. Watershed models generate storm water runoff and associated pollutant loads, while BMP models provide pollutant removal efficiency estimates. In this study an integration tool was developed by linking the Storm Water Runoff and Pollutant Model (SRPM) and the Best Management Practices Assessment Model (BMPAM) with a geographic information system using ArcView GIS software. The integrated GIS tool can support estimation of the runoff and pollutant loads from a watershed, evaluation of BMP performances, and investigation of storm water management alternatives. This tool provides a user-friendly interface with preprocessors and postprocessors for both models. This paper describes the development of the linkage using Avenue language between spatial data coverages, SRPM, BMPAM, preprocessors, and postprocessors. An example of the application of the integrated tool is presented to demonstrate the potential uses and capabilities of the integrated GIS tool in storm water management.”
“The Use of GIS to Manage LIDAR Elevation Data and Facilitate Integration with the MIKE21 2-D Hydraulic Model in a Flood Inundation Decision Support System.” Heinzer, T., Sebhat, M., Feinberg, B., and Kerper, D. 2000 ESRI International User Conference.
Abstract: “This paper describes the methods and results of an investigation into the use of LIDAR-derived digital elevation models as terrain input to the MIKE21 model. The purpose of this study is an attempt to utilize new developments in remote sensing technologies relating to elevation model derivation and implement these new sources in a GIS interfaced hydraulic modeling environment. The feasibility of simulating canal breaches is explored in areas near residential development. Ortho-rectified photographic images are referenced to eliminate LIDAR signals falling within building footprints and a ‘bare surface’ elevation model is interpolated. The buildings are subsequently re-extruded in the raster domain utilizing a GIS to provide realistic structural definitions. These data are converted into a MIKE21 finite difference mesh and the numeric modeling is performed. Snapshots at desired time steps and animations are generated in both the GIS and MIKE21 software. ArcView 3D Analyst is used to assist visualization. The results of these studies are supplied to local authorities for emergency response planning and to evaluate the degree of risk posed to local residents.”
“GIS-ROUT: Integration of ArcIMS and a River Water Quality Model.”
Wang, X., Du, C., Homer, M., Dyer, S. D., White-Hull, C. 2002 ESRI International User Conference.
Abstract: “Scientists often use mathematical models to assess river water quality. The study presented in this paper links ArcIMS to ROUT, a river model evolved from the U.S. Environmental Protection Agency’s Water Use Improvement and Impairment Model, to create a Web-GIS based river simulation model – GIS-ROUT. GIS-ROUT predicts consumer product ingredient concentrations in surface waters in the United States that receive one or more discharges from publicly owned wastewater treatment plants. The integration of spatial data, GIS, and analytical models in GIS-ROUT makes it possible to examine and share the results of dynamic linkages between water quality and human activities to support environmental risk assessment by scientists in different locations.”
“An Event-Driven Process for Creating Travel Demand Model Highway Networks Using Dynamic Segmentation.” Thomas, K. and Montoya, D. 1998. 1998 ESRI International User Conference.
Abstract: “This paper describes a method to integrate a transportation demand model and ArcInfo GIS without dependency on network conflation, using dynamic segmentation and well-defined event tables (e.g., bus routes, number of lanes, facility type) to organize highway and transit network data in a way that can readily be processed to create inputs for building a simplified transportation model. Once the model has been run on the simplified network, the output data (e.g., volume, time, speed) can easily be reattached to the geographic road base. This allows for the transportation model data to be analyzed and displayed based on the correct spatial geography.”
“Development of a Data Exchange Protocol between EMME/2 and ARCINFO.”
Lussier, R. and Wu, J., 1997. 1997 ESRI International User Conference.
Abstract: “Defining Issue: Sharing spatial information between a transportation planning software (like EMME/2) and a GIS is a complicated task in terms of data structure and informational needs. GIS Solution: INRO Consultants established guidelines to standardize the creation of a transportation planning coverage and developed a set of tools to transfer information between EMME/2 and ArcInfo. Methodology: We identify the motivation for the development of a data exchange procedure between ArcInfo and EMME/2. Then we identify the corresponding data items in ArcInfo and EMME/2, discuss the communication process for the data exchange and the computed results between the two systems, and develop a user-friendly interface to realize the data exchange. As a concrete example, we present a prototype of the system for the EMME/2 road and transit network, which can be used to display the attributes and results obtained in EMME/2. Future developments and possible applications of this system are discussed as well. Software: The prototype application is composed of ArcInfo AMLs, EMME/2 macros, and AWK scripts.”
“GIS Application for Linking Travel Demand Modeling and Air Quality Analysis.” Jensen, J. and Sathisan, S., 1996. Proceedings of the 1996 Geographic Information Systems for Transportation (GIST) Symposium.
Abstract: “The development of a Geographic Information System (GIS) based system to relate the travel demand modeling process with air quality analysis – specifically with respect to carbon monoxide (CO) – is discussed in this paper. A case study of the Las Vegas (NV) metropolitan area is used to demonstrate the development and application of the system. This area has been designated to be in non-attainment of the National Ambient Air Quality Standards (NAAQS) for CO. This necessitates the development of a State Implementation Plan (SIP) which identifies plans for attaining conformity with NAAQS. Transportation sources of emissions account for a vast majority (greater than 90%) of the CO emissions in the Las Vegas valley. Thus, the development of a SIP requires explicit consideration of travel demand, vehicular emissions and dispersion models. This paper documents the linking of tools and models that could be used to support such efforts. The models include TRANPLAN (a travel demand forecasting model) and the Urban Airshed Model, UAM (used for dispersion modeling to calculate concentrations of pollutants). Due to the powerful capabilities afforded by a GIS to manage, manipulate, analyze and display spatial data and due to the spatial nature of data required to support the analysis and modeling processes described above, a GIS environment was a logical chice for developing the model linkages. ARC/INFO was the GIS program selected for implementing the system. The system incorporates inputs to and outputs from UAM into the GIS environment. It combines this information with other travel demand, demographic, and land use data. This facilitates evaluation of the impact or effectiveness of policies ranging from land use planning/ zoning alternatives to travel demand management strategies on CO concentrations in the valley. The paper also documents problems and issues encountered in developing the system.”
“GIS for Transportation and Air Quality Analysis.”
Souleyrette, R. and Sathisan, S., 1992. Transportation and Transportation Planning and Air Quality.
“Including Caline3 Dispersion Model Predictions as Covariates in a Land Use Regression Model for NOX /NO2 in Seattle & Los Angeles.”
Wilton, D., Larson, T., Szpiro, A. and Gould, T., 2008. 2008 ISEA/ISEE Joint Annual Conference, Pasadena, California.
“Integrating Air Quality Analysis and GIS-T.”
Hallmark, S., W. O’Neill, 1995. Proceedings of the 1995 Geographic Information Systems for Transportation (GIS-T) Symposium.
Abstract: “Geographic Information Systems (GIS) linked with Environmental Protection Agency (EPA) air quality models are an effective tool for measuring and understanding the impact of transportation related air pollution. This paper describes a GIS-T/Air Quality system designed to enhance decision making in land use planning. Two EPA models are used to predict pollutant concentrations near roads from transportation sources. CALINE3 is a line dispersion model that calculates emissions based on free flow conditions along road segments. CAL3QHC is used for analysis of locations of extended idling, such as intersections. This model uses queue length information to estimate emissions based on delay. Output from the EPA models is passed to a GIS that generates air pollution concentration contours using a Triangulated Irregular Network (TIN) model. Contour layers are overlaid with land use layers to determine the compatibility between existing or proposed transportation facilities and development. Incompatible locations are identified so that mitigation strategies may be investigated. The specific application described in this paper focuses on air quality impacts from businesses with drive-thru facilities, such as banks, fast food restaurants, dry cleaners, etc. These facilities tend to cluster spatially and often contribute as much or more to local air pollution as signalized intersections in the vicinity. Transportation planners may use this model to evaluate mitigation strategies which include setting service time standards for drive-thru facilities, imposing impact fees for sites not meeting service standards, providing additional parking, etc. This paper describes the data model, data layers, and GIS tools that are used in this application.”
“Integrating Geographic Information Systems for Transportation and Air Quality Models for Microscale Analysis.”
Hallmark, S. and O’Neill, W., 1996. Transportation Research Record.
Abstract: “The inherently spatial nature of transportation-related air quality analysis makes the geographic information system (GIS) particularly well suited to enhancing microscale air quality analysis. GIS provides several features ideal for the type of analysis necessary to determine transportation-related air quality impacts. It is an excellent storage, manipulation, modeling, and mapping tool for spatial data. Spatial information such as street coordinates and accompanying attributes can be exported and manipulated as input to air quality models such as CALINE3 and CAL3QHC. Output from air quality models in the form of pollution concentrations at specified receptor locations can be input to GIS for hot-spot identification, estimation of contributions of off-road mobile sources, and impact analysis. GIS tools applied to air quality analysis include contour generation, classification, thematic analysis, point-in-polygon analysis, and polygon overlay. Several case studies demonstrating these capabilities using TRANSCAD, a transportation-based GIS package, are presented for microscale air quality analysis. Incompatibilities exist between current air quality models and most GIS. Differences in coordinate systems and distance metrics necessitate additional manipulation of data transferred between models and GIS. Other incompatibilities are that street segments are represented as centerlines in most planning applications of GIS and as a series of links in CAL3QHC and CALINE3, and that signalization parameters are represented differently from many common signal-analysis packages, which may necessitate additional data collection.”
“Integrating Travel Demand Forecasting Models with GIS to Estimate Hot Stabilized Mobile Source Emissions.”
Bachman, W., et al, 1996. Proceedings of the 1996 Geographic Information Systems for Transportation (GIS-T) Symposium. Washington, D. C
Abstract: “In a cooperative research effort with the U.S. Environmental Protection Agency, Georgia Tech is developing a regional mobile source emissions model using a Geographic Information System framework. The emissions model is designed to improve emission estimates by accounting for the spatial and temporal effects of a variety of vehicle activities, environmental factors, and vehicle and driver characteristics. While a description of the overall modeling approach is given, the emphasis of the paper is to describe the hot stabilized emissions estimation process and the role of travel demand forecasting models. Although travel demand forecasting models were designed for predicting future capacity requirements, they provide useful information needed for mobile source emissions estimates. Improvements to travel demand forecasting models to more accurately predict hot stabilized emissions are also discussed.”
“Modeling Transportation-Related Emissions Using GIS.”
Wu, P., 2006. Transportation Technology and Policy Graduate Group Institute of Transportation Studies, the University of California, Davis.
Abstract: “There are increasing requirements on the efficiency and accuracy of vehicular emission modeling due to significant contribution of the transportation sector to air quality problems. Because the essential component (i.e. transportation activities) of vehicular emission modeling is inherently spatially dependent, this study aims to move the existing old-fashioned Direct Travel Impact Model (DTIM), the California-specific Transportation-related emission inventory estimation model, towards a GIS-based model. The strengths of ArcGIS in data management, spatial analysis, and raster modeling are incorporated into three critical steps of emission modeling: disaggregating zonal travel activities (i.e. interzonal trip ends and intrazonal travels), combining travel activities (i.e. speeds and VMT) and emission factors, and gridding emissions into cells. This GIS-based method can promote an integrated transportation and air quality analysis. This proposed method was used to estimate vehicular emissions in the San Joaquin Valley, California.”
“A GIS framework for surface-layer soil moisture estimation combining satellite radar measurements and land surface modeling with soil physical property estimation.”
M. Tischler, M. Garcia, C. Peters-Lidard, M.S. Moran, S. Miller, D. Thoma, S. Kumar, and J. Geiger. Environmental Modelling & Software, Volume 22, Issue 6, June 2007, Pages 891-898, ISSN 1364-8152, DOI: 10.1016/j.envsoft.2006.05.022.
Abstract: “A GIS framework, the Army Remote Moisture System (ARMS), has been developed to link the Land Information System (LIS), a high performance land surface modeling and data assimilation system, with remotely sensed measurements of soil moisture to provide a high resolution estimation of soil moisture in the near surface. ARMS uses available soil (soil texture, porosity, Ksat), land cover (vegetation type, LAI, Fraction of Greenness), and atmospheric data (Albedo) in standardized vector and raster GIS data formats at multiple scales, in addition to climatological forcing data and precipitation. PEST (Parameter EStimation Tool) was integrated into the process to optimize soil porosity and saturated hydraulic conductivity (Ksat), using the remotely sensed measurements, in order to provide a more accurate estimate of the soil moisture. The modeling process is controlled by the user through a graphical interface developed as part of the ArcMap component of ESRI ArcGIS.”