ABSTRACT This paper discusses a multi-criteria GIS-based optimization model, which aims to determine the locations with the highest potential for the location of the water mains through the use of cost variables, as well as the best path for this tracing. As a result, it was possible to simulate minimum cost routes for the pipeline layout, considering criteria related to: the slope and altitude of the area, the distances of rivers and flooded areas and the proximity of highways. The analysis takes into account the importance (weight) of each criterion in the model. To minimize subjectivity in choosing the values of these weights, expert opinion was sought regarding the criteria analyzed. The HWA (Hierarchical Weight Analysis) method was used to weigh the criteria. To apply the methodology, the study area used an excerpt from the Pajeú pipeline in the state of Pernambuco and a high definition database from the Pernambuco Three-dimensional Program, as well as the SRTM/TOPODATA database. The results obtained through GIS allowed us to identify the areas considered to be the most suitable for the location of the pipeline and to determine an optimized route for this route. In practice, it meant determining a route for the pipeline installation, which suggests that the use of GIS and optimization techniques can help decision making regarding the design of water supply systems.

The downloadable ZIP file contains model documentation and contact information for the model creator. For more information, or a copy of the project report which provides greater model detail, please contact Ryan Urie - traigo12@gmail.com.This model was created from February through April 2010 as a central component of the developer's master's project in Bioregional Planning and Community Design at the University of Idaho to provide a tool for identifying appropriate locations for various land uses based on a variety of user-defined social, economic, ecological, and other criteria. It was developed using the Land-Use Conflict Identification Strategy developed by Carr and Zwick (2007). The purpose of this model is to allow users to identify suitable locations within a user-defined extent for any land use based on any number of social, economic, ecological, or other criteria the user chooses. The model as it is currently composed was designed to identify highly suitable locations for new residential, commercial, and industrial development in Kootenai County, Idaho using criteria, evaluations, and weightings chosen by the model's developer. After criteria were chosen, one or more data layers were gathered for each criterion from public sources. These layers were processed to result in a 60m-resolution raster showing the suitability of each criterion across the county. These criteria were ultimately combined with a weighting sum to result in an overall development suitability raster. The model is intended to serve only as an example of how a GIS-based land-use suitability analysis can be conceptualized and implemented using ArcGIS ModelBuilder, and under no circumstances should the model's outputs be applied to real-world decisions or activities. The model was designed to be extremely flexible so that later users may determine their own land-use suitability, suitability criteria, evaluation rationale, and criteria weights. As this was the first project of its kind completed by the model developer, no guarantees are made as to the quality of the model or the absence of errorsThis model has a hierarchical structure in which some forty individual land-use suitability criteria are combined by weighted summation into several land-use goals which are again combined by weighted summation to yield a final land-use suitability layer. As such, any inconsistencies or errors anywhere in the model tend to reveal themselves in the final output and the model is in a sense self-testing. For example, each individual criterion is presented as a raster with values from 1-9 in a defined spatial extent. Inconsistencies at any point in the model will reveal themselves in the final output in the form of an extent different from that desired, missing values, or values outside the 1-9 range.This model was created using the ArcGIS ModelBuilder function of ArcGIS 9.3. It was based heavily on the recommendations found in the text "Smart land-use analysis: the LUCIS model." The goal of the model is to determine the suitability of a chosen land-use at each point across a chosen area using the raster data format. In this case, the suitability for Development was evaluated across the area of Kootenai County, Idaho, though this is primarily for illustrative purposes. The basic process captured by the model is as follows: 1. Choose a land use suitability goal. 2. Select the goals and criteria that define this goal and get spatial data for each. 3. Use the gathered data to evaluate the quality of each criterion across the landscape, resulting in a raster with values from 1-9. 4. Apply weights to each criterion to indicate its relative contribution to the suitability goal. 5. Combine the weighted criteria to calculate and display the suitability of this land use at each point across the landscape. An individual model was first built for each of some forty individual criteria. Once these functioned successfully, individual criteria were combined with a weighted summation to yield one of three land-use goals (in this case, Residential, Commercial, or Industrial). A final model was then constructed to combined these three goals into a final suitability output. In addition, two conditional elements were placed on this final output (one to give already-developed areas a very high suitability score for development [a "9"] and a second to give permanently conserved areas and other undevelopable lands a very low suitability score for development [a "1"]). Because this model was meant to serve primarily as an illustration of how to do land-use suitability analysis, the criteria, evaluation rationales, and weightings were chosen by the modeler for expediency; however, a land-use analysis meant to guide real-world actions and decisions would need to rely far more heavily on a variety of scientific and stakeholder input.

Click to downloadClick for metadataService URL: https://gis.dnr.wa.gov/site2/rest/services/Public_Forest_Practices/WADNR_PUBLIC_FP_Water_Type/MapServer/4For large areas, like Washington State, download as a file geodatabase. Large data sets like this one, for the State of Washington, may exceed the limits for downloading as shape files, excel files, or KML files. For areas less than a county, you may use the map to zoom to your area and download as shape file, excel or KML, if that format is desired.The DNR Forest Practices Wetlands Geographic Information System (GIS) Layer is based on the National Wetlands Inventory (NWI). In cooperation with the Washington State Department of Ecology, DNR Forest Practices developed a systematic reclassification of the original USFWS wetlands codes into WAC 222-16-035 types. The reclassification was done in 1995 according to the Forest Practice Rules in place at the time. The WAC's for defining wetlands are 222-16-035 and 222-16-050.The DNR Forest Practices Wetlands Geographic Information System (GIS) Layer is based on the National Wetlands Inventory (NWI). In cooperation with the Washington State Department of Ecology, DNR Forest Practices developed a systematic reclassification of the original USFWS wetlands codes into WAC 222-16-035 types. The reclassification was done in 1995 according to the Forest Practice Rules in place at the time. The WAC's for defining wetlands are 222-16-035 and 222-16-050.It is intended that these data be only a first step in determining whether or not wetland issues have been or need to be addressed in an area. The DNR Forest Practices Division and the Department of Ecology strongly supports the additional use of hydric soils (from the GIS soils layer) to add weight to the call of 'wetland'. Reports from the Department of Ecology indicate that these data may substantially underestimate the extent of forested wetlands. Various studies show the NWI data is 25-80% accurate in forested areas. Most of these data were collected from stereopaired aerial photos at a scale of 1:58,000. The stated accuracy is that of a 1:24,000 map, or plus or minus 40 feet. In addition, some parts of the state have data that are 30 years old and only a small percentage have been field checked. Thus, for regulatory purposes, the user should not rely solely on these data. On-the-ground checking must accompany any regulatory call based on these data.The reclassification is based on the USFWS FWS_CODE. The FWS_CODE is a concatenation of three subcomponents: Wetland system, class, and water regime. Forest Practices further divided the components into system, subsystem, class, subclass, water regime, special modifiers, xclass, subxclass, and xsystem. The last three items (xsomething) are for wetland areas which do not easily lend themselves to one class alone. The resulting classification system uses two fields: WLND_CLASS and WLND_TYPE. WLND_CLASS indicates whether the polygon is a forested wetland (F), open water (O), or a vegetated wetland (W). WLND_TYPE, indicates whether the wetland is a type A (1), type B (2), or a generic wetland (3) that doesn't fit the categories for A or B type wetlands. WLND_TYPE = 0 (zero) is used where WLND_CLASS = O (letter "O").

The wetland polygon is classified as F, forested wetland; O, open water; or W, vegetated wetland depending on the following FWS_CODE categories: F O W --------------------------------------------------- Forested Open Vegetated Wetland Water Wetland --------------------------------------------PFO* POW PUB5 E2FO PRB* PML2 PUB1-4 PEM* PAB* L2US5 PUS1-4 L2EM2 PFL* PSS* L1RB* PML1 L1UB*
L1AB* L1OW L2RB* L2UB* L2AB* L2RS* L2US1-4 L2OW

• indicates inclusion of the subcategory (ie. PEM* includes PEM1F, PEM1FB, etc.).

DNR FOREST PRACTICES WETLANDS DATASET ON FPARS Internet Mapping Website: The FPARS Resource Map and Water Type Map display Forested, Type A, Type B, and "other" wetlands. Open water polygons are not displayed on the FPARS Resource Map and Water Type Map in an attempt to minimize clutter. The following code combinations are found in the DNR Forest Practices wetlands dataset:

WLND_CLASS WLND_TYPE wetland polygon classification F 3 Forested wetland as defined in WAC 222-16-035 O 0 *NWI open water (not displayed on FPARS Resource or Water Type Maps) W 1 Type A Wetland as defined in WAC 222-16-035 W 2 Type B Wetland as defined in WAC 222-16-035 W 3 other wetland

• NWI open water polygons are indicated by WLND_CLASS = O and WLND_TYPE = 0. Open water is used in the USFWS and WAC 222-16-035 classification system. These open water polygons are not included in the FPARS Resource Map and Water Type Map views of this dataset in an attempt to minimize clutter on the FPARS maps.

This dataset contains birth information for the state of Michigan in 2014. Included are births by ethnicity, number of births with inadequate prenatal care, number of low weight births, and births to teen mothers. Inadequate prenatal care was defined as births rated "Intermediate" or "Inadequate" on the Kessner Scale. Infants weighing under 2,500 grams were considered a low weight birth. Teen mothers were defined as mothers under the age of 20. Michigan Office of Vital Statistics provided individual birth data which was then suppressed by Data Driven Detroit.

The North American Rail Network (NARN) Rail Lines dataset was created in 2016 and was updated on July 18, 2025 from the Federal Railroad Administration (FRA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The NARN Rail Lines dataset is a database that provides ownership, trackage rights, type, passenger, STRACNET, and geographic reference for North America's railway system at 1:24,000 or better within the United States. The data set covers all 50 States, the District of Columbia, Mexico, and Canada. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1528950

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

This metadata contains data for two models: AWRA-L and AWRA-R. In the Macquarie-Tuggerah-Lake (MTL) coastal subregion, only AWRA-L modelling was conducted; in the Hunter subregion both modelling were conducted and AWRA-L flow outputs provided as model inputs for AWRA-R.

AWRA-L

The metadata within the dataset contains the workflow, processes, inputs and outputs data. The workflow pptx file under the top folder provides the top level summary of the modelling framework, including three slides. The first slide explains how to generate global definition file; the second slide outlines the calibration and simulation for AWRA-L model run; the third slide shows AWRA-L model post-processing for getting streamflow under baseline and coal mine resources development.

The exectable model framework is under the Application subfolder

Other subfolders, including model calibration, model simulation, post processing, contain the associated files used for model calibration, simulation and post processing, respectively.

Documentation about the implementation of AWRA-L in the Hunter bioregion is provided in BA HUN 2.6.1.3 and 2.6.1.4 products.

AWRA-R

The metadata within the dataset contains the workflow, processes, input and output data and instructions to implement the Hunter AWRA-R model for model calibration or simulation.

Each sub-folder in the associated data has a readme file indicating folder contents and providing general instructions about the workflow performed.

Detailed documentation of the AWRA-R model, is provided in: https://publications.csiro.au/rpr/download?pid=csiro:EP154523&dsid=DS2

Documentation about the implementation of AWRA-R in the Hunter bioregion is provided in BA HUN 2.6.1.3 and 2.6.1.4 products.

Purpose

BA surface water modelling in the Hunter bioregion

Dataset History

There are two sections, the first section describing AWRA-L, the second section describing AWRA-R.

Section 1 - AWRA-L

The directories within contain the input and output data of the Hunter AWRA-L model for model calibration, simulation and post-processing.

The calibration folder contains the input and output subfolders used for two model calibration schemes: lowflow and normal. The lowflow model calibration puts more weight on median and low streamflow; the normal model calibration puts more weight on high streamflow.

The simulation folder contains only one replicate of model input and output as an example.

The post-processing folder contains three subfolders: inputs, outputs and scripts used for generating streamflow under the baseline and coal mine resources development conditions. It contains the post-processing for the two subregions (MTL and HUN). In the MTL coastal subregion, the AWRA-L postprocessing results were final outputs, while in the HUN subregion AWRA-L flow outputs were model inputs for AWRA-R (Details below).

Input and output files are the daily data covering the period of 1953 to 2102, with the first 30 years (1953-1982) for model spin-up.

Documentation about the implementation of AWRA-L in the Hunter bioregion is provided in BA HUN 2.6.1.3 and 2.6.1.4 products.

Data details are below

Model calibrations

1. Climate forcings are under '... AWRAL_Metadata\model calibration\inputs\Climate\'

2. Lowflow calibration data including catchment location, global definition mapping, objective definition and optimiser definition under '... AWRAL_Metadata\model calibration\inputs\lowflow\'

3. Higflow calibration data including catchment location, global definition mapping, objective definition and optimiser definition under '... AWRAL_Metadata\model calibration\inputs
   ormal\'

4. Observed streamflow data used for model calibrations are under '... AWRAL_Metadata\model calibration\inputs\Streamflow\'

Model simulations

1. Climate forcings are under '... AWRAL_Metadata\model simulation\inputs\Climate\'

2. Global definition file used in csv output mode data is under '... AWRAL_Metadata\model simulation\inputs\csv_Model_1\'

3. Global definition file used in netcdf output mode data is under '... AWRAL_Metadata\model simulation\inputs\Netcdf_Model_1\'

4. Output files used in csv output mode data contain Dd, dgw, E0, Qg, Qtot, Rain, Sg outputs, which is used for AWRA-R model input and is under '... AWRAL_Metadata\model simulation\outputs\csv_Model_1\'

5. Output files used in netcdf output mode data contain Qg and Qtot outputs, which is used for AWRA-L postprocessing and is under '... AWRAL_Metadata\model simulation\outputs\Netcdf_Model_1\'

Post-processing

1. Input data include AWRA-L streamflow, ground water baseflow input and mine footprint data, stored at '... AWRAL_Metadata\post processing\Inputs\'

2. Output data include streamflow outputs under crdp and baseline for the HUN and MTL subregions, stored at '... AWRAL_Metadata\post processing\Outputs\'

3. Scripts for use for post-processing AWRA-L streamflow and ground water baseflow, is under '... AWRAL_Metadata\model simulation\post processing\Scripts\'

Section 2 - AWRA-R

The directories within contain the input data and outputs of the Hunter AWRA-R model for model calibration or simulation. The folders were calibration data stored is used as an example,

simulation uses mirror files of these data, albeit with longer time-series depending on the simualtion period.

Detailed documentation of the AWRA-R model, is provided in: https://publications.csiro.au/rpr/download?pid=csiro:EP154523&dsid=DS2

Documentation about the implementation of AWRA-R in the Hunter bioregion is provided in BA HUN 2.6.1.3 and 2.6.1.4 products.

Additional data needed to generate some of the inputs needed to implement AWRA-R are detailed in the corresponding metadata statement as stated below.

Input data needed:

1. Gauge/node topological information in '...\model calibration\HUN4_low\gis\sites\AWRARv5.00_reaches.csv'.

2. Look up table for soil thickness in '...\model calibration\HUN4_low\gis\ASRIS_soil_properties\HUN_AWRAR_ASRIS_soil_thickness_v5.00.csv'. (check metadata statement)

3. Look up tables of AWRA-LG groundwater parameters in '...\model calibration\HUN4_low\gis\AWRA-LG_gw_parameters\'.

4. Look up table of AWRA-LG catchment grid cell contribution in '...model calibration\HUN4_low\gis\catchment-boundary\AWRA-R_catchment_x_AWRA-L_weight.csv'. (check metadata statement)

5. Look up tables of link lengths for main river, tributaries and distributaries within a reach in \model calibration\HUN4_low\gis\rivers\'. (check metadata statement)

6. Time series data of AWRA-LG outputs: evaporation, rainfall, runoff and depth to groundwater.

7. Gridded data of AWRA-LG groundwater parameters, refer to explanation in '...'\model calibration\HUN4_low\rawdata\AWRA_LG_output\gw_parameters\README.txt'.

8. Time series of observed or simulated reservoir level, volume and surface area for reservoirs used in the simulation: Glenbawn Dam and Glennies Creek Dam.

   located in '...\model calibration\HUN4_low\rawdata\reservoirs\'.

9. Gauge station cross sections in '...\model calibration\HUN4_low\rawdata\Site_Station_Sections\'. (check metadata statement)

10. Daily Streamflow and level time-series in'...\model calibration\HUN4_low\rawdata\streamflow_and_level_all_processed\'.

11. Irrigation input, configuration and parameter files in '...\AWRAR_Metadata\model calibration\HUN4_low\inputs\HUN\irrigation\'. These come from the separate calibration of the AWRA-R irrigation module in:

    '...\irrigation calibration\', refer to explanation in readme.txt file therein.

12. Dam simulation script '\AWRAR_Metadata\dam model calibration simulation\scripts\Hunter_dam_run_2.R' and configuration files in

    '\AWRAR_Metadata\dam model calibration simulation\scripts\Hunter_dam_config_2.csv'. The config file comes from a separate calibration of AWRA-R dam module in

    '\AWRAR_Metadata\dam model calibration simulation\', refer to the explanation in the readme.txt file therein

Relevant ouputs include:

1. AWRA-R time series of stores and fluxes in river reaches ('...\AWRAR_Metadata\model calibration\HUN4_low\outputs\jointcalibration\v01\HUN\simulations\')

   including simulated streamflow in files denoted XXXXXX_full_period_states_nonrouting.csv where XXXXXX denotes gauge or node ID.

2. AWRA-R time series of stores and fluxes for irrigation/mining in the same directory as above in files XXXXXX_irrigation_states.csv

3. AWRA-R calibration validation goodness of fit metrics ('...\AWRAR_Metadata\model calibration\HUN4_low\outputs\jointcalibration\v01\HUN\postprocessing\')

   in files calval_results_XXXXXX_v5.00.csv

Dataset Citation

Bioregional Assessment Programme (XXXX) HUN AWRA-LR Model v01. Bioregional Assessment Derived Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/670de516-30c5-4724-bd76-8ff4a42ca7a5.

Dataset Ancestors

• Derived From Hunter River Salinity Scheme Discharge NSW EPA 2006-2012

• Derived From River Styles Spatial Layer for New South Wales

• Derived From HUN AWRA-L simulation nodes_v01

• Derived From HUN AWRA-R River Reaches Simulation v01

• Derived From [HUN AWRA-R simulation

This layer shows Households by Type. This is shown by state and county boundaries. This service contains the 2018-2022 release of data from the American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show Average Household Size and the Total Households in a bi-variate map. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2018-2022ACS Table(s): B11001, B25010, B25044, DP02, DP04Data downloaded from: Census Bureau's API for American Community Survey Date of API call: January 18, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico. The Counties (and equivalent) layer contains 3221 records - all counties and equivalent, Washington D.C., and Puerto Rico municipios. See Areas Published. Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes. All of these are rendered in this dataset as null (blank) values.

The power plant locations and characteristics are part of the California Energy Commission’s (CEC) California Energy Infrastructure geospatial data sets. The data is derived from the CEC’s QFER-1304 Power Plant Owner Reporting Database and is updated annually. Among other information, a number of identifying attributes are given for each power plant as well as the generator units at each plant, their energy type, the total nameplate capacity, and their owners and operators.

This California Power Plants data set has identical information to the many tables making up the QFER data set, however this single feature layer is derived by condensing several QFER tables into one. Some fields of the original tables have been omitted, and point geometries, determined by each plants’ address fields, have been appended for geospatial display. Four new fields have been compiled from QFER’s Annual Generation Table. These are listed and defined as:

• Nameplate Capacity (MW): The total nameplate capacity from every unit that makes up the power plant, regardless of status
• Units: List of the unit names at each power plant
• Primary Energy Source: A list of the primary energy sources used by every generator at the plant
• Last Reported Year: The last year that the power plant was recorded in the Annual Generation Table.

Primary Energy Source Descriptions:

For the purposes of the map, a Truck is defined as a vehicle with a gross vehicle weight rating greater than 26,000 pounds with a carrying capacity of more than 1 and ¼ ton. For the purposes of the map, a Bus is defined as a vehicle with a carrying capacity of greater than 15 passengers. This does not include WMATA or Circulator buses. A through route, or primary route, signifies a roadway that may be used by trucks and buses that have neither an origin or destination within the city or on the route. A non-through route (all other roadways not highlighted in blue), signifies a roadway that that may be used by trucks or buses only for the purpose of making a delivery or pickup of passengers or freight. Trucks and buses should only use a non-through route at the beginning or end of a trip, when traveling between their origin/destination and a through route.