This ArcGIS model inserts a file name into a feature class attribute table. The tool allows an user to identify features by a field that reference the name of the original file. It is useful when an user have to merge multiple feature classes and needs to identify which layer the features come from.

Raczynski, K., Grala, K., & Cartwright, J. H. (2024). GEO Tutorial: Work Automation in QGIS Using Model Builder. Mississippi State University: Geosystems Research Institute. [View Document] GEO TutorialNumber of Pages: 9Publication Date: 11/2024This work was supported through funding by the National Oceanic and Atmospheric Administration Regional Geospatial Modeling Grant, Award # NA19NOS4730207.

The Habitat Suitability Analysis was built using ArcGIS Pro's ModelBuilder tool. This program does not have an option to save the model's inputs as a relative file path. As a result, the model may not run because it's searching for each layer's original file path. If this happens, we have included a file titled Habitat_Suitability_Analysis_Script that outlines the processes we used to build the model. This submission contains three folders and three supplemental files. The folder titled "Data" includes all of the raw data and data input in the Habitat Suitability Analysis. The folder titled "Scripts" describes the steps to build the Habitat Suitability Analysis model in ArcGIS Pro. The Results folder contains the Habitat Suitability Analysis model and the data that was input into the model. The supplemental files are a file titled "Dryad_Folder_Contents" which describes the contents of every folder in this submission, and a file titled "Habitat_Suitability_Analysis_README" which contain...

This permit conversion tool converts ePermit .xls files to quarter-quarter or lat/long locations. Also included is a public lands survey geodatabase necessary to run the POU tool. This Model Builder toolset is available for ArcGIS 10.1-5. The March 2018 update provided here tests for field types and processes the fields accordingly.

Portions of the world's interior, such as central Asia are extremely secluded from the ocean and are more than 2,000 km from the nearest coast. Distance to coast can be used in asset management and modeling project costs. Phenomenon Mapped: Distance to coastUnits: KilometersCell Size: 655.9259912 metersSource Type: DiscretePixel Type: Signed integerSpatial Reference: World Equidistant CylindricalMosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: EsriPublication Date: 2015ArcGIS Server URL: https://oceans2.arcgis.com/arcgis/The Distance to Coast layer was calculated by Esri using the Euclidean Distance Tool in ArcMap and the Esri Country Boundaries layer.What can you do with this layer?Visualization: This layer can be used for visualization online in web maps and in ArcGIS Desktop.Analysis: This layer can be used as an input to geoprocessing tools and model builder.Raster Functions: Unit Conversion – kilometers to miles, Unit Conversion - kilometers to nautical miles, Cartographic Renderer, and Classified Renderer see this blog for more information.This layer is part of the Living Atlas of the World that provides access to thousands of beautiful and authoritative layers, web maps, and apps.

This service is available to all ArcGIS Online users with organizational accounts. For more information on this service, including the terms of use, visit us online at http://goto.arcgisonline.com/earthobs2/REMSS_SeaSurfaceTempSea Surface Temperature is a key climate and weather measurement used for weather prediction, ocean forecasts, tropical cyclone forecasts, and in coastal applications such as fisheries, pollution monitoring and tourism. El Niño and La Niña are two examples of climate events which are forecast through the use of sea surface temperature maps. The Naval Oceanographic Office sea surface temperature dataset is calculated from satellite-based microwave and infrared imagery. These data are optimally interpolated to provide a daily, global map of the midday (12:00 pm) sea surface temperature. Learn more about the source data. Phenomenon Mapped: Sea Surface TemperatureUnits: Degrees CelsiusTime Interval: DailyTime Extent: 2008/04/01 12:00:00 UTC to presentCell Size: 11 kmSource Type: ContinuousPixel Type: Floating PointData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: Global OceansSource: Naval Oceanographic OfficeUpdate Cycle: SporadicArcGIS Server URL: http://earthobs2.arcgis.com/arcgisTime: This is a time-enabled layer. It shows the average sea surface temperature during the map's time extent, or if time animation is disabled, a time range can be set using the layer's multidimensional settings. The map shows the average of all days in the time extent. Minimum temporal resolution is one day; maximum is one month.What can you do with this layer?Visualization: This layer can be used for visualization online in web maps and in ArcGIS Desktop.Analysis: This layer can be used as an input to geoprocessing tools and model builder. Units are in degrees Celsius, and there is a processing template to convert pixels to Fahrenheit. See this Esri blog post for more information on how to use this layer in your analysis. Do not use this layer for analysis while the Cartographic Renderer processing template is applied.This layer is part of the Living Atlas of the World that provides an easy way to explore the earth observation layers and many other beautiful and authoritative maps on hundreds of topics.

Landforms are large recognizable features such as mountains, hills and plains; they are an important determinant of ecological character, habitat definition and terrain analysis. Landforms are important to the distribution of life in natural systems and are the basis for opportunities in built systems, and therefore landforms play a useful role in all natural science fields of study and planning disciplines. Dataset SummaryPhenomenon Mapped: LandformsGeographic Extent: GlobalProjection: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereUnits: MetersCell Size: 231.91560581932 metersPixel Depth: 8-bit unsigned integerAnalysis: Restricted single source analysis. Maximum size of analysis is 30,000 x 30,000 pixels.Source: EsriPublication Date: May 2016ArcGIS Server URL: https://landscape7.arcgis.com/arcgis/ In February 2017, Esri updated the World Landforms - Improved Hammond Method service with two display functions: Ecological Land Units landform classes and Ecological Facets landform classes. This layer represents Ecological Facets landform classes. You can view the Ecological Land Units landform classes by choosing Image Display, and changing the Renderer. This layer was produced using the Improved Hammond Landform Classification Algorithm produced by Esri in 2016. This algorithm published and described by Karagulle et al. 2017: Modeling global Hammond landform regions from 250-m elevation data in Transactions in GIS. The algorithm, which is based on the most recent work in this area by Morgan, J. & Lesh, A. 2005: Developing Landform Maps Using Esri’s Model Builder., Esri converted Morgan’s model into a Python script and revised it to work on global 250-meter resolution GMTED2010 elevation data. Hammond’s landform classification characterizes regions rather than identifying individual features, thus, this layer contains sixteen classes of landforms:Nearly flat plains Smooth plains with some local relief Irregular plains with moderate relief Irregular plains with low hills Scattered moderate hills Scattered high hills Scattered low mountains Scattered high mountains Moderate hills High hills Tablelands with moderate relief Tablelands with considerable relief Tablelands with high relief Tablelands with very high relief Low mountains High mountains To produce these classes, Esri staff first projected the 250-meter resolution GMTED elevation data to the World Equidistant Cylindrical coordinate system. Each cell in this dataset was assigned three characteristics: slope based on 3-km neighborhood, relief based on 6 km neighborhood, and profile based on 6-km neighborhood. The last step was to overlay the combination of these three characteristics with areas that are exclusively plains. Slope is the percentage of the 3-km neighborhood occupied by gentle slope. Hammond specified 8% as the threshold for gentle slope. Slope is used to define how flat or steep the terrain is. Slope was classified into one of four classes: Percent of neighborhood over 8% of slopeSlope Classes0 - 20%40021% -50%30051% - 80%200>81% 100Local Relief is the difference between the maximum and minimum elevation within in the 6-km neighborhood. Local relief is used to define terrain how rugged or the complexity of the terrain"s texture. Relief was assigned one of six classes:Change in elevationRelief Class ID0 – 30 meters1031 meter – 90 meters2091 meter – 150 meters30151 meter – 300 meters40301 meter – 900 meters50>900 meters60The combination of slope and relief begin to define terrain as mountains, hills and plains. However, the difference between mountains or hills and tablelands cannot be distinguished using only these parameters. Profile is used to determine tableland areas. Profile identifies neighborhoods with upland and lowland areas, and calculates the percent area of gently sloping terrain within those upland and lowland areas. A 6-km circular neighborhood was used to calculate the profile parameter. Upland/lowland is determined by the difference between average local relief and elevation. In the 6-km neighborhood window, if the difference between maximum elevation and cell’s elevation is smaller than half of the local relief it’s an upland. If the difference between maximum elevation and cell’s elevation is larger than half of the local relief it’s a lowland. Profile was assigned one of five classes:Percent of neighborhood over 8% slope in upland or lowland areasProfile ClassLess than 50% gentle slope is in upland or lowland0More than 75% of gentle slope is in lowland150%-75% of gentle slope is in lowland250-75% of gentle slope is in upland3More than 75% of gentle slope is in upland4Early reviewers of the resulting classes noted one confusing outcome, which was that areas were classified as "plains with low mountains", or "plains with hills" were often mostly plains, and the hills or mountains were part of an adjacent set of exclusively identified hills or mountains. To address this areas that are exclusively plains were produced, and used to override these confusing areas. The hills and mountains within those areas were converted to their respective landform class. The combination of slope, relief and profile merged with the areas of plains, can be better understood using the following diagram, which uses the colors in this layer to show which classes are present and what parameter values produced them: What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. Restricted single source analysis means this layer has size constraints for analysis and it is not recommended for use with other layers in multisource analysis. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks. The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics. Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group. The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

1. The word document "Source and processing of GIS-RS input data" contains the information of the used Sentinel-2 and DEM data and details of the followed pre-processing steps.2. Calculation of weighted average: the Geodatabase and the Toolbox can be opened in ArcGis. The model in the "WeightedAverage" tbx contains the steps to calculate the values of four vegetation indices for each pixel portion inside each plot. The resulting attribute table can be copied to excel and values/plot can be calculated using the proportional area of each polygon in proportion to the plot area. Notes: a) some very small polygons will require manual filling of the VIs value. b) Check the new location (folder) of the inputs and outputs for each step of the model (model builder).3. The following geo files are input for the model builder in ArcGis (model in the tbx) that need to be transformed into a geodatabase "WeightedAverage":- Shapefiles: all files named "Buffer_Plots_2017", "FishNet10m", "FishNet_DEM12.5", "PlotPolygones", "FishDEM30".- Raster files: all files named "SAVI_Clip", "bi_Clip", "ireci_Clip", "mtci_Clip", "ndi45_Clip", "Slope30utm", "Slope_Degree".4. Models. The R script ModelsP1 contains the information on the model selection process and parameter estimation using K-fold using the examples of two models for two ecosystem services. Use the provided excel file as input. Date Copyrighted: 2020-06-01 Date Submitted: 2020-08-19

Our Co-design team is from the University of Texas, working on a Department of Energy-funded project focused on the Beaumont-Port Arthur area. As part of this project, we will be developing climate-resilient design solutions for areas of the region. More on www.caee.utexas.edu.We captured aerial photos in the Port Arthur Coastal Neighborhood Community and the Golf Course on Pleasure Island, Texas, in June 2024.Aerial photos taken were through DroneDeploy autonomous flight, and models were processed through the DroneDeploy engine as well. All aerial photos are in .JPG format and contained in zipped files for each area.The processed data package includes 3D models, geospatial data, mappings, and point clouds. Please be aware that DTM, Elevation toolbox, Point cloud, and Orthomosaic use EPSG: 6588. And 3D Model uses EPSG: 3857.For using these data:- The Adobe Suite gives you great software to open .Tif files.- You can use LASUtility (Windows), ESRI ArcGIS Pro (Windows), or Blaze3D (Windows, Linux) to open a LAS file and view the data it contains.- Open an .OBJ file with a large number of free and commercial applications. Some examples include Microsoft 3D Builder, Apple Preview, Blender, and Autodesk.- You may use ArcGIS, Merkaartor, Blender (with the Google Earth Importer plug-in), Global Mapper, and Marble to open .KML files.- The .tfw world file is a text file used to georeference the GeoTIFF raster images, like the orthomosaic and the DSM. You need suitable software like ArcView to open a .TFW file.This dataset provides researchers with sufficient geometric data and the status quo of the land surface at the locations mentioned above. This dataset could streamline researchers' decision-making processes and enhance the design as well.

Retirement Notice: This item is in mature support as of April 2024 and will be retired in December 2026. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.Sea Surface Temperature is a key climate and weather measurement used for weather prediction, ocean forecasts, tropical cyclone forecasts, and in coastal applications such as fisheries, pollution monitoring and tourism. El Niño and La Niña are two examples of climate events which are forecast through the use of sea surface temperature maps. The Naval Oceanographic Office sea surface temperature dataset is calculated from satellite-based microwave and infrared imagery. These data are optimally interpolated to provide a daily, global map of the midday (12:00 pm) sea surface temperature. Learn more about the source data. Phenomenon Mapped: Sea Surface TemperatureUnits: Degrees CelsiusTime Interval: Daily Time Extent: 2008/04/01 12:00:00 UTC to presentCell Size: 11 kmSource Type: ContinuousPixel Type: Floating PointData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: Global OceansSource: Naval Oceanographic OfficeArcGIS Server URL: https://earthobs2.arcgis.com/arcgis Time: This is a time-enabled layer. It shows the average sea surface temperature during the map's time extent, or if time animation is disabled, a time range can be set using the layer's multidimensional settings. The map shows the average of all days in the time extent. Minimum temporal resolution is one day; maximum is one month. What can you do with this layer? Visualization: This layer can be used for visualization online in web maps and in ArcGIS Desktop. Analysis: This layer can be used as an input to geoprocessing tools and model builder. Units are in degrees Celsius, and there is a processing template to convert pixels to Fahrenheit. Do not use this layer for analysis while the Cartographic Renderer processing template is applied.

Need to be able to automate the available child care facilities for the Arkansas Dept of Human Services to keep their list of facilities up to date. Eventually we would like to use model builder and python to avoid any human intervention, but in the meantime we can get by with just replacing the data set instead of totally rebuilding the symbology with each data update.

This layer is subset of World Ecological Facets Landform Classes Image Layer. Landforms are large recognizable features such as mountains, hills and plains; they are an important determinant of ecological character, habitat definition and terrain analysis. Landforms are important to the distribution of life in natural systems and are the basis for opportunities in built systems, and therefore landforms play a useful role in all natural science fields of study and planning disciplines.Dataset SummaryPhenomenon Mapped: LandformsUnits: MetersCell Size: 231.91560581932 metersSource Type: ThematicPixel Type: 8-bit unsigned integerData Coordinate System: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: EsriPublication Date: May 2016ArcGIS Server URL: https://landscape7.arcgis.com/arcgis/In February 2017, Esri updated the World Landforms - Improved Hammond Method service with two display functions: Ecological Land Units landform classes and Ecological Facets landform classes. This layer represents Ecological Facets landform classes. You can view the Ecological Land Units landform classes by choosing Image Display, and changing the Renderer. This layer was produced using the Improved Hammond Landform Classification Algorithm produced by Esri in 2016. This algorithm published and described by Karagulle et al. 2017: Modeling global Hammond landform regions from 250-m elevation data in Transactions in GIS.The algorithm, which is based on the most recent work in this area by Morgan, J. & Lesh, A. 2005: Developing Landform Maps Using Esri’s Model Builder., Esri converted Morgan’s model into a Python script and revised it to work on global 250-meter resolution GMTED2010 elevation data. Hammond’s landform classification characterizes regions rather than identifying individual features, thus, this layer contains sixteen classes of landforms:Nearly flat plainsSmooth plains with some local reliefIrregular plains with moderate relief Irregular plains with low hillsScattered moderate hillsScattered high hillsScattered low mountainsScattered high mountainsModerate hillsHigh hills Tablelands with moderate reliefTablelands with considerable reliefTablelands with high relief Tablelands with very high relief Low mountainsHigh mountainsTo produce these classes, Esri staff first projected the 250-meter resolution GMTED elevation data to the World Equidistant Cylindrical coordinate system. Each cell in this dataset was assigned three characteristics: slope based on 3-km neighborhood, relief based on 6 km neighborhood, and profile based on 6-km neighborhood. The last step was to overlay the combination of these three characteristics with areas that are exclusively plains. Slope is the percentage of the 3-km neighborhood occupied by gentle slope. Hammond specified 8% as the threshold for gentle slope. Slope is used to define how flat or steep the terrain is. Slope was classified into one of four classes: Percent of neighborhood over 8% of slopeSlope Classes0 - 20%40021% -50%30051% - 80%200>81% 100Local Relief is the difference between the maximum and minimum elevation within in the 6-km neighborhood. Local relief is used to define terrain how rugged or the complexity of the terrain's texture. Relief was assigned one of six classes:Change in elevationRelief Class ID0 – 30 meters1031 meter – 90 meters2091 meter – 150 meters30151 meter – 300 meters40301 meter – 900 meters50>900 meters60The combination of slope and relief begin to define terrain as mountains, hills and plains. However, the difference between mountains or hills and tablelands cannot be distinguished using only these parameters. Profile is used to determine tableland areas. Profile identifies neighborhoods with upland and lowland areas, and calculates the percent area of gently sloping terrain within those upland and lowland areas. A 6-km circular neighborhood was used to calculate the profile parameter. Upland/lowland is determined by the difference between average local relief and elevation. In the 6-km neighborhood window, if the difference between maximum elevation and cell’s elevation is smaller than half of the local relief it’s an upland. If the difference between maximum elevation and cell’s elevation is larger than half of the local relief it’s a lowland. Profile was assigned one of five classes:Percent of neighborhood over 8% slope in upland or lowland areasProfile ClassLess than 50% gentle slope is in upland or lowland0More than 75% of gentle slope is in lowland150%-75% of gentle slope is in lowland250-75% of gentle slope is in upland3More than 75% of gentle slope is in upland4Early reviewers of the resulting classes noted one confusing outcome, which was that areas were classified as "plains with low mountains", or "plains with hills" were often mostly plains, and the hills or mountains were part of an adjacent set of exclusively identified hills or mountains. To address this areas that are exclusively plains were produced, and used to override these confusing areas. The hills and mountains within those areas were converted to their respective landform class.The combination of slope, relief and profile merged with the areas of plains, can be better understood using the following diagram, which uses the colors in this layer to show which classes are present and what parameter values produced them:What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group.The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

Marine life have certain thresholds for temperature that they can live in. For instance, deep-water corals have been recorded in temperatures of -1⁰C. Seafloor temperatures generally decrease with increasing depth. Phenomenon Mapped: Seafloor temperatureUnits: Degrees CelsiusCell Size: 30 arc seconds, approximately 1 kmSource Type: DiscretePixel Type: Signed integerSpatial Reference: GCS_WGS_1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: Global oceansSource: Marine Conservation Institute (MCI)Citation: Boyer TP, Levitus S, Garcia HE, Locamini RA, Stephens C, et al. (2005) Objective analyses of annual, seasonal, and monthly temperature and salinity for the World Ocean on a 0.25° grid. International Journal of Climatology 25: 931–945.Publication Date: 2005ArcGIS Server URL: https://oceans2.arcgis.com/arcgis/The Marine Conservation Institute used this dataset as an input to a predictive habitat model documented in the publication Global Habitat Suitability for Framework-Forming Cold-Water Corals.What can you do with this layer?Visualization: This layer can be used for visualization online in web maps and in ArcGIS Desktop.Analysis: This layer can be used as an input to geoprocessing tools and model builder.Raster Functions: Unit Conversion – Celsius to Fahrenheit, Unit Conversion – Celsius to Kelvin, and Cartographic Renderer - see this blog for more information.This layer is part of the Living Atlas of the World that provides access to thousands of beautiful and authoritative layers, web maps, and apps.

Ocean depth plays an important role in the distribution and abundance of living organisms and has important implications for shipping and offshore development projects such as wind power and oil extraction.Phenomenon Mapped: Seafloor depth, bathymetryUnits: Meters below sea levelCell Size: 30 arc seconds, approximately 1 kmSource Type: DiscretePixel Type: Signed integerSpatial Reference: GCS_WGS_1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: Global oceansSource: Marine Conservation Institute (MCI)Citation: Becker JJ, Sandwell DT, Smith WHF, Braud J, Binder B, et al. (2009) Global bathymetry and elevation data at 30 arc seconds resolution: SRTM30_PLUS. Marine Geodesy 32: 355–371.Publication Date: 2009ArcGIS Server URL: https://oceans2.arcgis.com/arcgis/The Marine Conservation Institute used this dataset as an input to a predictive habitat model documented in the publication Global Habitat Suitability for Framework-Forming Cold-Water Corals.The source data is available from the Scripps Institution of Oceanography Satellite Geodesy Webpage.What can you do with this layer?Visualization: This layer can be used for visualization online in web maps and in ArcGIS Desktop.Analysis: This layer can be used as an input to geoprocessing tools and model builder.Raster Functions: Unit Conversion – meters to feet, Cartographic Renderer, Aspect, Slope, and Hillshade - see this blog for more information.This layer is part of the Living Atlas of the World that provides access to thousands of beautiful and authoritative layers, web maps, and apps.

This data is the global urban boundary of 222 scenarios combined by different population data sets, population density and population size thresholds, and the GIS model builder for calculating these data. See the README file for details.

This data is the global urban boundary of 222 scenarios combined by different population data sets, population density and population size thresholds, and the GIS model builder for calculating these data. See the README file for details.

Includes settings for a CPI (Crop Production Index) Generation toolbox and instructions on how to alter it to be usable for your county.This documentation assumes the user has a basic understanding of ArcGIS, its tools, and its data structure, Model Builder. Arcade and Python Scripting used here will be covered in the documentation.

Portions of the world's oceans are extremely remote including areas in the South Pacific that are more the 2,500 km from the nearest land. Distance from shore can be used in asset management, modeling project costs, and as an index of human influence. Phenomenon Mapped: Distance from shoreUnits: KilometersCell Size: 655.9259912 metersSource Type: DiscretePixel Type: Signed integerSpatial Reference: World Equidistant CylindricalMosaic Projection: Web Mercator Auxiliary SphereExtent: Global oceansSource: EsriPublication Date: 2015ArcGIS Server URL: https://oceans2.arcgis.com/arcgis/The Distance from Shore layer was calculated by Esri using the Euclidean Distance Tool in ArcMap and the Esri Country Boundaries layer.What can you do with this layer?Visualization: This layer can be used for visualization online in web maps and in ArcGIS Desktop.Analysis: This layer can be used as an input to geoprocessing tools and model builder.Raster Functions: Unit Conversion – kilometers to miles, Unit Conversion - kilometers to nautical miles, Cartographic Renderer, and Classified Renderer see this blog for more information.This layer is part of the Living Atlas of the World that provides access to thousands of beautiful and authoritative layers, web maps, and apps.

1. IntroductionThe lower cost of sensors is making possible the acquisition of big data sets in several applications and research areas [1]. Indoor air quality and commuter exposure to pollutants are some of these areas, which can have impacts on our livelihood. We considered activities that happen outdoors, like public or private transportation while commuting, as an indoor environment that can accumulate CO2, mainly as a result of the metabolic process in humans. This is why CO2 is often considered a good indicator of inefficient ventilation. Concentrations above 1000 ppm fall within this category.Figure 1. Graphical abstractSome studies have been able to register peak concentrations above the recommended 5000 ppm CO2 [2] inside Hong Kong buses [3]. However, these were sustained for short periods of time and do not pose a concerning health risk. Despite this, recent findings suggest that concentrations as low as 950 ppm, sustained for an 8-hour workday, can cause loss of 15% in performance during cognitive tests, although it is recognized that further research is needed to isolate CO2 as the single culprit [4].In order to know if we are exposed to a certain type of pollutant and for how long, we must first be able to quantify it. In air quality monitoring this is not accessible to just anyone as reliable heavy-duty sensors can cost upwards of thousands of euros. These costly sensors offer extremely accurate measurements but suffer from low temporal resolution and low spatial density. This is why in recent years, as technology improves and gets miniaturized, a copious amount of low-cost sensors (LCS) have flooded the market with acceptable performances at very low prices. It is recognized that they are still not on par with high-cost sensors when it comes to accuracy and reliability; however, the gap is being narrowed year by year as advancements both in technology and research are made. Correlation coefficients above 0.75 are not uncommon.2. ObjectivesThe main objective of this exploratory research was to assemble portable equipment along with an in-house prototype, low-cost and easy to replicate in any location worldwide. In the published work [5], in which this summary is based on, it was answered how CO2, noise and energy expenditure compare in transportation modes with indoor environments (metro, bus and car). Assessments on commuting times and search for correlations and trends between the parameters were also made.3. MethodsThe assembled equipment made use of an Arduino Uno, an SD-card module and a low-cost sensor (Sensirion SCD30). These components, along with a smartwatch and a smartphone, were capable of quantifying personal exposure to CO2, noise and also measure heart rate.The exploratory field campaign was conducted on an urban commuting route, in Lisbon city, between Rossio (downtown of Lisbon city) and Campo Grande (near FCUL campus) during 6 weeks.At the end of the campaign, all the usable data, mainly from CO2 and HR, in CSV format, was integrated into a geospatial database using Esri© technology (ArcMap 10.5) with the intent of producing maps that compare modes of transportation, morning versus afternoon and school break versus school period. Considerable quantities of data required the use of ArcMap model builder to speed up the process as seen in figure 2. All the CO2 and HR data was merged from multiple CSV sheets into a single shapefile that was then used to search for hot spots using the Getis and Ord cluster tool. Some experiments with the make route event layer tool were done to try and visualize metro underground data. The utilization of such tool is crucial to recreate the underground trips, where global positioning systems (GPS) cannot reach. This is an extremely useful tool that was tested with and despite our success using it, the maps produced revealed some inherent methodology constraints which will be fixed in future iterations.Nonetheless, for the remainder of the data, all the maps proved invaluable to help corroborate any trend or correlation with observational data as well as categorize the modes of transportation in the study.Figure 2. Model Builder for Bus and Car hot spot analysis (metro was discarded).4. ResultsThe commuter made a total of 35 round-trips, 12 were made by metro, 11 by bus and another 12 by car. This corresponds to a total of 70 one-way measurement trips. Sample sizes for each variable were as follows: 32794 for CO2, temperature and humidity, 27825 for HR and 297521 for noise. Noise and metro measurements were excluded from the results section as the utilization of GIS revealed some aspects of the methodology that could be improved. Next it is highlighted some of the valid GIS results obtained. Figure 3. Bus CO2 hot spots. All morning Figure 4. Bus CO2 hot spots. All afternoon data.In figure 3 and 4, it can be seen that in afternoon commutes, the occurrence of hot spots is more sparse. In the morning commutes however, there is a stronger prevalence of hot spots between Restauradores and Av. da República. It was in these areas that the highest CO2 concentration of 2190 ppm was registered. Even though this concentration was not enough to surpass the 5000 ppm registered in Hong Kong buses [3] or pose a concerning health risk [6], it is still significant and indicates that the bus suffers from inefficient ventilation during certain parts of the commute [7].Figure 5. Heart rate hot spots of all Bus morning data.The map seen in figure 5, was useful to corroborate observational data of overcrowding periods. Along with a correlations table seen in the full article [5], it was realized that these HR hot spots have no direct correlation with CO2 seen in figures 3 and 4, but instead seem related with the specific overcrowding periods.5. ConclusionsThe bus has big portions of its commute time spent in the inefficient ventilation category. It is advisable that the A/C and/or air circulation should be turned to a higher setting during these moments. With the GIS analysis, it could be stated that high concentrations of CO2 were prevalent between Restauradores and Av. da República, something that would not have been possible to affirm by simple using a temporal analysis. As previously stated, bus HR hot spots were correlated with the overcrowding periods and not the CO2 variable [5]. During these periods, the commuter would usually be standing or maneuvering through the crowd that is entering and exiting the bus. As expected, it can be seen more examples of heart rate hot spots while entering and exiting.The car CO2 measurements were considered uneventful for the most part and were mainly dependent on the windows being opened or not. Some indications of traffic light stops trends with CO2 and HR could be seen in some periods, but revised methodology is required to make sure our affirmations are solid. Nonetheless, it was considered the worst mode of transportation in the study as it goes against the idea of a less congested and clean city. Also, the car reached CO2 concentrations close to the values in the bus (2000 ppm) with only one passenger.The expansion of this project is underway and is intend to have more commuters (10 minimum), routes, transportation modes and sensors (Ozone - O3 and Nitrogen Oxides - NOx) collecting personal exposure data. We will also have a prototype replica calibrated for 6 months in-situ versus very high cost sensors (10,000€+) to further test the sensors capabilities.The strength of this project relies on its endless possibilities. The novelty of measuring these particular parameters together and acquiring big datasets fits perfectly with GIS analysis tools. The author adds that there is a strong case to be made about the under-utilization of software technologies, like ArcMap, for these purposes. It was denoted, while conducting research, that many mobile air quality stations researchers don’t use the full potential of GIS technologies or end-up using mobile apps that do a disservice to the quality of the data obtained. The use of such tools made it possible to vastly improve our methodology offering ways to speed up the process of data analysis (e.g. model builder). It also presented new ideas on ways to produce underground metro maps using the make route event layer. The reason the metro was not included in the GIS analysis was because we did not carefully timestamp all underground movements. This is something that will be present in the next iteration of the project as it is very easy to implement. We will be able to produce hot spot analysis of underground measurements with high fidelity and compare it with the other modes of transportation.References1. Kumar, P.; Morawska, L.; Martani, C.; Biskos, G.; Neophytou, M.; Di Sabatino, S.; Bell, M.; Norford, L.; Britter, R. The rise of low-cost sensing for managing air pollution in cities, 2015. doi:10.1016/j.envint.2014.11.019.2. WHO/Europe. WHO guidelines for indoor air quality: selected pollutants. Bonn, Germany: in puncto druck+ medien GmbH 2010. doi:10.1186/2041-1480-2-S2-I1.3. Chan, A.T. Commuter exposure and indoor-outdoor relationships of carbon oxides in buses in Hong Kong. Atmospheric Environment 2003. doi:10.1016/S1352-2310(03)00465-5.4. Vehviläinen, T.; Lindholm, H.; Rintamäki, H.; Pääkkönen, R.; Hirvonen, A.; Niemi, O.; Vinha, J. High indoor CO2 concentrations in an office environment increases the transcutaneous CO2 level and sleepiness during cognitive work. Journal of Occupational and Environmental Hygiene 2016. doi:10.1080/15459624.2015.1076160.5. Soares, A.; Catita, C.; Silva, C. Exploratory Research of CO2, Noise and Metabolic Energy Expenditure in Lisbon Commuting. MDPI 2020. doi:https://doi.org/10.3390/en13040861.6. COHEN, JOEL M.. PETERSON, R.D. COMPLETE GUIDE TO OSHA COMPLIANCE; CRC PRESS, 2019.7. ANSI. ANSI/ASHRAE Standard 62.1-2010, Ventilation for Acceptable Indoor Air Quality. Ashrae 2007.doi:ANSI/ASHRAE Standard 62.1-2004.

The purpose of the Tool is to use CPI, CER (Crop Equivalency Rating) or NCCPI (National Commodity Crop Productivity Index) to assess tax values accurately and fairly, regarding tillable acres of land. The steps in this document will focus solely on CPI values, but can be modified to use CER or NCCPI data. This project was created by counties for counties to use as a no cost solution for assessing tax values to tillable acres.This documentation assumes the user has a basic understanding of ArcGIS, its tools, and its data structure, Model Builder, and basic Arcade and Python Scripting.