This layer was created by performing a union of tidal marsh vegetation types from the CoastalWetlandPoly3 layer (provided to the County by Friends of the San Juans) and the county's tidal wetlands layer. New classifications were provided by Paul Adamus. Acres were calculated using the calculate geometry tool.

Spin energetics is one of the biggest challenges associated with energy calculations for electronic structure methods. The energy differences of the spin states in spin-crossover compounds are very small, making them one of the most difficult systems to calculate. Few methods provide accurate results for calculating these energy differences. In addition, studies have usually focused on calculating energetics of single molecules, while spin-crossover properties are usually experimentally studied in the solid phase. In this paper, we have used periodic boundary conditions employing methods based on density functional theory to calculate the high- and low-spin energy differences for a test case of 20 extended systems. Compounds with different metals and ligands have been selected, and the results indicate that a semiquantitative description of the energy differences can be obtained with the combination of geometry optimization using the PBE functional including many-body dispersion approach and the use of meta-GGA functionals, such as r2SCAN but especially KTBM24, for the energy calculation. Other hybrid functionals, such as TPSSh, give generally good results, but the calculation of the exact exchange with periodic boundary conditions involves a huge increase in computer time and computational resources. It makes the proposed nonhybrid functional approach (KTBM24//PBE+MB) a great advantage for the study of periodic systems.

The here shown data results from a methodology for calculating the similarity of CAD parts. CAD parts are analyzed using various algorithms to find geometric features and discrete point cloud representations. The folder AnalyzedCADData contains a zip-file with the raw analyzed data of each part. All evaluated results can be found in folder EvaluationOfData in HDF5 formattet tables. An overview of the data set and the analyzed geometric features is given by an interactive HTML diagram in folder FeatureDiagram.

The dipole moment is a simple descriptor of the charge distribution and polarity and is important for understanding and predicting various molecular properties. Semiempirical (SE) methods offer a cost-effective way to calculate dipole moment that can be used in high-throughput screening applications although the accuracy of the methods is still in question. Therefore, we have evaluated AM1, GFN0-xTB, GFN1-xTB, GFN2-xTB, PM3, PM6, PM7, B97-3c, HF-3c, and PBEh-3c SE methods, which cover a variety of SE approximations, to directly assess the performance of SE methods in predicting molecular dipole moments and their directions using 7211 organic molecules contained in the QM7b database. We find that B97-3c and PBEh-3c perform best against coupled-cluster reference values yielding dipole moments with a mean absolute error (MAE) of 0.10 and 0.11 D, respectively, which is similar to the MAE of density functional theory (DFT) methods (∼0.1 D) reported earlier. Analysis of the atomic composition shows that all SE methods show good performance for hydrocarbons for which the spread of error was within 1 D of the reference data, while the worst performances are for sulfur-containing compounds for which only B97-3c and PBEh-3c show acceptable performance. We also evaluate the effect of SE optimized geometry, instead of the benchmark DFT geometry, that shows a dramatic drop in the performance of AM1 and PM3 for which the range of error tripled. Based on our overall findings, we highlight that there is an optimal compromise between accuracy and computational cost using GFN2-xTB (MAE: 0.25 D) that is 3 orders of magnitude faster than B97-3c and PBEh-3c. Thus, we recommend using GFN2-xTB for cost-efficient calculation of the dipole moment of organic molecules containing C, H, O, and N atoms, whereas, for sulfur-containing organic molecules, we suggest PBEh-3c.

Regional- and continental-scale models predicting variations in the magnitude and timing of streamflow are important tools for forecasting water availability as well as flood inundation extent and associated damages. Such models must define the geometry of stream channels through which flow is routed. These channel parameters, such as width, depth, and hydraulic resistance, exhibit substantial variability in natural systems. While hydraulic geometry relationships have been extensively studied in the United States, they remain unquantified for thousands of stream reaches across the country. Consequently, large-scale hydraulic models frequently take simplistic approaches to channel geometry parameterization. Over-simplification of channel geometries directly impacts the accuracy of streamflow estimates, with knock-on effects for water resource and hazard prediction.

Here, we present a hydraulic geometry dataset derived from long-term measurements at U.S. Geological Survey (USGS) stream gages across the conterminous United States (CONUS). This dataset includes (a) at-a-station hydraulic geometry parameters following the methods of Leopold and Maddock (1953), (b) at-a-station Manning's n calculated from the Manning equation, (c) daily discharge percentiles, and (d) downstream hydraulic geometry regionalization parameters based on HUC4 (Hydrologic Unit Code 4). This dataset is referenced in Heldmyer et al. (2022); further details and implications for CONUS-scale hydrologic modeling are available in that article (https://doi.org/10.5194/hess-26-6121-2022).

At-a-station Hydraulic Geometry

We calculated hydraulic geometry parameters using historical USGS field measurements at individual station locations. Leopold and Maddock (1953) derived the following power law relationships:

\(w={aQ^b}\)

\(d=cQ^f\)

\(v=kQ^m\)

where Q is discharge, w is width, d is depth, v is velocity, and a, b, c, f, k, and m are at-a-station hydraulic geometry (AHG) parameters. We downloaded the complete record of USGS field measurements from the USGS NWIS portal (https://waterdata.usgs.gov/nwis/measurements). This raw dataset includes 4,051,682 individual measurements from a total of 66,841 stream gages within CONUS. Quantities of interest in AHG derivations are Q, w, d, and v. USGS field measurements do not include d--we therefore calculated d using d=A/w, where A is measured channel area. We applied the following quality control (QC) procedures in order to ensure the robustness of AHG parameters derived from the field data:

1. We considered only measurements which reported Q, v, w and A.
2. For each gage, we excluded measurements older than the most recent five years, so as to minimize the effects of long-term channel evolution on observed hydraulic geometry relationships.
3. We excluded gages for which measured Q disagreed with the product of measured velocity and measured area by more than 5%. Gages for which \( Q eq vA\) are often tidally influenced and therefore may not conform to expected channel geometry relationships.
4. Q, v, w, and d from field measurements at each gage were log-transformed. We performed robust linear regressions on the relationships between log(Q) and log(w), log(v), and log(d). AHG parameters were derived from the regressed explanatory variables.
   1. We applied an iterative outlier detection procedure to the linear regression residuals. Values of log-transformed w, v, and d residuals falling outside a three median absolute deviation (MAD) envelope were excluded. Regression coefficients were recalculated and the outlier detection procedure was reapplied until no new outliers were detected.
   2. Gages for which one or more regression had p-values >0.05 were excluded, as the relationships between log-transformed Q and w, v, or d lacked statistical significance.
   3. Gages were omitted if regressed AHG parameters did not fulfill two additional relationships derived by Leopold and Maddock: \(b+f+m=1{\displaystyle \pm }0.1\) and \(a{\displaystyle \times }c{\displaystyle \times }k=1{\displaystyle \pm }0.1\).
5. If the number of field measurements for a given gage was less than 10, either initially or after individual measurements were removed via steps 1-4, the gage was excluded from further analysis.

Application of the QC procedures described above removed 55,328 stream gages, many of which were short-term campaign gages at which very few field measurements had been recorded. We derived AHG parameters for the remaining 11,513 gages which passed our QC.

At-a-station Manning's n

We calculated hydraulic resistance at each gage location by solving Manning's equation for Manning's n, given by

\(n = {{R^{2/3}S^{1/2}} \over v}\)

where v is velocity, R is hydraulic radius and S is longitudinal slope. We used smoothed reach-scale longitudinal slopes from the NHDPlusv2 (National Hydrography Dataset Plus, version 2) ElevSlope data product. We note that NHDPlusv2 contains a minimum slope constraint of 10^-5 m/m--no reach may have a slope less than this value. Furthermore, NHDPlusv2 lacks slope values for certain reaches. As such, we could not calculate Manning's n for every gage, and some Manning's n values we report may be inaccurate due to the NHDPlusv2 minimum slope constraint. We report two Manning's n values, both of which take stream depth as an approximation for R. The first takes the median stream depth and velocity measurements from the USGS's database of manual flow measurements for each gage. The second uses stream depth and velocity calculated for a 50th percentile discharge (Q₅₀; see below). Approximating R as stream depth is an assumption which is generally considered valid if the width-to-depth ratio of the stream is greater than 10—which was the case for the vast majority of field measurements. Thus, we report two Manning's n values for each gage, which are each intended to approximately represent median flow conditions.

Daily discharge percentiles

We downloaded full daily discharge records from 16,947 USGS stream gages through the NWIS online portal. The data includes records from both operational and retired gages. Records for operational gages were truncated at the end of the 2018 water year (September 30, 2018) in order to avoid use of preliminary data. To ensure the robustness of daily discharge percentiles, we applied the following QC:

1. For a given gage, we removed blocks of missing discharge values longer than 6 months. These long blocks of missing data generally correspond to intervals in which a gage was temporarily decommissioned for maintenance.
2. A gage was omitted from further analysis if its discharge record was less than 10 years (3,652 days) long, and/or less than 90% complete (>10% missing values after removal of long blocks in step 1.

We calculated discharge percentiles for each of the 10,871 gages which passed QC. Discharge percentiles were calculated at increments of 1% between Q₁ and Q₅, increments of 5% (e.g. Q₁₀, Q₁₅, Q₂₀, etc.) between Q₅ and Q₉₅, increments of 1% between Q₉₅ and Q₉₉, and increments of 0.1% between Q₉₉ and Q₁₀₀ in order to provide higher resolution at the lowest and highest flows, which occur much less frequently.

HG Regionalization

We regionalized AHG parameters from gage locations to all stream reaches in the conterminous United States. This downstream hydraulic geometry regionalization was performed using all gages with AHG parameters in each HUC4, as opposed to traditional downstream hydraulic geometry--which involves interpolation of parameters of interest to ungaged reaches on individual streams. We performed linear regressions on log-transformed drainage area and Q at a number of flow percentiles as follows:

\(log(Q_i) = \beta_1log(DA) + \beta_0\)

where Q_i is streamflow at percentile i, DA is drainage area and \(\beta_1\) and \(\beta_0\) are regression parameters. We report \(\beta_1\), \(\beta_0\) , and the r² value of the regression relationship for Q percentiles Q₁₀, Q₂₅, Q₅₀, Q₇₅, Q₉₀, Q₉₅, Q₉₉, and Q_99.9. Further discussion and additional analysis of HG regionalization are presented in Heldmyer et al. (2022).

Dataset description

We present the HyG dataset in a comma-separated value (csv) format. Each row corresponds to a different USGS stream gage. Information in the dataset includes gage ID (column 1), gage location in latitude and longitude (columns 2-3), gage drainage area (from USGS; column 4), longitudinal slope of the gage's stream reach (from NHDPlusv2; column 5), AHG parameters derived from field measurements (columns 6-11), Manning's n calculated from median measured flow conditions (column 12), Manning's n calculated from Q50 (column 13), Q percentiles (columns 14-51), HG regionalization parameters and r² values (columns 52-75), and geospatial information for the HUC4 in which the gage is located (from USGS; columns 76-87). Users are advised to exercise caution when opening the dataset. Certain software, including Microsoft Excel and Python, may drop the leading zeros in USGS gage IDs and HUC4 IDs if these columns are not explicitly imported as strings.

Errata

In version 1, drainage area was mistakenly reported in cubic meters but labeled in cubic kilometers. This error has been corrected in version 2.

pockmarks are defined as depressions on the seabed and are usually formed by fluid expulsions. recently discovered, pockmarks along the aquitaine slope within the french eez, were manually mapped although two semi-automated methods were tested without convincing results. in order to potentially highlight different groups and possibly discriminate the nature of the fluids involved in their formation and evolution, a morphological study was conducted, mainly based on multibeam data and in particular bathymetry from the marine expedition gazcogne1, 2013. bathymetry and seafloor backscatter data, covering more than 3200 km², were acquired with the kongsberg em302 ship-borne multibeam echosounder of the r/v le suroît at a speed of ~8 knots, operated at a frequency of 30 khz and calibrated with ©sippican shots. precision of seafloor backscatter amplitude is +/- 1 db. multibeam data, processed using caraibes (©ifremer), were gridded at 15x15 m and down to 10x10 m cells, for bathymetry and seafloor backscatter, respectively. the present table includes 11 morphological attributes extracted from a geographical information system project (mercator 44°n conserved latitude in wgs84 datum) and additional parameters related to seafloor backscatter amplitudes. pockmark occurrence with regards to the different morphological domains is derived from a morphological analysis manually performed and based on gazcogne1 and bobgeo2 bathymetric datasets.the pockmark area and its perimeter were calculated with the “calculate geometry” tool of arcmap 10.2 (©esri) (https://desktop.arcgis.com/en/arcmap/10.3/manage-data/tables/calculating-area-length-and-other-geometric-properties.htm). a first method to calculate pockmark internal depth developed by gafeira et al. was tested (gafeira j, long d, diaz-doce d (2012) semi-automated characterisation of seabed pockmarks in the central north sea. near surface geophysics 10 (4):303-315, doi:10.3997/1873-0604.2012018). this method is based on the “fill” function from the hydrology toolset in spatial analyst toolbox arcmap 10.2 (©esri), (https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/fill.htm) which fills the closed depressions. the difference between filled bathymetry and initial bathymetry produces a raster grid only highlighting filled depressions. thus, only the maximum filling values which correspond to the internal depths at the apex of the pockmark were extracted. for the second method, the internal pockmark depth was calculated with the difference between minimum and maximum bathymetry within the pockmark.latitude and longitude of the pockmark centroid, minor and major axis lengths and major axis direction of the pockmarks were calculated inside each depression with the “zonal geometry as table” tool from spatial analyst toolbox in arcgis 10.2 (©esri) (https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/zonal-statistics.htm). pockmark elongation was calculated as the ratio between the major and minor axis length.cell count is the number of cells used inside each pockmark to calculate statistics (https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/zonal-geometry.htm). cell count and minimum, maximum and mean bathymetry, slope and seafloor backscatter values were calculated within each pockmark with “zonal statistics as table” tool from spatial analyst toolbox in arcgis 10.2 (©esri). slope was calculated from bathymetry with “slope” function from spatial analyst toolbox in arcgis 10.2 (©esri) and preserves its 15 m grid size (https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/slope.htm). seafloor backscatter amplitudes (minimum, maximum and mean values) of the surrounding sediments were calculated within a 100 m buffer around the pockmark rim.

Scanned-energy mode photoelectron diffraction (PhD) and ab initio density functional theory calculations have been employed to investigate the adsorption geometry of benzoate ([C6H5COO]−) on rutile-TiO2(110)(1 × 1). PhD data indicate that the benzoate moiety binds to the surface through both of its oxygen atoms to two adjacent fivefold surface titanium atoms in an essentially upright geometry. Moreover, its phenyl (C6H5−) and carboxylate ([−COO]−) groups are determined to be coplanar, being aligned along the [001] azimuth. This experimental result is consistent with the benzoate geometry emerging from DFT calculations conducted for laterally rather well-separated adsorbates. At shorter interadsorbate distances, the theoretical modeling predicts a more tilted and twisted adsorption geometry, where the phenyl and carboxylate groups are no longer coplanar; i.e., interadsorbate interactions influence the configuration of adsorbed benzoate.

This data was pulled from the BLM's MLRS database for each state using the case code (Transmission 285002, 285003, 285004, 285005, 285011, 231109, 231110, 234400, 285130) (Solar 283101, 283102, 283103, 283104) (Wind 283001, 283002, 283003, 283004) . The data was joined with the calculated centroid for each section in the states presented. Some sections did not have the proper designation or a point for plotting and in those instances the developer made every attempt to make a point in the logical place.For each state the first division of the PLSS for each state was obtained from either: a local government agency, the BLM Navigator, or from the USGS. Data was pulled in November of 2021. A snapshot of mining claims listings in each state from the BLM’s MLRS online database (Date Specified on Mining Claims Map) For each state, the projection of the PLSS layer is the projection that was used to create the claim points. From the PLSS first division for each state, the centroid was calculated using the calculate geometry function in ArcMap. A SectionID field was added to generate unique values. These unique values consist of the Meridian, Township, Range, and Section identifiers formatted to match the MTRS field when pulling the mining claims listings. Fields where concatenated together to generate the Section ID. ROWs with a status of Active, Pending, and Interim were queried from the Bureau of Land Management’s MLRS online database using the CR Case Information - Customer and Land. The ROW data was joined with the SectionID data to assign an easting and a northing, based on the MTRS description for the given ROW from the MLRS database. A ROW points feature class was generated using the coordinates from the centroid of the section it is listed to be within. Some ROWs did not plot. ROWs that did not may have fallen in areas that were previously visually inspected when generating claims layers and modifications were made if possible. The reason for claims, plans or notices not plotting was due to protracted blocks and the absence of a first division polygon. It is assumed this may be the case for some ROWs. The section numbers for protracted blocks are greater than 36, so in areas where claims were present on protracted blocks, the section numbers were reassigned the section number of which the general public would refer to it as (1-36 only). For any states where the first division was not available for a Township, section centroid points were made with the INFERRED PLSS description assigned to the points. Understand that assumptions were made during this process. Polygons were not made for missing sections.

A scarcity of known chemical kinetic parameters leads to the use of many reaction rate estimates, which are not always sufficiently accurate, in the construction of detailed kinetic models. To reduce the reliance on these estimates and improve the accuracy of predictive kinetic models, we have developed a high-throughput, fully automated, reaction rate calculation method, AutoTST. The algorithm integrates automated saddle-point geometry search methods and a canonical transition state theory kinetics calculator. The automatically calculated reaction rates compare favorably to existing estimated rates. Comparison against high level theoretical calculations show the new automated method performs better than rate estimates when the estimate is made by a poor analogy. The method will improve by accounting for internal rotor contributions and by improving methods to determine molecular symmetry.

Subduction zones are home to the most seismically active faults on the planet. The shallow megathrust interface of subduction zones host our largest earthquakes, and are the only faults capable of M9+ ruptures. Despite these facts, our knowledge of subduction zone geometry - which likely plays a key role in determining the spatial extent and ultimately the size of subduction zone earthquakes - is incomplete. Here we calculate the three- dimensional geometries of all active global subduction zones. The resulting model - Slab2 - provides for the first time a comprehensive geometrical analysis of all known slabs in unprecedented detail.

This data was pulled from the BLM's MLRS database for each state using the case code (380910,380913,380911). The data was joined with the calculated centroid for each section in the states presented. Some sections did not have the proper designation or a point for plotting and in those instances the developer made every attempt to make a point in the logical place.For each state the first division of the PLSS for each state was obtained from either: a local government agency, the BLM Navigator, or from the USGS. Data was pulled in November of 2021. A snapshot of mining claims listings in each state from the BLM’s MLRS online database (Date Specified on Mining Claims Map) For each state, the projection of the PLSS layer is the projection that was used to create the claim points. From the PLSS first division for each state, the centroid was calculated using the calculate geometry function in ArcMap. A SectionID field was added to generate unique values. These unique values consist of the Meridian, Township, Range, and Section identifiers formatted to match the MTRS field when pulling the mining claims listings. Fields where concatenated together to generate the Section ID. Mining claims with a status of Active, Pending, Submitted, and Filed claims were queried from the Bureau of Land Management’s MLRS online database using the PUB MC Serial Number Index under the Public Mining Claims Reports. The claims data was joined with the SectionID data to assign an easting and a northing, based on the MTRS description for the given claim from the MLRS database. A “claim point listings” feature class was generated using the coordinates from the centroid of the section it is listed to be within. Some plans or notices did not plot. plans or notices that did not plot were visually inspected by and modifications were made if possible, to display the plans or notices. The reason for plans or notices not plotting was due to protracted blocks and the absence of a first division polygon. The section numbers for protracted blocks are greater than 36, so in areas where claims were present on protracted blocks, the section numbers were reassigned the section number of which the general public would refer to it as (1-36 only). For any states where the first division was not available for a Township, section centroid points were made with the INFERRED PLSS description assigned to the points. Understand that assumptions were made during this process. Polygons were not made for missing sections.

This data was pulled from the BLM's MLRS database for each state using the case code (Transmission 285002, 285003, 285004, 285005, 285011, 231109, 231110, 234400, 285130) (Solar 283101, 283102, 283103, 283104) (Wind 283001, 283002, 283003, 283004) . The data was joined with the calculated centroid for each section in the states presented. Some sections did not have the proper designation or a point for plotting and in those instances the developer made every attempt to make a point in the logical place.For each state the first division of the PLSS for each state was obtained from either: a local government agency, the BLM Navigator, or from the USGS. Data was pulled in November of 2021. A snapshot of mining claims listings in each state from the BLM’s MLRS online database (Date Specified on Mining Claims Map) For each state, the projection of the PLSS layer is the projection that was used to create the claim points. From the PLSS first division for each state, the centroid was calculated using the calculate geometry function in ArcMap. A SectionID field was added to generate unique values. These unique values consist of the Meridian, Township, Range, and Section identifiers formatted to match the MTRS field when pulling the mining claims listings. Fields where concatenated together to generate the Section ID. ROWs with a status of Active, Pending, and Interim were queried from the Bureau of Land Management’s MLRS online database using the CR Case Information - Customer and Land. The ROW data was joined with the SectionID data to assign an easting and a northing, based on the MTRS description for the given ROW from the MLRS database. A ROW points feature class was generated using the coordinates from the centroid of the section it is listed to be within. Some ROWs did not plot. ROWs that did not may have fallen in areas that were previously visually inspected when generating claims layers and modifications were made if possible. The reason for claims, plans or notices not plotting was due to protracted blocks and the absence of a first division polygon. It is assumed this may be the case for some ROWs. The section numbers for protracted blocks are greater than 36, so in areas where claims were present on protracted blocks, the section numbers were reassigned the section number of which the general public would refer to it as (1-36 only). For any states where the first division was not available for a Township, section centroid points were made with the INFERRED PLSS description assigned to the points. Understand that assumptions were made during this process. Polygons were not made for missing sections.

[Background]: In-situ gamma-ray measurements employ radiation detectors to measure the environmental gamma spectrum, and it is necessary to calculate the conversion factor that translates the characteristic peak count rate of the measured spectrum into the specific activity of radionuclides in surrounding materials. The calculation of the conversion factor essentially involves integrating the detection efficiency of gamma rays emitted from different spatial positions within the surrounding materials. However, numerical solutions for the integral are challenging in complex geometries, limiting the method’s application. [Purpose]: This study aims to overcome the limitation of the integral method, simplify the calculation and broaden the scope of applications. [Methods]: We applied the monte carlo simulation technique to calculate the integral. First, we built the geometry of the surrounding materials in the Geant4. Then, we sampled and emitted Geantinos facing the detector from the surroundings. Afterwards, we logged the positions when the Geantinos passing the boundaries. Finally, we calculated the parameters for the integral, such as the track lengths, and got the conversion factor by calculating the average of the integral items of all particles. [Results]: We applied this method to calculate the conversion factors for the building materials of the large polyethylene shielding chamber in the second phase of the China Jinping Underground Laboratory (CJPL). The conversion factors for the 1-meter-thick polyethylene walls and an external 20 cm thick concrete support were determined, which allowed us to assess the shielding capability of the polyethylene chamber against various nuclide backgrounds in the external concrete, providing support and reference for in-situ measurements in the second phase of CJPL. [Conclusions]: We propose a novel method to calculate the conversion factor for in-situ gamma-ray measurement. This method overcomes the challenges of complex geometric integrations through random sampling and improves Monte Carlo simulation efficiency by transporting virtual particles, offering versatility in geometry and computational efficiency. We applied this method to the calculation of the conversion factor of the 1-meter-thick polyethylene walls, the geometry of which is very complex, for the in-situ measurement at CJPL. And in this case, the computing efficiency is increased by more than 5 times.

These address data are updated, typically by request, to City of San Marcos Planning and Development Services on a daily to weekly basis. Updates occur as new parcel plats are recorded, as building footprints change, when new service equipment such as cell towers and meters is installed, to bring existing address points into compliance with CAPCOG 911-Addressing guidelines, and as needed for various other circumstances.The 911 addresses (denoted in the Address911 field as “Y”) follow the CAPCOG (Capital Area Council of Governments) Addressing Guidelines (10-28-09) available here: http://www.capcog.org/divisions/emergency-communications/911-technology/(last accessed March 30, 2017).Non-911 addresses (denoted in the Address911 field as “N”) are maintained for location finding, public infrastructure inventory, and for various other circumstances. Location finding address points includes all intersection, 100 block numbers, and mile markers.There are two types of new addresses, In-fill and Subdivisions. In-fill addressing occurs in already developed areas that experience change. The Planning and Development Services Planning Technician updates and maintains the infill addresses, often in coordination with the City of San Marcos Fire Marshal’s office. Planning and Development Services 911 Address Coordinator creates new subdivision addressing. This feature exists in DevServices.sde. Field Information:OBJECTID- System-generated unique identifier for each record within the feature classMAXIMOID- unique identifier tie for public services asset management software; field is auto populated by IT GIS scriptMAXIMOIDPFX- unique identifier with prefix indicating (ADDR) feature tie for public services asset management software; field is auto populated by IT GIS scriptSAN- Site Address Number, assigned based on CAPCOG guidelines; alias: ADDRESSPRD- Prefix Directional (N, S, E, W); alias: PREFIX DIRECTIONSTN- Street Name; alias: STREET NAME; domain: ST_TYPESTS- Street Suffix; alias: STREET TYPEUNIT_NUM- FULLADDR- all caps concatenation of PRD + STN + STS (field calculate with this expression: ucase ([SAN] &" "& [PRD]&" "& [STN]&" "& [STS])UNIT TYPE*- values include: APT, ACSRY, BLDG, CLBHSE, CONDO, DUP, STE- these values , ; domain: ServUnitTypeZIP CODE- Zipcode- currently all 78666 COUNTY- Hays, Caldwell, Comal, or GuadalupeADDINFO*- used to add information about address, such as Business or Complex name or address type SF (single-family), intersection, etc.; alias DESCRIPTIONADDRESS911- yes or no value distinguishes 911 addresses from non-911 addresses; domain: YORNPOINT_X- Calculated geometry for “X Coordinate of Point” in PCS: NAD 1983 StatePlane Texas South Central FIPS 4204 Feet using Decimal DegreesPOINT_Y- Calculated geometry for “X Coordinate of Point” in PCS: NAD 1983 StatePlane Texas South Central FIPS 4204 Feet using Decimal DegreesCREATEDBY- system generated value based on log in ID CREATEDDATE- system generated value in UTMMODIFIEDBY- system generated value based on log in IDMODIFIEDDATE- system generated value in UTMSHAPE System-generated geometry type of the featureADDRESS_TYPE*- used to add information about addressGlobalID-System-generated unique identifier for each record that is required in replicated geodatabases*Indicate field is not consistent. The feature is under audit and overhaul in 2017 and 2018. Project will encompass and establish specific, consistent descriptors, update and add domains, compare and correct, as needed, consistency with these features: AptSteNum, Condo, Apartment, MFHousing, Parcel, Building, Centerline and Street address ranges

Lease valuations for 1, 2 or 3 lives

Absorbed doses to the lens (sensitive and insensitive regions) in the JPF phantom for photon irradiation in AP geometry.

We provide the computational output files of all calculations needed for the structure determination as well as high level coupled cluster reference calculations. For the B3LYP-D3(BJ), PBE0-D3(BJ), CAM-B3LYP-D3(BJ), LC-ωPBE-D3(BJ), M06-2X, B2PLYP-D3(BJ), DSD-PBEP86-D3(BJ) and MP2 methods, this includes vibrational perturbation theory calculations of second order (VPT2) for the parent (all isotopes in their naturally most abundant form), singly 13C substituted and singly 18O substituted species. 18O data is only available for citraconic anhydride. These calculations have ben carried out with Gaussian 16 (Rev. C.01).

Abstract

The dataset was derived by the Bioregional Assessment Programme. This dataset is a clip of the Spatial Data Conversion of the Atlas of Australian Soils to the Australian Soil Classification v01 (GUID:6f804e8b-2de9-4c88-adfa-918ec327c32f) to the Gippsland Basin Bioregion Boundary (GUID: 27413de5-d13a-4231-ac79-fc77f4cbb5f7). You can find a link to the parent datasets in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived.

This dataset contains the Spatial Data Conversion of the Atlas of Australian Soils clipped to the Gippsland Basin bioregion.

Dataset History

The dataset was derived by the Bioregional Assessment Programme. This dataset was derived from multiple datasets. You can find a link to the parent datasets in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived.

The Digital Atlas of Australian Soils (GUID: 6f804e8b-2de9-4c88-adfa-918ec327c32f) was clipped by the Gippsland project boundary (GUID: e8a2d577-c5c5-4e2c-b0fa-53e4b2d4a034) in ESRI ArcMap 10.2 using the 'Extract by Mask (Spatial Analyst) tool'.

Soil type statistics were derived from the clipped dataset by:

1. Converting the clipped dataset to Albers Equal Area gda94 projection and then dissolving the polygons based on the Soil field.

2. Adding the field AREA (type: Double) and using the Calculate Geometry tool to calculate the area of each polygon in square kilometers.

3. Calculating the total area using the statistics tool. Area = 14115.682417.

4. Adding the field PERC_AREA (type: Float) and calculating the percentage covered by each soil type using the following formula in the Field Calculator: (\[AREA\]/14115.682417)\*100

\*Note that the final table in context statement does not add up to 100% because the lake category of the soils dataset has been ignored.

Dataset Citation

Bioregional Assessment Programme (2014) Digital Atlas of Australian Soils - Gippsland Basin bioregion clip. Bioregional Assessment Derived Dataset. Viewed 29 September 2017, http://data.bioregionalassessments.gov.au/dataset/4728df66-acab-407e-9b4e-7dc031aa2687.

Dataset Ancestors

• Derived From Digital Atlas of Australian Soils

• Derived From Conversion of the Atlas of Australian Soils to the Australian Soil Classification

• Derived From Gippsland Project boundary

• Derived From Spatial Data Conversion of the Atlas of Australian Soils to the Australian Soil Classification v01

• Derived From GEODATA TOPO 250K Series 3

• Derived From Victoria - Seamless Geology 2014

It has been widely recognised that Light Detection And Ranging (LiDAR) data is a valuable resource for estimating the geometry of natural and artificial features. While the LiDAR point cloud data can be extremely detailed and difficult to use for the recognition and extraction of three dimensional objects, the Digital Elevation Model and Digital Surface Model are useful for rapidly estimating the horizontal extent of features and the height variations across those features. This has utility in describing the characteristics of buildings or other artificial structures.

LiDAR is an optical remote sensing technology that can measure the distance from the sensor to a target area by illuminating the target area with light, often using pulses from a laser scanner. LiDAR has many applications in a broad range of fields, including aiding in mapping features beneath forest canopies, creating high resolution digital elevation and surface models. A Digital Surface Model (DSM) represents the earth's surface and includes all objects on it, while the Digital Elevation Model (DEM) represents the bare ground surface without any natural or artificial objects such as vegetation, structures and buildings.

The Building Geometry Model (BGM) application is a Python-based software system, used to execute ArcGIS geoprocessing routines developed by Geoscience Australia, which can derive the horizontal and vertical extents and geometry information of building and other elevated features from LiDAR data. The Building Geometry Model algorithms were developed in response to the availability of LiDAR data for the development of exposure information for natural hazard risk analysis. The LiDAR derivatives were used to estimate building footprint areas, inter-storey heights across areas occupied by buildings, and eventually an estimate of gross floor area of different types of buildings.

The design and development of the BGM application started in February 2012 as part of a natural hazard risk analysis project in the Philippines. Many of the examples of interface usage in this document contain references to locations and terms used in the Philippines. However, the BGM application has been designed to process data regardless of its geographic location. The object-oriented programming techniques and design patterns were used in the software design and development. In order to provide users with a convenient interface to run the application on Microsoft® Windows, a Python-based Graphical User Interface (GUI) was implemented in March 2012 and significantly improved in the subsequent months. The application can be either run as a command-line program or start via the GUI.

The original Version 1.0 of the BGM has been replaced by Version 1.1, which incorporates changes to both the geoprocessing methods and the GUI.

In the geoprocessing methods for Version 1.1, the method for calculating the extent of blue roof areas has been improved, which ultimately improves the estimation of vegetation extents. In this version, the user now also has the ability to specify additional datasets that can be used to mask out features from the calculations (such as elevated structures that are not buildings).

As a result of changes to the GUI in Version 1.1, the user can now:

• Specify the new threshold for the blue roof values in the new Blue Roof Unmask;
• Designate band numbers and colours specific to the aerial imagery being used;
• Control the NDVI threshold used for determining vegetation extents from aerial imagery;
• Specify one or more additional masking datasets.

Minor changes to the temporary/intermediate file names have also been made.

This document is a user guide to the BGM GUI. It describes the main User Interface (UI) components, functionality and procedures for running the BGM processes via GUI.