This app introduces the key concepts of Map Algebra with a special focus on explaining the following operators:local operatorsfocal operatorszonal operatorsglobal operatorsThe understanding of the above-mentioned operators is facilitated through several exercises dedicated to calculating normalized Digital Surface Model (nDSM), slope and stream network layers. The new layers will be created using ArcGIS Desktop tools. The following datasets are used in these exercises:Digital Elevation Model (DEM) with 1 m resolutionDigital Surface Model (DSM) with 1 m resolutionBoth datasets have been published as image service and therefore they can be manipulated and used as input in further spatial analysis tasks.

Abstract Arithmetic map operations are very common procedures used in GIS to combine raster maps resulting in a new and improved raster map. It is essential that this new map be accompanied by an assessment of uncertainty. This paper shows how we can calculate the uncertainty of the resulting map after performing some arithmetic operation. Actually, the propagation of uncertainty depends on a reliable measurement of the local accuracy and local covariance, as well. In this sense, the use of the interpolation variance is proposed because it takes into account both data configuration and data values. Taylor series expansion is used to derive the mean and variance of the function defined by an arithmetic operation. We show exact results for means and variances for arithmetic operations involving addition, subtraction and multiplication and that it is possible to get approximate mean and variance for the quotient of raster maps.

The USGS Shaded Relief service from The National Map was created using data from the 3D Elevation Program, which maintains a seamless dataset of best available raster elevation data for the conterminous United States, Alaska, Hawaii, and Territorial Islands of the US. Derived using a hill shade technique, this base map represents a continental view showing shaded relief from USGS elevation layers at resolutions of 1/3-, 1-, and 2-arc-second (in Alaska only). This hill shade is actually five separate shaded relief datasets created from the original terrain data. Each shaded relief has different azimuths and altitude values as follows: 0 45, 135 60, 270 45, 315 45, 45 45. These five datasets are then combined into one feature class using map algebra to compute the raster layers using the following equation (shadedrelief1 + shadedrelief2 + shadedrelief3 + (shadedrelief4 x 2) + shaded relief5 / 6). This equation gives double importance to the 315 degrees azimuth and 45 degrees altitude. Color characteristics are the result of applying a color ramp in which RGB values range from near-white (RGB: 255, 255, 252) to brown (RGB: 156, 142, 107). The color ramp was applied with a stretch type of 4.3 standard deviations, since the scale of brightness values range from 130 to 1240. Contrast (24 percent) and brightness (3 percent) enhancements were applied for cartographic purposes. For additional information on the 3D Elevation Program, go to https://nationalmap.gov/3DEP/.

Third grade English Language Arts (ELA) and Math test results for the 2016-2017 school year for the state of Michigan. Data Driven Detroit obtained these datasets from MI School Data, for the State of the Detroit Child tool in July 2017. Test results were originally obtained on a school level and aggregated to state by Data Driven Detroit. Student data was suppressed when less than five students were tested per school.Click here for metadata (descriptions of the fields).

Third grade English Language Arts (ELA) and Math test results for the 2016-2017 school year by House of Representative districts for the state of Michigan. Data Driven Detroit obtained these datasets from MI School Data, for the State of the Detroit Child tool in July 2017. Test results were originally obtained on a school level and aggregated to districts by Data Driven Detroit. Student data was suppressed when less than five students were tested per school. Click here for metadata (descriptions of the fields).

The creation of the contour data was executed to capture the existing ground conditions at a specific point in time for the purpose of GIS mapping and analysis. Contour information is used for plannimetric mapping and for creating TINs and RASTERs, which support 3D mapping and ground/slope visualization. TINs and RASTERs of elevation data support spatial analyses related to slopes, viewsheds, water runoff, and other map algebra-based applications.Although the contract with VGIN (VA Geographic Information Network) was initiated in 2006, due to the late signing of the contact between the State and their contractor, there was not a long-enough "leaf-off" period to complete state-wide orthophotograhy mapping; the City of Richmond was one of many localities postponed until the Winter of 2007.

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 resource contains raster datasets created using ArcGIS to analyse groundwater levels in the Namoi subregion.

Purpose

These data layers were created in ArcGIS as part of the analysis to investigate surface water - groundwater connectivity in the Namoi subregion. The data layers provide several of the figures presented in the Namoi 2.1.5 Surface water - groundwater interactions report.

Dataset History

Extracted points inside Namoi subregion boundary. Converted bore and pipe values to Hydrocode format, changed heading of 'Value' column to 'Waterlevel' and removed unnecessary columns then joined to Updated_NSW_GroundWaterLevel_data_analysis_v01\NGIS_NSW_Bore_Join_Hydmeas_unique_bores.shp clipped to only include those bores within the Namoi subregion.

Selected only those bores with sample dates between >=26/4/2012 and <31/7/2012. Then removed 4 gauges due to anomalous ref_pt_height values or WaterElev values higher than Land_Elev values.

Then added new columns of calculations:

WaterElev = TsRefElev - Water_Leve

DepthWater = WaterElev - Ref_pt_height

Ref_pt_height = TsRefElev - LandElev

Alternatively - Selected only those bores with sample dates between >=1/5/2006 and <1/7/2006

2012_Wat_Elev - This raster was created by interpolating Water_Elev field points from HydmeasJune2012_only.shp, using Spatial Analyst - Topo to Raster tool. And using the alluvium boundary (NAM_113_Aquifer1_NamoiAlluviums.shp) as a boundary input source.

12_dw_olp_enf - Select out only those bores that are in both source files.

Then using depthwater in Topo to Raster, with alluvium as the boundary, ENFORCE field chosen, and using only those bores present in 2012 and 2006 dataset.

2012dw1km_alu - Clipped the 'watercourselines' layer to the Namoi Subregion, then selected 'Major' water courses only. Then used the Geoprocessing 'Buffer' tool to create a polygon delineating an area 1km around all the major streams in the Namoi subregion.

selected points from HydmeasJune2012_only.shp that were within 1km of features the WatercourseLines then used the selected points and the 1km buffer around the major water courses and the Topo to Raster tool in Spatial analyst to create the raster.

Then used the alluvium boundary to truncate the raster, to limit to the area of interest.

12_minus_06 - Select out bores from the 2006 dataset that are also in the 2012 dataset. Then create a raster using depth_water in topo to raster, with ENFORCE field chosen to remove sinks, and alluvium as boundary. Then, using Map Algebra - Raster Calculator, subtract the raster just created from 12_dw_olp_enf

Dataset Citation

Bioregional Assessment Programme (2017) Namoi bore analysis rasters. Bioregional Assessment Derived Dataset. Viewed 10 December 2018, http://data.bioregionalassessments.gov.au/dataset/7604087e-859c-4a92-8548-0aa274e8a226.

Dataset Ancestors

• Derived From Bioregional Assessment areas v02

• Derived From Gippsland Project boundary

• Derived From Bioregional Assessment areas v04

• Derived From Upper Namoi groundwater management zones

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From Bioregional Assessment areas v03

• Derived From Victoria - Seamless Geology 2014

• Derived From GIS analysis of HYDMEAS - Hydstra Groundwater Measurement Update: NSW Office of Water - Nov2013

• Derived From Bioregional Assessment areas v01

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

• Derived From GEODATA TOPO 250K Series 3

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Geological Provinces - Full Extent

• Derived From Hydstra Groundwater Measurement Update - NSW Office of Water, Nov2013

THE GEOINQUIRIES™ COLLECTION FOR MATHEMATICS

http://www.esri.com/geoinquiries

The GeoInquiry™ collection for Mathematics contains 15 free, standards-based activities that correspond and extend spatial concepts found in course textbooks frequently used in introductory algebra or geometry classes. The activities use a common inquiry-based instructional model, require only 15 minutes to deliver, and are device/laptop agnostic. Each activity includes an ArcGIS Online map but requires no login or installation. The activities harmonize with the Common Core mathematics national curriculum standards.

All Mathematics GeoInquiries™ can be found at: http://eseriurl.com/mathGeoInquiries

All GeoInquiries™ can be found at: http://www.esri.com/geoinquiries

Use an aerial photograph to determine the distance around a track, and then calculate rate and time for each lap and the race as a whole. THE GEOINQUIRIES™ COLLECTION FOR MATHEMATICShttp://www.esri.com/geoinquiriesThe GeoInquiry™ collection for Mathematics contains 15 free, standards-based activities that correspond and extend spatial concepts found in course textbooks frequently used in introductory algebra or geometry classes. The activities use a common inquiry-based instructional model, require only 15 minutes to deliver, and are device/laptop agnostic. Each activity includes an ArcGIS Online map but requires no login or installation. The activities harmonize with the Common Core math national curriculum standards. Activities include:· Rates & Proportions: A lost beach· D=R x T· Linear rate of change: Steady growth· How much rain? Linear equations· Rates of population change· Distance and midpoint· The coordinate plane· Euclidean vs Non-Euclidean· Area and perimeter at the mall· Measuring crop circles· Area of complex figures· Similar triangles· Perpendicular bisectors· Centers of triangles· Volume of pyramids

Teachers, GeoMentors, and school administrators can learn more at http://www.esri.com/geoinquiries.

This dataset was created using the 123 PISCES extant species range layers listed below in the table. The PISCES datasets were converted to rasters using GIS and then map algebra was used to calculate the total number of species in each huc12 watershed. Using GIS, an extended table was created to provide species lists by watershed using a one to many table relate. Species presence data was provided by PISCES: Moyle, Quinones and Bell (direct addition), Moyle and Randall (gstpoly), . These layers were generated by PISCES on 10/30/2014. The datasets listed in the table below were used in this analysis.Pisces_CodeCommon_NameAAM01Northern Green SturgeonAAT01White sturgeonCCF01Owens SuckerCCO02Goose Lake SuckerCCS01Klamath Largescale SuckerCCA03Clear Lake Prickly SculpinCCG01Riffle SculpinCCK03Upper Klamath Marbled SculpinCCK01Bigeye Marbled SculpinCCK02Lower Klamath Marbled SculpinCCN02Amargosa River PupfishCCN01Saratoga Springs PupfishCCN04Shoshone PupfishCCS03Salt Creek PupfishPEF01Northern California Brook LampreyPES01Klamath River LampreyPET02Goose Lake LampreyPET01Pacific LampreyCGC01Blue ChubCGO01Arroyo ChubEHT03Clear Lake Tule PerchEHT02Russian River Tule PerchEHT01Sacramento Tule PerchPLH01Kern Brook LampreyPLL01Pit-Klamath Brook LampreyPLR01Western Brook LampreyCLE01Sacramento HitchCLE03Monterey HitchCLP01Gualala RoachCLS06Navarro RoachCLS05Monterey RoachCLS04Clear Lake RoachCLS02Red Hills RoachCLS03Russian River RoachCLS07Tomales RoachCLS01Central California RoachCMC01HardHeadSOC01Coastal Cutthroat TroutSOM03Klamath Mountains Province Winter SteelheadSOM04Klamath Mountains Province Summer SteelheadSOM14California Golden TroutSOM12Eagle Lake Rainbow TroutSOM13Kern River Rainbow TroutSOM10McCloud River Redband TroutSOM11Goose Lake Redband TroutSOT08Central Valley Fall Chinook SalmonSOT07Central Valley Late Fall Chinook SalmonSOT03Southern Oregon Northern California Coast Fall Chinook SalmonSOT01Upper Klamath-Trinity Fall Chinook SalmonSOT02Upper Klamath-Trinity Spring Chinook SalmonCCP01Lahontan Mountain SuckerCPM01Sacramento SplittailSPW01Mountain WhitefishCRO06Amargosa Canyon Speckled DaceCRO05Long Valley Speckled DaceCRO04Owens Speckled DaceCRO07Santa Ana Speckled DaceCSB03Lahontan Lake Tui ChubCSB05Eagle Lake Tui ChubCST01Goose Lake Tui ChubCST03Cow Head Tui ChubAAM02Southern Green SturgeonCCL01Lost River SuckerCCM01Modoc SuckerCCO04Humboldt SuckerCCO03Monterey SuckerCCO01Sacramento SuckerCCR01Klamath Smallscale SuckerCCS02Santa Ana SuckerCCT01Tahoe SuckerCCB01Shortnose SuckerCCA04Coastrange SculpinCCA02Prickly SculpinCCA01Rough SculpinCCB02Paiute SculpinCCP03Reticulate SculpinCCP02Pit SculpinCCM02Desert PupfishCCR02Owens PupfishCCS04Cottonball Marsh PupfishGEN01Tidewater GobyCFP01California KillfishGGA01Coastal Threespine SticklebackGGA02Inland Threespine SticklebackGGA04Santa Ana (Shay Creek) SticklebackGGA03Unarmored Threespine SticklebackOHP01Delta SmeltPLA01River LampreyCLE02Clear Lake HitchCLS08Northern (Pit) RoachCLA01Staghorn SculpinMMC02Striped MulletSOC03Lahontan Cutthroat TroutSOC02Paiute Cutthroat TroutSOG01Pink SalmonSOK03Chum SalmonSOK01Central Coast Coho SalmonSOK02Southern Oregon Northern California Coast Coho SalmonSOM05Central California Coast Winter SteelheadSOM06Central Valley SteelheadSOM02Northern California Coast Summer SteelheadSOM01Northern California Coast Winter SteelheadSOM07South Central California Coast SteelheadSOM08Southern California SteelheadSOM09Coastal Rainbow TroutSOM15Little Kern Golden TroutSOT04California Coast Fall Chinook SalmonSOT06Central Valley Spring Chinook SalmonSOT05Central Valley Winter Chinook SalmonCOM01Sacramento BlackfishPPS01Starry FlounderCPG01Sacramento PikeminnowCRO03Klamath Speckled DaceCRO02Lahontan Speckled DaceCRO01Sacramento Speckled DaceCRE01Lahontan RedsideCSB01Klamath Tui ChubCSB04Lahontan Stream Tui ChubCSB06Owens Tui ChubCST02Pit River Tui ChubOST01Longfin SmeltOTP01EulachonCXT01Razorback Sucker

The percentage of students who met or exceeded PARCC exams in reading and mathematics in 3rd, 5th, and 8th grades. Partnership for Assessment of Readiness for College and Careers (PARCC) scores measure the number of students scoring in one of three classifications out of all students enrolled in that grade. Students can either be rated as exceeded, met, approached, partially met, or did not meet expectations of a subject. This indicator includes only those students who have tested as exceeded or met expectations. Source: Baltimore City Public School System Years Available: 2015, 2016, 2017

Third grade English Language Arts (ELA) and Math test results for the 2016-2017 school year by ZIP Code Tabulation Areas (ZCTAs) for the state of Michigan. Data Driven Detroit obtained these datasets from MI School Data, for the State of the Detroit Child tool in July 2017. Test results were originally obtained on a school level and aggregated to ZCTA by Data Driven Detroit. Student data was suppressed when less than five students were tested per school.

THE GEOINQUIRIES™ COLLECTION FOR MATHEMATICS

http://www.esri.com/geoinquiries

The GeoInquiry™ collection for Mathematics contains 15 free, standards-based activities that correspond and extend spatial concepts found in course textbooks frequently used in introductory algebra or geometry classes. The activities use a common inquiry-based instructional model, require only 15 minutes to deliver, and are device/laptop agnostic. Each activity includes an ArcGIS Online map but requires no login or installation. The activities harmonize with the Common Core mathematics national curriculum standards.

All Mathematics GeoInquiries™ can be found at: http://eseriurl.com/mathGeoInquiries

All GeoInquiries™ can be found at: http://www.esri.com/geoinquiries

To support the ecological model of the study Rodríguez et al. (2020, in review), five BIOCLIM variables (BIO1, BIO6, BIO10, BIO11) were computed from the Oscillayers dataset, for 11 subdivisions of the Marine Isotope Stages MIS 14 to MIS 11, as defined in (Rodríguez et al 2020, in review).
Oscillayers is a global‐scale and region‐specific BIOCLIM paleoclimatic datasets with high temporal resolution spanning the Plio‐Pleistocene, facilitating the study of climatic oscillations during the last 5.4 million years at high spatial (2.5 arc‐minutes) and temporal (10 kyr time periods) resolution (Gamisch, 2019).
BIOCLIM is a model designed for Species Distribution Modelling (SDM) that defines a set of 19 bioclimatic variables derived from monthly temperature and rainfall values in order to obtain biologically meaningful variables that are commonly used in ecology to model species or biome distributions (Booth et al., 2014; Nix, 1986).
The GIS computation was conducted using GRASS GIS map algebra (Shapiro & Westervelt, 1991) scripted via its Python API. The according Python scripts are attached to this dataset.

The percentage of students passing M.S.A. exams in reading and mathematics in 3rd, 5th, and 8th grades. Maryland School Assessment (MSA) scores measure the number of students scoring in one of three classifications out of all students enrolled in that grade. Students can either be rated as advanced, proficient, or having basic knowledge of a subject. This indicator includes only those students who have tested as advanced or proficient. Source: Baltimore City Public Schools Years Available: 2009-2010, 2010-2011, 2011-2012, 2012-2013, 2013-2014

NIWA's bathymetry model of New Zealand at a 250m resolution. The 2016 model is a compilation of data digitised from published coastal charts, digital soundings archive, navy collector sheets and digital multibeam data sourced from surveys by NIWA, LINZ, as well as international surveys by vessels from United States of America, France, Germany, Australia, and Japan. All data used is held at NIWA.Image service can be used for analysis in ArcGIS Desktop or ArcGIS Online - no need to download the data, just stream using this service and classify, symbolise, mask, extract or apply map algebra - just like you would with local raster files. https://enterprise.arcgis.com/en/server/latest/publish-services/windows/key-concepts-for-image-services.htmMap information and metadata Offshore representation was generated from digital bathymetry at a grid resolution of 250m. Sun illumination is from an azimuth of 315° and 45° above the horizon.Projection Mercator 41 (WGS84 datum). EPSG: 3994Scale 1:5,000,000 at 41°S. Not to be used for navigational purposes Bibliographic reference Mitchell, J.S., Mackay, K.A., Neil, H.L., Mackay, E.J., Pallentin, A., Notman P., 2012. Undersea New Zealand, 1:5,000,000. NIWA Chart, Miscellaneous Series No. 92Further Information: https://www.niwa.co.nz/our-science/oceans/bathymetry/further-informationLicence: https://www.niwa.co.nz/environmental-information/licences/niwa-open-data-licence-by-nn-nc-sa-version-1_Item Page Created: 2017-11-01 00:55 Item Page Last Modified: 2025-04-05 18:48Owner: NIWA_OpenData

The percentage of students who met or exceeded PARCC exams in reading and mathematics in 3rd, 5th, and 8th grades. Partnership for Assessment of Readiness for College and Careers (PARCC) scores measure the number of students scoring in one of three classifications out of all students enrolled in that grade. Students can either be rated as exceeded, met, approached, partially met, or did not meet expectations of a subject. This indicator includes only those students who have tested as exceeded or met expectations. Source: Baltimore City Public School System Years Available: 2015, 2016, 2017

By creating similar triangles, it is possible to find the distance across a river using indirect measurements. THE GEOINQUIRIES™ COLLECTION FOR MATHEMATICShttp://www.esri.com/geoinquiriesThe GeoInquiry™ collection for Mathematics contains 15 free, standards-based activities that correspond and extend spatial concepts found in course textbooks frequently used in introductory algebra or geometry classes. The activities use a common inquiry-based instructional model, require only 15 minutes to deliver, and are device/laptop agnostic. Each activity includes an ArcGIS Online map but requires no login or installation. The activities harmonize with the Common Core math national curriculum standards. Activities include:· Rates & Proportions: A lost beach· D=R x T· Linear rate of change: Steady growth· How much rain? Linear equations· Rates of population change· Distance and midpoint· The coordinate plane· Euclidean vs Non-Euclidean· Area and perimeter at the mall· Measuring crop circles· Area of complex figures· Similar triangles· Perpendicular bisectors· Centers of triangles· Volume of pyramids

Teachers, GeoMentors, and school administrators can learn more at http://www.esri.com/geoinquiries.

The percentage of high school students who have successfully passed the H.S.A. exams out of all high school students that took the exam in the school year (considering only the highest score per subject area). In Maryland, all students who entered 9th grade in or after 2005 are required to take and pass the High School Assessments (H.S.A.) in order to graduate, including students in special education, English language learners (ELLs), and students with 504 plans. There are currently three H.S.A. exams: English, Algebra/Data Analysis; and Biology (a H.S.A. in Government has since been discontinued). Students can retake the HSAs as many times as necessary to pass. Source: Baltimore City Public Schools Years Available: 2009-2010, 2011-2012, 2012-2013, 2013-2014