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ArcGIS Field Maps is a mobile app that allows you to view and collect field data using an Android or iOS smartphone or tablet. It is also a web app that allows you to configure web maps for use in the mobile app. The tutorials in this learning path will introduce you to the features of the Field Maps mobile app, how to create and configure web maps in Field Maps Designer that can be used in the Field Maps mobile app in online and offline mode, and how to collect data from a map and in the field with the mobile app.

This data set contains processed CF and, where applicable, pRF mapping data for our project investigating the robustness of our CF reverse-correlation methods in the presence of eye movement and optical defocus.

There are several participants in this archive. All of them participated in the LaserKiwi experiment in which they engaged in a video game, shooting coronaviruses with their eye gaze. Three participants (P4, P5, P6) also participated in the Unstable Eye experiments in which we used a classical sweeping bar pRF mapping design either with stable eye fixation in the screen centre or at a randomly jumping fixation dot.

You will need SamSrf to read these files (version 9.3 recommended). It has been tested on Matlab R2020a & b.

In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within California’s State Waters. The program supports a large number of coastal-zone- and ocean-management issues, including the California Marine Life Protection Act (MLPA) (California Department of Fish and Wildlife, 2008), which requires information about the distribution of ecosystems as part of the design and proposal process for the establishment of Marine Protected Areas. A focus of CSMP is to map California’s State Waters with consistent methods at a consistent scale. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow (to about 100 m) subsurface geology. It is emphasized that the more interpretive habitat and geology data rely on the integration of multiple, new high-resolution datasets and that mapping at small scales would not be possible without such data. This approach and CSMP planning is based in part on recommendations of the Marine Mapping Planning Workshop (Kvitek and others, 2006), attended by coastal and marine managers and scientists from around the state. That workshop established geographic priorities for a coastal mapping project and identified the need for coverage of “lands” from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of California’s State Waters. Unfortunately, surveying the zone from MHHW out to 10-m water depth is not consistently possible using ship-based surveying methods, owing to sea state (for example, waves, wind, or currents), kelp coverage, and shallow rock outcrops. Accordingly, some of the data presented in this series commonly do not cover the zone from the shore out to 10-m depth. This data is part of a series of online U.S. Geological Survey (USGS) publications, each of which includes several map sheets, some explanatory text, and a descriptive pamphlet. Each map sheet is published as a PDF file. Geographic information system (GIS) files that contain both ESRI ArcGIS raster grids (for example, bathymetry, seafloor character) and geotiffs (for example, shaded relief) are also included for each publication. For those who do not own the full suite of ESRI GIS and mapping software, the data can be read using ESRI ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed September 20, 2013). The California Seafloor Mapping Program is a collaborative venture between numerous different federal and state agencies, academia, and the private sector. CSMP partners include the California Coastal Conservancy, the California Ocean Protection Council, the California Department of Fish and Wildlife, the California Geological Survey, California State University at Monterey Bay’s Seafloor Mapping Lab, Moss Landing Marine Laboratories Center for Habitat Studies, Fugro Pelagos, Pacific Gas and Electric Company, National Oceanic and Atmospheric Administration (NOAA, including National Ocean Service–Office of Coast Surveys, National Marine Sanctuaries, and National Marine Fisheries Service), U.S. Army Corps of Engineers, the Bureau of Ocean Energy Management, the National Park Service, and the U.S. Geological Survey. These web services for the Offshore of Point Conception map area includes data layers that are associated to GIS and map sheets available from the USGS CSMP web page at https://walrus.wr.usgs.gov/mapping/csmp/index.html. Each published CSMP map area includes a data catalog of geographic information system (GIS) files; map sheets that contain explanatory text; and an associated descriptive pamphlet. This web service represents the available data layers for this map area. Data was combined from different sonar surveys to generate a comprehensive high-resolution bathymetry and acoustic-backscatter coverage of the map area. These data reveal a range of physiographic including exposed bedrock outcrops, large fields of sand waves, as well as many human impacts on the seafloor. To validate geological and biological interpretations of the sonar data, the U.S. Geological Survey towed a camera sled over specific offshore locations, collecting both video and photographic imagery; these “ground-truth” surveying data are available from the CSMP Video and Photograph Portal at https://doi.org/10.5066/F7J1015K. The “seafloor character” data layer shows classifications of the seafloor on the basis of depth, slope, rugosity (ruggedness), and backscatter intensity and which is further informed by the ground-truth-survey imagery. The “potential habitats” polygons are delineated on the basis of substrate type, geomorphology, seafloor process, or other attributes that may provide a habitat for a specific species or assemblage of organisms. Representative seismic-reflection profile data from the map area is also include and provides information on the subsurface stratigraphy and structure of the map area. The distribution and thickness of young sediment (deposited over the past about 21,000 years, during the most recent sea-level rise) is interpreted on the basis of the seismic-reflection data. The geologic polygons merge onshore geologic mapping (compiled from existing maps by the California Geological Survey) and new offshore geologic mapping that is based on integration of high-resolution bathymetry and backscatter imagery seafloor-sediment and rock samplesdigital camera and video imagery, and high-resolution seismic-reflection profiles. The information provided by the map sheets, pamphlet, and data catalog has a broad range of applications. High-resolution bathymetry, acoustic backscatter, ground-truth-surveying imagery, and habitat mapping all contribute to habitat characterization and ecosystem-based management by providing essential data for delineation of marine protected areas and ecosystem restoration. Many of the maps provide high-resolution baselines that will be critical for monitoring environmental change associated with climate change, coastal development, or other forcings. High-resolution bathymetry is a critical component for modeling coastal flooding caused by storms and tsunamis, as well as inundation associated with longer term sea-level rise. Seismic-reflection and bathymetric data help characterize earthquake and tsunami sources, critical for natural-hazard assessments of coastal zones. Information on sediment distribution and thickness is essential to the understanding of local and regional sediment transport, as well as the development of regional sediment-management plans. In addition, siting of any new offshore infrastructure (for example, pipelines, cables, or renewable-energy facilities) will depend on high-resolution mapping. Finally, this mapping will both stimulate and enable new scientific research and also raise public awareness of, and education about, coastal environments and issues. Web services were created using an ArcGIS service definition file. The ArcGIS REST service and OGC WMS service include all Offshore of Point Conception map area data layers. Data layers are symbolized as shown on the associated map sheets.

As one of iCorridor applications, MEPDG web map provides site specific traffic data (Level 1) such as AADTT, vehicle class distribution, number of axle per truck, and axle load distribution for AASHTOWare Pavement ME Design. This program can generate the following three data files for any specific LHRS sections: Traffic data input file in XML format that contains the AADTT, vehicle class distribution, axle per truck, and axle spacing & configuration. Axle load spectrum file in ALF format that contains the axle load spectrum tables of single, tandem, tridem and quad axle types. A summary file in spreadsheet format that contains the above traffic data. The above XMF and ALF files can be directly input into AASHTOWare Pavement ME Design to run the analysis. If traffic data is insufficient within the LHRS section, the tables for Southern or Northern Ontario will be generated.NON-DIRECTIONAL option will provide an overall AADT and AADTT of the selected LHRS section in both directions. The pavement designer should enter the corresponding percent split of traffic volume for the design direction (typical 50%) to the ‘Percent trucks in design direction’ field. DIRECTIONAL option will provide the AADT and AADTT of the specific direction of the selected LHRS section, and the designer requires to enter 100% to the ‘Percent trucks in design direction’ field. Note that the designer requires to zoom in very close to the map in order to identify which direction to be chosen. Under rare circumstances should the designer require to select this option. If necessary, the data for AADT and AADTT as provided in iCorridor shall be overridden by the latest data provided by other sources, and the number of lanes at the design section should be verified with the designer or owner

Detailed land-cover mapping is essential for a range of research issues addressed by sustainability science, especially for questions posed of urban areas, such as those of the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) program. This project provides a 1-meter land-cover mapping of the CAP LTER study area (greater Phoenix metropolitan area and surrounding Sonoran desert). The mapping is generated primarily using 2015 National Agriculture Imagery Program (NAIP) four-band data, with auxiliary GIS data used to improve accuracy. Auxiliary data include the 2015 cadastral parcel data, the 2014 USGS LiDAR data (1-meter), the 2014 Microsoft/OpenStreetMap Building Footprint data, the 2015 Street TIGER/Line, and a previous (2010) NAIP-based land-cover map of the study area (https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-cap&identifier=623). Among auxiliary data, building footprints and LiDAR data significantly improved the boundary detection of above-ground objects. Post-classification, manual editing was applied to minimize classification errors. As a result, the land-cover map achieves an overall accuracy of 94 per cent. The map contains eight land cover classes, including: (1) building, (2) asphalt, (3) bare soil and concrete, (4) tree and shrub, (5) grass, (6) water, (7) active cropland, and (8) fallow. When compared to the aforementioned, previous (2010) NAIP-based land-cover map for the study area, buildings and tree canopies are classified more accurately in this 2015 land-cover map.

This is the dataset to replicate study findings of the paper 'The impact of user characteristics of smallholder farmers on user experiences with collaborative map applications'. We collected data in two field surveys in Uganda and Colombia in 2019.

The soil map shows the dominant composition of the soil in the first 2 meters below ground level. In the interests of simplification, a classification in sand, loam or clay per square kilometre has been chosen. This was drawn up on the basis of the soil type map and supplemented with drilling rigs of own drillings and drillings found in the Database Subsurface Flanders. In the context of rainwater policy and the principles of optimal separation with the aim of promoting the natural runoff and infiltration of rainwater, it is important to have an insight into the soil condition and infiltration sensitivity of the upper soil layer in Antwerp. In this way, the integration of water management into urban design can be controlled more effectively and efficiently, as well as the localisation of infiltration-prone areas for future construction projects. For example, city authorities regularly receive questions from contractors, architects and design offices about the possibility of infiltration on a particular plot. This information is also important in the design and implementation of public urban renewal works. The layout of geohydrological maps can contribute to a better alignment between spatial planning, public space design, green management and water management. It is quite possible to save costs by combining multiple management aspects with the construction of green-blue structures: tackling flooding, combating soil desiccation, developing more urban nature and biodiversity. Finally, these data are used as substantiation in the preparation of the rainwater plan; a plan indicating at district level how much infiltration and/or buffer capacity is desirable and in which forms (e.g. collective wadi, canal or pond). The contract concerns the preparation of four geohydrological maps, in particular: a soil map, a groundwater map (meter - ground level) with an annual average depth of the phreatic groundwater table below street level, a groundwater map with annual average levels relative to the topographic reference level (meter -/+ TAW) and an infiltration map of the Antwerp region. The study is part of the characterisation of the subsurface of Antwerp with a view to the localisation of infiltration-prone areas for future construction projects. These maps describe the entire regionthe territory of Antwerp (city of Antwerp with its 9 districts and submunicipalities), with the exception of the Antwerp port area. For the right bank of the Antwerp port area, the possibilities of rainwater infiltration and buffering have already been investigated (What about rainwater in the Antwerp port area?, IMDC iov Port of Antwerp and Alfaport, 2013). In function of the calibration and calculation of groundwater and groundwater data, it was important to integrate the port area into the model area. . The study area is bounded to the north, east, south and west respectively by the national border and municipalities of Berendrecht, Deurne, HobokenStabroek, Kapellen, Brasschaat, Schoten Wijnegem, Wommelgem, Borsbeek, Mortsel, Edegem, Aartselaar, Hemiksem and LinkeroeverZwijndrecht.

Equivalence (the equal-area property of a map projection) is important to some categories of maps. However, unlike for conformal projections, completely general techniques have not been developed for creating new, computationally reasonable equal-area projections. The literature describes many specific equal-area projections and a few equal-area projections that are more or less configurable, but flexibility is still sparse. This work develops a tractable technique for generating a continuum of equal-area projections between two chosen equal-area projections. The technique gives map projection designers unlimited choice in tailoring the projection to the need. The technique is particularly suited to maps that dynamically adapt optimally to changing scale and region of interest, such as required for online maps.

The County Council has identified each area of the County as urban, suburban, or rural for road code Urban Road: a road segment in or abutting a Metro Station Policy Area, Town Center Policy Area, or other urban area expressly identified in a Council resolution.Rural Road: a road segment located in a rural policy area as defined in the County Growth Policy; Suburban Road: a road segment located elsewhere in the County.For more information, contact: GIS Manager Information Technology & Innovation (ITI) Montgomery County Planning Department, MNCPPC T: 301-650-5620

In order to use the Romanian color standard for soil type map legends, a dataset of ESRI ArcMap-10 files, consisting of a shapefile set (.dbf, .shp, .shx, .sbn, and .sbx files), four different .lyr files, and three different .style files (https://desktop.arcgis.com/en/arcmap/10.3/map/ : saving-layers-and-layer-packages, about-creating-new-symbols, what-are-symbols-and-styles-), have been prepared. The shapefile set is not a “real” georeferenced layer/coverage; it is designed only to handle all the instants of soil types from the standard legend.

This legend contains 67 standard items: 63 proper colors (different color hues, each of them having, generally, 2 - 4 degrees of lightness and/or chroma, four shades of grey, and white color), and four hatching patterns on white background. The “color difference DE*ab” between any two legend colors, calculated with the color perceptually-uniform model CIELAB, is greater than 10 units, thus ensuring acceptably-distinguishable colors in the legend. The 67 standard items are assigned to 60 main soils existing in Romania, four main nonsoils, and three special cases of unsurveyed land. The soils are specified in terms of the current Romanian system of soil taxonomy, SRTS-2012+, and of the international system WRB-2014.

The four different .lyr files presented here are: legend_soilcode_srts_wrb.lyr, legend_soilcode_wrb.lyr, legend_colorcode_srts_wrb.lyr, and legend_colorcode_wrb.lyr. The first two of them are built using as value field the “Soil_codes” field, and as labels (explanation texts) the “Soil_name” field (storing the soil types according to SRTS/WRB classification), respectively, the “WRB” field (the soil type according to WRB classification), while the last two .lyr files are built using as value field the “color_code” field (storing the color codes) and as labels the soil name in SRTS and WRB, respectively, in WRB classification.

In order to exemplify how the legend is displayed, two .jpg files are also presented: legend_soil_srts_wrb.jpg and legend_color_wrb.jpg. The first displays the legend (symbols and labels) according to the SRTS classification order, the second according to the WRB classification.

The three different .style files presented here are: soil_symbols.style, wrb_codes.style, and color_codes.style. They use as name the soil acronym in SRTS classification, soil acronym in WRB classification, and, respectively, the color code.

The presented file set may be used to directly implement the Romanian color standard in digital soil type map legends, or may be adjusted/modified to other specific requirements.

Data reported in research published in Crop Science, “Mapping the quantitative field resistance to stripe rust in a hard winter wheat population ‘Overley’ × ‘Overland.’” Authors are Wardah Mustahsan, Mary J. Guttieri, Robert L. Bowden, Kimberley Garland-Campbell, Katherine Jordan, Guihua Bai, Guorong Zhang from USDA Agricultural Research Service and Kansas State University. This study was conducted to identify quantitative trait loci (QTL) associated with field resistance to stripe rust, also known as yellow rust (YR), in hard winter wheat. Stripe rust infection type and severity were rated in recombinant inbred lines (RILs, n=204) derived from a cross between hard red winter wheat cultivars ‘Overley’ and ‘Overland’ in replicated field trials in the Great Plains and Pacific Northwest. RILs (n=184) were genotyped with reduced representation sequencing to produce SNP markers from alignment to the ‘Chinese Spring’ reference sequence, IWGSC v2.1, and from alignment to the reference sequence for ‘Jagger’, which is a parent of Overley. Genetic linkage maps were developed independently from each set of SNP markers. QTL analysis identified genomic regions on chromosome arms 2AS, 2BS, 2BL, and 2DL that were associated with stripe rust resistance using multi-environment best linear unbiased predictors for stripe rust infection type and severity. Results for the two linkage maps were very similar. PCR-based SNP marker assays associated with the QTL regions were developed to efficiently identify these genomic regions in breeding populations.Field response to YR was evaluated in seven trials: Rossville, KS (2018 and 2019), Hays, KS (2019), Pullman, WA (2019 and 2020) and Central Ferry, WA (2019 and 2020). An augmented experimental design was used at Rossville, KS with highly replicated checks and two full replications of RILs (n=187 in 2018; n=204 in 2019). The field experiment at Hays was arranged in a partially replicated augmented design with one or two replications of each RIL (n=194). The parental checks (Overley and Overland) were represented in three blocks for each of the two field replications at Hays, and RILs were distributed among blocks; not all RILs were present in each replication. RILs were arranged in an augmented design with two replications at Pullman (n=204 RILs) and Central Ferry (n=155 RILs in 2019; n=204 in 2020). At Pullman and Central Ferry.The trials at Rossville, KS were inoculated using an inoculum consisting of equal parts of four isolates that were all virulent to Yr9. Two isolates were collected in Kansas in 2010 and had virulence to Yr17 but not QYr.tamu-2B. The other two isolates were from Kansas in 2012 and had virulence to QYr.tamu-2B, but not Yr17. Susceptible spreader rows (KS89180B, carrying Yr9) were inoculated several times during the tillering stage in the evenings with an ultra-low volume sprayer using a suspension of 2 mL of fresh urediniospores in 1 L of Soltrol 170 isoparaffin oil. Trials at Pullman, WA and Central Ferry, WA were evaluated under natural inoculum supplemented by a mixture of isolates collected in the previous field season. The trial at Hays, KS was evaluated under natural infection.Data collection at Rossville, KS began once the susceptible check (KS89180B) had an infection severity coverage of ~10% and continued until senescence. In Rossville, disease ratings (IT and SEV) were collected on 16, 22, and 28th of May 2019. Most ratings in Rossville were taken some time after heading from Zadoks stages 55 to 70. In Pullman, disease ratings were collected on July 1 and 12. In Central Ferry, disease ratings were taken on 12th and 18th of June 2019. The second rating date was used for subsequent statistical analysis. In Hays, disease ratings were taken on June 1, 2019, when the plants were in early booting or heading stages (Zadoks 31-41). Stripe rust evaluations were measured using two disease rating scales: IT (0-9; from no infection to highly susceptible, Line and Qayoum, 1992) and SEV based on visual estimation of the percent flag leaf area affected by the pathogen including associated chlorosis and necrosis (0-100%).DNA was extracted from seedlings, and genotyping-by-sequencing was conducted as described previously (Guttieri, 2020) on a subset of 189 lines (187 RILS and 2 parents) of which 23 RILs were F6-derived and 164 RILs were F9-derived. Single nucleotide polymorphisms (SNPs) were identified in parallel using reference-based calling in the TASSEL pipeline (Bradbury et al., 2007) using both the IWGSC v2.1 reference genome (Zhu et al., 2021) and the Jagger reference sequence (Wheat Genomes Project (http://www.10wheatgenomes.com/10-wheat-genomes-project-and-the-wheat-initiative/). The TASSEL pipeline was executed with the following parameters: minimum read count = 1, minimum quality score = 0, minimum locus coverage = 0.19, and minimum minor allele frequency = 0.005, minimum heterozygous proportion = 0, and removal of minor SNP states. The resulting SNP datasets from each reference sequence were filtered in TASSEL by taxa (RILs) and sites (SNPs). The RILs were filtered to include those RILs for which at least 20% sites were present. The sites were filtered to include sites for which > 60% of RILs were called, minor allele frequency (MAF) > 0.25, maximum allele frequency < 0.75, maximum heterozygous proportion = 0.25, and removal of minor SNP states. The ABH plugin in TASSEL was applied to this reduced dataset to identify parental genotypes.Resources in this dataset:Resource Title: Multilocation Stripe Rust Data File Name: MultiLocRawData_Yr.xslxResource Title: OvOv_CS_TasselSNPCalls File Name: KSM17-OvOv-parentsmerge1.hmp.txt Resource Description: Output of TASSEL GBS SNP calling pipeline using Chinese Spring v2 refseq. Starting point for map construction pipeline.Resource Title: OvOv GBS SNP Calls Jagger RefSeq File Name: KSM17-OvOv-Jaggerpmerge1.hmp.txt Resource Description: TASSEL output from reference-based SNP calling using the Jagger reference sequenceResource Title: QTL-Associated KASP Markers with IT and SEV BLUPs File Name: KASP_Data_IT_SEV.xlsx Resource Description: Multilocation best linear unbiased predictors (BLUPs) for stripe rust infection type and severity of recombinant inbred lines. KASP assay results for QTL-associated SNPs, coded Overley = 2, Overland = 0, Het = 1, Missing = "."

Abstract

The work plan activities in Kiribati related to the updating of the listing of all households and institutions in Kiribati is to produce a sex and age disaggregated population count that forms the basis for a sampling frame for the upcoming Social Indicator Survey (SIS) and Household Income and Expenditure Survey (HIES). It also serves the purpose of digitalising and harmonising enumeration areas (EAs) to facilitate random sampling and census planning. To achieve this, SPC was engaged to conduct the following activities:

1. Planning and budgeting: prepare a comprehensive plan and budget for the household listing.
2. Mapping: prepare field maps to be used in the listing; digitalise EA boundaries and harmonisation of new EA framework; training and capacity building of the Ministry of Environment, Lands and Agricultural Development; prepare maps for the selected EAs in the SIS.
3. Listing questionnaire design, enumerator training and technology: develop a tablet-based household listing questionnaire and associated training resources, and set up of technology (e.g., server, tablet interviewer application, backup protocols); support Kiribati's National Statistics Office (KI-NSO) to conduct training of enumerators in all aspects of the collection; and administer South-South support to Kiribati for the duration of the listing.
4. Sample design: design the sample and field plan for the SIS; and build capacity of KI-NSO in sample design and field work planning.

Geographic coverage

National coverage (full coverage).

Analysis unit

Households/Institutions and Individuals.

Universe

Households, Institutions, de jure household members.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Not Applicable.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire, which is designed in English, is divided into three main sections:

1) Household ID and Building Type 2) Person Roster 3) Geographic Information and Photo

The questionnaire was generated by Survey Solutions and is provided as an external resource.

Cleaning operations

Data was processed using the software STATA. Corrections were made both automatically and by visual control: validation checks in the questionnaire as well as final editing of the raw data.

A concept map is a powerful method that promotes meaningful learning and is highly recommended for use in biology classes. According to multimedia research, the effectiveness of concept maps could be improved by incorporating pictorial elements. Apart from using realistic images, a new field of research claims that specific design manipulations, including human-like features with appealing colors (emotional design), influence learners’ affective state and improve learning. A positive affective state is assumed to evoke emotions and provoke deeper cognitive processing, which increases the cognitive resources available for a task. We conducted two experiments with a total of N = 249 junior high school students, comparing the effect of concept maps with emotional design illustrations (emotional design), with non-emotional design illustrations (neutral design), and without illustrations (control design). Experiment 1 examined the influence of these designs on students’ perceived affective state, perceived cognitive load (extraneous, intrinsic, and germane load), perceived task difficulty, and learning performance (n = 202), experiment 2 focused on the perceived affective state of the students (n = 47). We found that emotional design led to a significant decrease in perceived task difficulty, but we neither found an effect on learning performance nor the positive affective state. Learning with pictorial concept maps (in emotional or neutral design) reduced the negative affect compared to learning with control concept maps. Other than expected, the neutral design led to reduced perceived extraneous and intrinsic cognitive load. Consequently, in terms of learning, emotional design in concept maps did not hamper learning but did not foster it either.

In Charlottesville historic resources may be designated as part of a historic district, or may be individually designated. An Architectural Design Control district, often referred to as an ADC or local historic district, is a group of historic resources that are designated for protection through zoning. The goal of local designation is to identify and preserve buildings, structures, landscapes, settings, neighborhoods, places, and features with historic, cultural and architectural significance; to protect visible reminders of the historic, cultural, architectural, or archaeological heritage of the city; to ensure that new buildings, additions, and landscaping will be in harmony with the existing character; to maintain property values; and to promote tourism and quality of life.All properties designated within a local ADC district are subject to review by the Board of Architectural Review (BAR) for any exterior changes including demolitions. This ensures a public notification and review process before changes can be made to a protected property.

A plethora of magnetic nanoparticles has been developed and investigated under different alternating magnetic fields (AMF) for the hyperthermic treatment of malignant tissues. Yet, clinical applications of magnetic hyperthermia are sporadic, mostly due to the low energy conversion efficiency of the metallic nanoparticles and the high tissue concentrations required. Here, we study the hyperthermic performance of commercially available formulations of superparamagnetic iron oxide nanoparticles (SPIOs), with core diameter of 5, 7 and 14 nm, in terms of absolute temperature increase ΔT and specific absorption rate (SAR). These nanoparticles are operated under a broad range of AMF conditions, with frequency f varying between 0.2 and 30 MHz; field strength H ranging from 4 to 10 kA m−1; and concentration cMNP varying from 0.02 to 3.5 mg ml−1. At high frequency field (∼30 MHz), non specific heating dominates and ΔT correlates with the electrical conductivity of the medium. At low frequency field (42°C) and thermal ablation (Ttissue >50°C) are derived in terms of cMNP, operating AMF conditions and blood perfusion. The resulting maps can be used to rationally design hyperthermic treatments and identifying the proper route of administration – systemic versus intratumor injection – depending on the magnetic and biodistribution properties of the nanoparticles.

Maps are derived from field maps and published maps of the site.

CDFW BIOS GIS Dataset, Contact: Kristeen Penrod, Description: The Critical Linkages: Bay Area & Beyond project was initiated in 2010 to identify areas that are vital for connectivity within the nine-county Bay Area and beyond to ensure the region is connected to the larger landscapes to the north and south.

Purpose: Displays Geographic area of planning areas govern by the Downtown Master Plan and part of the DMP Zoning Atlas.Intended Use: All downtown development is subject to the provisions of the Downtown Master Plan (DMP) and urban code to ensure that all new buildings contribute to the urban life of the city. The intent of the DMP urban regulations is to create a sustainable downtown with an enhanced quality of life by creating a zoning code which is reflective of the downtown's 13 districts and their different characteristics. These urban regulations enable flexible building design by encouraging a variety of uses, heights, and forms. The urban regulations describe maximum development allowances. Department: Development Services / Urban Design DivisionData Source: Layer referenced is located within the Planning datasetHow was the data derived: From existing maps with the direction of the Urban Design Division when the code of ordinances established the Comprehensive Master Plan and the Chapter 94 - Zoning and Planning development Regulations, Article IV - Downtown Master Plan Urban Regulations.How was the data modified: Static layer except when directed by the Urban Design Division when amending the code of ordinances at Chapter 94 - Zoning and Planning Development Regulations, Article IV - Downtown Master Plan Urban Regulations and/or the Comprehensive Master Plan.Update Frequency: As needed when amendments occur and directed by the Urban Design Division.03/17/23: The latest updates resulted from ordinance 4984-21 of the Comprehensive Master Plan and ordinance 4985-21 of the Chapter 94 - Zoning and Planning development Regulations, Article IV - Downtown Master Plan Urban Regulations.Notes: View the Zoning and development Code https://online.encodeplus.com/regs/westpalmbeach-fl/doc-viewer.aspx#secid-280

Download .zipThe A Law Coal Permit Maps county coverage sets were developed using the original mine maps for coal mining and reclamation permits issued under Ohio law from approximately 1966 through 1973. Approximately 1111 A-Permits were issued during this time period, however, only 350 records could be located and captured at this time. The Division of Mineral Resources Management will continue to search for missing A permit archival records as resources allow; additional A permit data may be added to this existing coverage in the future.

Ohio started issuing coal mining licenses in the 1940s. The earliest license and permit requirements were minimal and sometimes did not include submittal of a map or other delineation of the mined area. Significant changes to legal requirements are reflected by the alphabetical designation of each subsequent law revision, i.e., earlier A-law permits (circa 1966) through contemporary D-law permits. The ODNR-Division of Mineral Resources Management (DMRM) has attempted to create as complete a database as possible from available archive records, however, research has identified missing permit files. Thus, this GIS data is known to be incomplete due to the loss of archival records.

The A law permit maps were scanned at a density of 200 dots per inch (dpi). The scanned image was then heads-up digitized using Microstation computer aided design software (CAD) to create design files grouped by county location. Data captured within the design file includes permit boundary and affected boundary and associated attributes. When available, test hole locations and associated attributes were also captured. The design file was then "placed-to-ground" using ODNR Division of Geological Survey's "ODNR Land Subdivision Background Design Files" NAD83 State Plane coverages and DOQQ aerial images obtained through the Ohio Geographically Referenced Information Program (OGRIP)/Ohio Department of Administrative Services. The design file was then converted to ARC/INFO coverage and projected to State Plane Ohio Coordinates, NAD83:

Projected coordinate system name: NAD_1983_StatePlane_Ohio_South_FIPS_3402_Feet or NAD_1983_StatePlane_Ohio_North_FIPS_3401_Feet

Geographic coordinate system name: GCS_North_American_1983

A complete county coverage set consists of three data files for the permit area, affected area, and test hole locations. For example, the coverage for Harrison County includes:

harrison_a_permitted (Harrison County, A-permit area polygons) harrison_a _affected (Harrison County, A-permit affected area polygons) harrison_testholes_a (Harrison County, Test Hole points)

In addition to the ArcView shape files in the county data sets, the scanned TIF images for source documents are available at DMRM. The scanned mine map depicts information about the operations conducted, environmental resources, and extracted coal resources. If more detailed information is desired, the available archival record for each captured permit can be accessed at either the State Archives at the Ohio Historical Society or the ODNR-DMRM central office.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesDivision of Mineral Resources ManagementAbandoned Mine Land Program2045 Morse Rd, Bldg I-2Columbus, OH, 43229Telephone: 614-265-6462Email: gis.support@dnr.ohio.gov

The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public land and voluntarily provided private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastre Theme (https://communities.geoplatform.gov/ngda-cadastre/). The PAD-US is an ongoing project with several published versions of a spatial database including areas dedicated to the preservation of biological diversity, and other natural (including extraction), recreational, or cultural uses, managed for these purposes through legal or other effective means. The database was originally designed to support biodiversity assessments; however, its scope expanded in recent years to include all public and nonprofit lands and waters. Most are public lands owned in fee (the owner of the property has full and irrevocable ownership of the land); however, long-term easements, leases, agreements, Congressional (e.g. 'Wilderness Area'), Executive (e.g. 'National Monument'), and administrative designations (e.g. 'Area of Critical Environmental Concern') documented in agency management plans are also included. The PAD-US strives to be a complete inventory of public land and other protected areas, compiling “best available” data provided by managing agencies and organizations. The PAD-US geodatabase maps and describes areas using over twenty-five attributes and five feature classes representing the U.S. protected areas network in separate feature classes: Fee (ownership parcels), Designation, Easement, Marine, Proclamation and Other Planning Boundaries. Five additional feature classes include various combinations of the primary layers (for example, Combined_Fee_Easement) to support data management, queries, web mapping services, and analyses. This PAD-US Version 2.1 dataset includes a variety of updates and new data from the previous Version 2.0 dataset (USGS, 2018 https://doi.org/10.5066/P955KPLE ), achieving the primary goal to "Complete the PAD-US Inventory by 2020" (https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/pad-us-vision) by addressing known data gaps with newly available data. The following list summarizes the integration of "best available" spatial data to ensure public lands and other protected areas from all jurisdictions are represented in PAD-US, along with continued improvements and regular maintenance of the federal theme. Completing the PAD-US Inventory: 1) Integration of over 75,000 city parks in all 50 States (and the District of Columbia) from The Trust for Public Land's (TPL) ParkServe data development initiative (https://parkserve.tpl.org/) added nearly 2.7 million acres of protected area and significantly reduced the primary known data gap in previous PAD-US versions (local government lands). 2) First-time integration of the Census American Indian/Alaskan Native Areas (AIA) dataset (https://www2.census.gov/geo/tiger/TIGER2019/AIANNH) representing the boundaries for federally recognized American Indian reservations and off-reservation trust lands across the nation (as of January 1, 2020, as reported by the federally recognized tribal governments through the Census Bureau's Boundary and Annexation Survey) addressed another major PAD-US data gap. 3) Aggregation of nearly 5,000 protected areas owned by local land trusts in 13 states, aggregated by Ducks Unlimited through data calls for easements to update the National Conservation Easement Database (https://www.conservationeasement.us/), increased PAD-US protected areas by over 350,000 acres. Maintaining regular Federal updates: 1) Major update of the Federal estate (fee ownership parcels, easement interest, and management designations), including authoritative data from 8 agencies: Bureau of Land Management (BLM), U.S. Census Bureau (Census), Department of Defense (DOD), U.S. Fish and Wildlife Service (FWS), National Park Service (NPS), Natural Resources Conservation Service (NRCS), U.S. Forest Service (USFS), National Oceanic and Atmospheric Administration (NOAA). The federal theme in PAD-US is developed in close collaboration with the Federal Geographic Data Committee (FGDC) Federal Lands Working Group (FLWG, https://communities.geoplatform.gov/ngda-govunits/federal-lands-workgroup/); 2) Complete National Marine Protected Areas (MPA) update: from the National Oceanic and Atmospheric Administration (NOAA) MPA Inventory, including conservation measure ('GAP Status Code', 'IUCN Category') review by NOAA; Other changes: 1) PAD-US field name change - The "Public Access" field name changed from 'Access' to 'Pub_Access' to avoid unintended scripting errors associated with the script command 'access'. 2) Additional field - The "Feature Class" (FeatClass) field was added to all layers within PAD-US 2.1 (only included in the "Combined" layers of PAD-US 2.0 to describe which feature class data originated from). 3) Categorical GAP Status Code default changes - National Monuments are categorically assigned GAP Status Code = 2 (previously GAP 3), in the absence of other information, to better represent biodiversity protection restrictions associated with the designation. The Bureau of Land Management Areas of Environmental Concern (ACECs) are categorically assigned GAP Status Code = 3 (previously GAP 2) as the areas are administratively protected, not permanent. More information is available upon request. 4) Agency Name (FWS) geodatabase domain description changed to U.S. Fish and Wildlife Service (previously U.S. Fish & Wildlife Service). 5) Select areas in the provisional PAD-US 2.1 Proclamation feature class were removed following a consultation with the data-steward (Census Bureau). Tribal designated statistical areas are purely a geographic area for providing Census statistics with no land base. Most affected areas are relatively small; however, 4,341,120 acres and 37 records were removed in total. Contact Mason Croft (masoncroft@boisestate) for more information about how to identify these records. For more information regarding the PAD-US dataset please visit, https://usgs.gov/gapanalysis/PAD-US/. For more information about data aggregation please review the Online PAD-US Data Manual available at https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-manual .