Statistical analyses and maps representing mean, high, and low water-level conditions in the surface water and groundwater of Miami-Dade County were made by the U.S. Geological Survey, in cooperation with the Miami-Dade County Department of Regulatory and Economic Resources, to help inform decisions necessary for urban planning and development. Sixteen maps were created that show contours of (1) the mean of daily water levels at each site during October and May for the 2000-2009 water years; (2) the 25th, 50th, and 75th percentiles of the daily water levels at each site during October and May and for all months during 2000-2009; and (3) the differences between mean October and May water levels, as well as the differences in the percentiles of water levels for all months, between 1990-1999 and 2000-2009. The 80th, 90th, and 96th percentiles of the annual maximums of daily groundwater levels during 1974-2009 (a 35-year period) were computed to provide an indication of unusually high groundwater-level conditions. These maps and statistics provide a generalized understanding of the variations of water levels in the aquifer, rather than a survey of concurrent water levels. Water-level measurements from 473 sites in Miami-Dade County and surrounding counties were analyzed to generate statistical analyses. The monitored water levels included surface-water levels in canals and wetland areas and groundwater levels in the Biscayne aquifer. Maps were created by importing site coordinates, summary water-level statistics, and completeness of record statistics into a geographic information system, and by interpolating between water levels at monitoring sites in the canals and water levels along the coastline. Raster surfaces were created from these data by using the triangular irregular network interpolation method. The raster surfaces were contoured by using geographic information system software. These contours were imprecise in some areas because the software could not fully evaluate the hydrology given available information; therefore, contours were manually modified where necessary. The ability to evaluate differences in water levels between 1990-1999 and 2000-2009 is limited in some areas because most of the monitoring sites did not have 80 percent complete records for one or both of these periods. The quality of the analyses was limited by (1) deficiencies in spatial coverage; (2) the combination of pre- and post-construction water levels in areas where canals, levees, retention basins, detention basins, or water-control structures were installed or removed; (3) an inability to address the potential effects of the vertical hydraulic head gradient on water levels in wells of different depths; and (4) an inability to correct for the differences between daily water-level statistics. Contours are dashed in areas where the locations of contours have been approximated because of the uncertainty caused by these limitations. Although the ability of the maps to depict differences in water levels between 1990-1999 and 2000-2009 was limited by missing data, results indicate that near the coast water levels were generally higher in May during 2000-2009 than during 1990-1999; and that inland water levels were generally lower during 2000-2009 than during 1990-1999. Generally, the 25th, 50th, and 75th percentiles of water levels from all months were also higher near the coast and lower inland during 2000–2009 than during 1990-1999. Mean October water levels during 2000-2009 were generally higher than during 1990-1999 in much of western Miami-Dade County, but were lower in a large part of eastern Miami-Dade County.

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GIS In Telecom Sector Market Size 2024-2028

The GIS in telecom sector market size is forecast to increase by USD 1.91 billion at a CAGR of 14.68% between 2023 and 2028.

Geographic Information Systems (GIS) have gained significant traction In the telecom sector due to the increasing adoption of advanced technologies such as big data, sensors, drones, and LiDAR. The use of GIS enables telecom companies to effectively manage and analyze large volumes of digital data, including satellite and GPS information, to optimize infrastructure monitoring and antenna placement. In the context of smart cities, GIS plays a crucial role in enabling efficient communication between developers and end-users by providing real-time data on construction progress and infrastructure status. Moreover, the integration of LiDAR technology with drones offers enhanced capabilities for surveying and mapping telecom infrastructure, leading to improved accuracy and efficiency.
However, the implementation of GIS In the telecom sector also presents challenges, including data security concerns and the need for servers and computers to handle the large volumes of data generated by these technologies. In summary, the telecom sector's growing reliance on digital technologies such as GIS, big data, sensors, drones, and LiDAR is driving market growth, while the need for effective data management and security solutions presents challenges that must be addressed.

What will be the Size of the GIS In Telecom Sector Market During the Forecast Period?

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The Geographic Information System (GIS) market In the telecom sector is experiencing significant growth due to the increasing demand for electronic information and visual representation of data in various industries. This market encompasses a range of hardware and software solutions, including GNSS/GPS antennas, Lidar, GIS collectors, total stations, imaging sensors, and more. Major industries such as agriculture, oil & gas, architecture, and infrastructure monitoring are leveraging GIS technology for data analysis and decision-making. The adoption rate of GIS In the telecom sector is driven by the need for efficient data management and analysis, as well as the integration of real-time data from various sources.
Data formats and sources vary widely, from satellite and aerial imagery to ground-based sensors and IoT devices. The market is also witnessing innovation from startups and established players, leading to advancements in data processing capabilities and integration with other technologies like 5G networks and AI. Applications of GIS In the telecom sector include smart urban planning, smart utilities, and smart public works, among others.

How is this GIS In Telecom Sector Industry segmented and which is the largest segment?

The GIS in telecom sector industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Product

  Software
  Data
  Services


Deployment

  On-premises
  Cloud


Geography

  APAC

    China


  North America

    Canada
    US


  Europe

    UK
    Italy


  South America



  Middle East and Africa

By Product Insights

The software segment is estimated to witness significant growth during the forecast period. The telecom sector's Global GIS market encompasses software solutions for desktops, mobiles, cloud, and servers, along with developers' platforms. companies provide industry-specific GIS software, expanding the growth potential of this segment. Telecom companies heavily utilize intelligent maps generated by GIS for informed decisions on capacity planning and enhancements, such as improved service and next-generation networks. This drives significant growth In the software segment. Commercial entities offer open-source GIS software to counteract the threat of counterfeit products.
GIS technologies are integral to telecom network management, spatial data analysis, infrastructure planning, location-based services, network coverage mapping, data visualization, asset management, real-time network monitoring, design, wireless network mapping, integration, maintenance, optimization, and geospatial intelligence. Key applications include 5G network planning, network visualization, outage management, geolocation, mobile network optimization, and smart infrastructure planning. The GIS industry caters to major industries, including agriculture, oil & gas, architecture, engineering, construction, mining, utilities, retail, healthcare, government, and smart city planning. GIS solutions facilitate real-time data management, spatial information, and non-spatial information, offering enterprise solutions and transportation applications.

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Access National Hydrography ProductsThe National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.The NHD is a national framework for assigning reach addresses to water-related entities, such as industrial discharges, drinking water supplies, fish habitat areas, wild and scenic rivers. Reach addresses establish the locations of these entities relative to one another within the NHD surface water drainage network, much like addresses on streets. Once linked to the NHD by their reach addresses, the upstream/downstream relationships of these water-related entities--and any associated information about them--can be analyzed using software tools ranging from spreadsheets to geographic information systems (GIS). GIS can also be used to combine NHD-based network analysis with other data layers, such as soils, land use and population, to help understand and display their respective effects upon one another. Furthermore, because the NHD provides a nationally consistent framework for addressing and analysis, water-related information linked to reach addresses by one organization (national, state, local) can be shared with other organizations and easily integrated into many different types of applications to the benefit of all.Statements of attribute accuracy are based on accuracy statements made for U.S. Geological Survey Digital Line Graph (DLG) data, which is estimated to be 98.5 percent. One or more of the following methods were used to test attribute accuracy: manual comparison of the source with hardcopy plots; symbolized display of the DLG on an interactive computer graphic system; selected attributes that could not be visually verified on plots or on screen were interactively queried and verified on screen. In addition, software validated feature types and characteristics against a master set of types and characteristics, checked that combinations of types and characteristics were valid, and that types and characteristics were valid for the delineation of the feature. Feature types, characteristics, and other attributes conform to the Standards for National Hydrography Dataset (USGS, 1999) as of the date they were loaded into the database. All names were validated against a current extract from the Geographic Names Information System (GNIS). The entry and identifier for the names match those in the GNIS. The association of each name to reaches has been interactively checked, however, operator error could in some cases apply a name to a wrong reach.Points, nodes, lines, and areas conform to topological rules. Lines intersect only at nodes, and all nodes anchor the ends of lines. Lines do not overshoot or undershoot other lines where they are supposed to meet. There are no duplicate lines. Lines bound areas and lines identify the areas to the left and right of the lines. Gaps and overlaps among areas do not exist. All areas close.The completeness of the data reflects the content of the sources, which most often are the published USGS topographic quadrangle and/or the USDA Forest Service Primary Base Series (PBS) map. The USGS topographic quadrangle is usually supplemented by Digital Orthophoto Quadrangles (DOQs). Features found on the ground may have been eliminated or generalized on the source map because of scale and legibility constraints. In general, streams longer than one mile (approximately 1.6 kilometers) were collected. Most streams that flow from a lake were collected regardless of their length. Only definite channels were collected so not all swamp/marsh features have stream/rivers delineated through them. Lake/ponds having an area greater than 6 acres were collected. Note, however, that these general rules were applied unevenly among maps during compilation. Reach codes are defined on all features of type stream/river, canal/ditch, artificial path, coastline, and connector. Waterbody reach codes are defined on all lake/pond and most reservoir features. Names were applied from the GNIS database. Detailed capture conditions are provided for every feature type in the Standards for National Hydrography Dataset available online through https://prd-wret.s3-us-west-2.amazonaws.com/assets/palladium/production/atoms/files/NHD%201999%20Draft%20Standards%20-%20Capture%20conditions.PDF.Statements of horizontal positional accuracy are based on accuracy statements made for U.S. Geological Survey topographic quadrangle maps. These maps were compiled to meet National Map Accuracy Standards. For horizontal accuracy, this standard is met if at least 90 percent of points tested are within 0.02 inch (at map scale) of the true position. Additional offsets to positions may have been introduced where feature density is high to improve the legibility of map symbols. In addition, the digitizing of maps is estimated to contain a horizontal positional error of less than or equal to 0.003 inch standard error (at map scale) in the two component directions relative to the source maps. Visual comparison between the map graphic (including digital scans of the graphic) and plots or digital displays of points, lines, and areas, is used as control to assess the positional accuracy of digital data. Digital map elements along the adjoining edges of data sets are aligned if they are within a 0.02 inch tolerance (at map scale). Features with like dimensionality (for example, features that all are delineated with lines), with or without like characteristics, that are within the tolerance are aligned by moving the features equally to a common point. Features outside the tolerance are not moved; instead, a feature of type connector is added to join the features.Statements of vertical positional accuracy for elevation of water surfaces are based on accuracy statements made for U.S. Geological Survey topographic quadrangle maps. These maps were compiled to meet National Map Accuracy Standards. For vertical accuracy, this standard is met if at least 90 percent of well-defined points tested are within one-half contour interval of the correct value. Elevations of water surface printed on the published map meet this standard; the contour intervals of the maps vary. These elevations were transcribed into the digital data; the accuracy of this transcription was checked by visual comparison between the data and the map.

Background and Data Limitations The Massachusetts 1830 map series represents a unique data source that depicts land cover and cultural features during the historical period of widespread land clearing for agricultural. To our knowledge, Massachusetts is the only state in the US where detailed land cover information was comprehensively mapped at such an early date. As a result, these maps provide unusual insight into land cover and cultural patterns in 19th century New England. However, as with any historical data, the limitations and appropriate uses of these data must be recognized: (1) These maps were originally developed by many different surveyors across the state, with varying levels of effort and accuracy. (2) It is apparent that original mapping did not follow consistent surveying or drafting protocols; for instance, no consistent minimum mapping unit was identified or used by different surveyors; as a result, whereas some maps depict only large forest blocks, others also depict small wooded areas, suggesting that numerous smaller woodlands may have gone unmapped in many towns. Surveyors also were apparently not consistent in what they mapped as ‘woodlands’: comparison with independently collected tax valuation data from the same time period indicates substantial lack of consistency among towns in the relative amounts of ‘woodlands’, ‘unimproved’ lands, and ‘unimproveable’ lands that were mapped as ‘woodlands’ on the 1830 maps. In some instances, the lack of consistent mapping protocols resulted in substantially different patterns of forest cover being depicted on maps from adjoining towns that may in fact have had relatively similar forest patterns or in woodlands that ‘end’ at a town boundary. (3) The degree to which these maps represent approximations of ‘primary’ woodlands (i.e., areas that were never cleared for agriculture during the historical period, but were generally logged for wood products) varies considerably from town to town, depending on whether agricultural land clearing peaked prior to, during, or substantially after 1830. (4) Despite our efforts to accurately geo-reference and digitize these maps, a variety of additional sources of error were introduced in converting the mapped information to electronic data files (see detailed methods below). Thus, we urge considerable caution in interpreting these maps. Despite these limitations, the 1830 maps present an incredible wealth of information about land cover patterns and cultural features during the early 19th century, a period that continues to exert strong influence on the natural and cultural landscapes of the region. For users without access to GIS software, the data are available for viewing at: http://harvardforest.fas.harvard.edu/research/1830instructions.html Acknowledgements Financial support for this project was provided by the BioMap Project of the Massachusetts Natural Heritage and Endangered Species Program, the National Science Foundation, and the Andrew Mellon Foundation. This project is a contribution of the Harvard Forest Long Term Ecological Research Program.

Statistical analyses and maps representing mean, high, and low water-level conditions in the surface water and groundwater of Miami-Dade County were made by the U.S. Geological Survey, in cooperation with the Miami-Dade County Department of Regulatory and Economic Resources, to help inform decisions necessary for urban planning and development. Sixteen maps were created that show contours of (1) the mean of daily water levels at each site during October and May for the 2000-2009 water years; (2) the 25th, 50th, and 75th percentiles of the daily water levels at each site during October and May and for all months during 2000-2009; and (3) the differences between mean October and May water levels, as well as the differences in the percentiles of water levels for all months, between 1990-1999 and 2000-2009. The 80th, 90th, and 96th percentiles of the annual maximums of daily groundwater levels during 1974-2009 (a 35-year period) were computed to provide an indication of unusually high groundwater-level conditions. These maps and statistics provide a generalized understanding of the variations of water levels in the aquifer, rather than a survey of concurrent water levels. Water-level measurements from 473 sites in Miami-Dade County and surrounding counties were analyzed to generate statistical analyses. The monitored water levels included surface-water levels in canals and wetland areas and groundwater levels in the Biscayne aquifer. Maps were created by importing site coordinates, summary water-level statistics, and completeness of record statistics into a geographic information system, and by interpolating between water levels at monitoring sites in the canals and water levels along the coastline. Raster surfaces were created from these data by using the triangular irregular network interpolation method. The raster surfaces were contoured by using geographic information system software. These contours were imprecise in some areas because the software could not fully evaluate the hydrology given available information; therefore, contours were manually modified where necessary. The ability to evaluate differences in water levels between 1990-1999 and 2000-2009 is limited in some areas because most of the monitoring sites did not have 80 percent complete records for one or both of these periods. The quality of the analyses was limited by (1) deficiencies in spatial coverage; (2) the combination of pre- and post-construction water levels in areas where canals, levees, retention basins, detention basins, or water-control structures were installed or removed; (3) an inability to address the potential effects of the vertical hydraulic head gradient on water levels in wells of different depths; and (4) an inability to correct for the differences between daily water-level statistics. Contours are dashed in areas where the locations of contours have been approximated because of the uncertainty caused by these limitations. Although the ability of the maps to depict differences in water levels between 1990-1999 and 2000-2009 was limited by missing data, results indicate that near the coast water levels were generally higher in May during 2000-2009 than during 1990-1999; and that inland water levels were generally lower during 2000-2009 than during 1990-1999. Generally, the 25th, 50th, and 75th percentiles of water levels from all months were also higher near the coast and lower inland during 2000–2009 than during 1990-1999. Mean October water levels during 2000-2009 were generally higher than during 1990-1999 in much of western Miami-Dade County, but were lower in a large part of eastern Miami-Dade County.

The CAD and GIS printer market is projected to reach a value of XXX million by 2033, growing at a CAGR of XX% from 2025 to 2033. The growth of the market is primarily driven by the increasing demand for high-quality prints in various industries, including architecture, engineering, construction, and manufacturing. Additionally, the rising adoption of computer-aided design (CAD) and geographic information systems (GIS) software is further fueling the demand for specialized printers capable of handling complex designs and maps. The market for CAD and GIS printers is segmented based on application, type, and region. Major players in the market include Canon, Epson, Hewlett-Packard, Roland, Konica Minolta, Mimaki Engineering, Mutoh, and Ricoh. North America and Europe are expected to remain key regional markets, followed by Asia-Pacific. Emerging markets in Latin America, the Middle East, and Africa are also expected to contribute to the growth of the market in the coming years.

Statistical analyses and maps representing mean, high, and low water-level conditions in the surface water and groundwater of Miami-Dade County were made by the U.S. Geological Survey, in cooperation with the Miami-Dade County Department of Regulatory and Economic Resources, to help inform decisions necessary for urban planning and development. Sixteen maps were created that show contours of (1) the mean of daily water levels at each site during October and May for the 2000-2009 water years; (2) the 25th, 50th, and 75th percentiles of the daily water levels at each site during October and May and for all months during 2000-2009; and (3) the differences between mean October and May water levels, as well as the differences in the percentiles of water levels for all months, between 1990-1999 and 2000-2009. The 80th, 90th, and 96th percentiles of the annual maximums of daily groundwater levels during 1974-2009 (a 35-year period) were computed to provide an indication of unusually high groundwater-level conditions. These maps and statistics provide a generalized understanding of the variations of water levels in the aquifer, rather than a survey of concurrent water levels. Water-level measurements from 473 sites in Miami-Dade County and surrounding counties were analyzed to generate statistical analyses. The monitored water levels included surface-water levels in canals and wetland areas and groundwater levels in the Biscayne aquifer. Maps were created by importing site coordinates, summary water-level statistics, and completeness of record statistics into a geographic information system, and by interpolating between water levels at monitoring sites in the canals and water levels along the coastline. Raster surfaces were created from these data by using the triangular irregular network interpolation method. The raster surfaces were contoured by using geographic information system software. These contours were imprecise in some areas because the software could not fully evaluate the hydrology given available information; therefore, contours were manually modified where necessary. The ability to evaluate differences in water levels between 1990-1999 and 2000-2009 is limited in some areas because most of the monitoring sites did not have 80 percent complete records for one or both of these periods. The quality of the analyses was limited by (1) deficiencies in spatial coverage; (2) the combination of pre- and post-construction water levels in areas where canals, levees, retention basins, detention basins, or water-control structures were installed or removed; (3) an inability to address the potential effects of the vertical hydraulic head gradient on water levels in wells of different depths; and (4) an inability to correct for the differences between daily water-level statistics. Contours are dashed in areas where the locations of contours have been approximated because of the uncertainty caused by these limitations. Although the ability of the maps to depict differences in water levels between 1990-1999 and 2000-2009 was limited by missing data, results indicate that near the coast water levels were generally higher in May during 2000-2009 than during 1990-1999; and that inland water levels were generally lower during 2000-2009 than during 1990-1999. Generally, the 25th, 50th, and 75th percentiles of water levels from all months were also higher near the coast and lower inland during 2000–2009 than during 1990-1999. Mean October water levels during 2000-2009 were generally higher than during 1990-1999 in much of western Miami-Dade County, but were lower in a large part of eastern Miami-Dade County.

Geospatial Solutions Market size was valued at USD 282.75 Billion in 2024 and is projected to reach USD 650.14 Billion by 2031, growing at a CAGR of 12.10% during the forecast period 2024-2031.

Geospatial Solutions Market: Definition/ Overview

Geospatial solutions are applications and technologies that use spatial data to address geography, location, and Earth’s surface problems. They use tools like GIS, remote sensing, GPS, satellite imagery analysis, and spatial modelling. These solutions enable informed decision-making, resource allocation optimization, asset management, environmental monitoring, infrastructure planning, and addressing challenges in sectors like urban planning, agriculture, transportation, disaster management, and natural resource management. They empower users to harness spatial information for better understanding and decision-making in various contexts.

Geospatial solutions are technologies and methodologies used to analyze and visualize spatial data, ranging from urban planning to agriculture. They use GIS, remote sensing, and GNSS to gather, process, and interpret data. These solutions help users make informed decisions, solve complex problems, optimize resource allocation, and enhance situational awareness. They are crucial in addressing challenges and unlocking opportunities in today’s interconnected world, such as mapping land use patterns, monitoring ecosystem changes, and real-time asset tracking.

Kuwait's arid desert landscape, geological formations, and extreme climate conditions make it a potential site for establishing a terrestrial Mars analog, as this research presents a new GIS-based methodology. The Analog Conjunctive Method (ACM) was specifically developed to identify a suitable location in Kuwait to hold a terrestrial Mars analog using a geographic information system (GIS) and remote sensing techniques. Analogs play a crucial role in simulating different Martian conditions, supporting astronaut training, testing various exploration technologies, and doing different types of scientific research on these environments. The ACM method integrates GIS and remote sensing techniques to evaluate the study area, resulting in potential sites for analog. The analysis employs two stages to finalize the best location. In stage one, the newly developed ACM is applied; it systematically eliminates unstable areas while allowing minimal flexibility for real-world environmental adjustment, particularly in regions with natural wind barriers. ACM is used to process the buffers created for the seven criteria (urban areas and farms, coastal areas, streets, airports, oil fields, natural reserves, and country borders) in QGIS to exclude unsuitable areas. Stage two screens the stage one map locations using different data (STRM, Copernicus sentinel-2, and field visits) to polish the selection based on other criteria (water bodies, dust rate, vegetation cover, and topography). The result shows nine locations in Jal Al-Zor as potential analog sites where a random location is selected for a 3D model creation to visualize the analog. Java Mission-planning and Analysis for Remote Sensing (JMARS) software was used to identify similarities between specific areas, such as the Jal Al-Zor escarpment and Huwaimllyah sand dunes in the Kuwait desert, and comparable terrains on Mars. The research concluded that Jal Al-Zor holds substantial potential as a terrestrial Mars analog site due to its geological and topographical similarities to Martian landscapes. This makes it an ideal location for crew training, Mars equipment testing, and further research in Mars analog studies, providing valuable insights for future planetary exploration.

DVRPC has tracked regional land use changes and the preservation of land for decades. Since 1970, DVRPC has produced land use files derived from aerial photography in our nine-county region. Starting in 1990, these digital land use files have been produced every five years using Computer Assisted Mapping and Geographic Information Systems software. Methods and technology were updated for the 2000 inventory and subsequent releases. Although reasonable comparisons can be drawn between DVRPC's land use files produced in 2005 or later, users should be cautious when comparing these later files to earlier land use data, particularly within specific developed land use categories and/or specific municipalities.

DVRPC also maintains an inventory of protected public and private open space in the region. The inventory tracks all publicly owned open space, and preserved farmland and nonprofit-protected open space (both of which are typically still owned by private landowners). States, counties, municipalities, and nonprofits (such as land trusts and conservancies) may purchase a parcel of land outright for preservation or purchase the development rights to the parcel. Purchase of development rights is common for farmland, enabling the farmer landowner to continue working on the land. Outright purchase of the parcel (not just development rights but full ownership of the parcel) can be accomplished by both public and private entities. Funding sources for land conservation vary, but public entities typically use tax funds, while nonprofits may use a combination of public and private sources, including income and estate tax deductions.

Statistical analyses and maps representing mean, high, and low water-level conditions in the surface water and groundwater of Miami-Dade County were made by the U.S. Geological Survey, in cooperation with the Miami-Dade County Department of Regulatory and Economic Resources, to help inform decisions necessary for urban planning and development. Sixteen maps were created that show contours of (1) the mean of daily water levels at each site during October and May for the 2000-2009 water years; (2) the 25th, 50th, and 75th percentiles of the daily water levels at each site during October and May and for all months during 2000-2009; and (3) the differences between mean October and May water levels, as well as the differences in the percentiles of water levels for all months, between 1990-1999 and 2000-2009. The 80th, 90th, and 96th percentiles of the annual maximums of daily groundwater levels during 1974-2009 (a 35-year period) were computed to provide an indication of unusually high groundwater-level conditions. These maps and statistics provide a generalized understanding of the variations of water levels in the aquifer, rather than a survey of concurrent water levels. Water-level measurements from 473 sites in Miami-Dade County and surrounding counties were analyzed to generate statistical analyses. The monitored water levels included surface-water levels in canals and wetland areas and groundwater levels in the Biscayne aquifer. Maps were created by importing site coordinates, summary water-level statistics, and completeness of record statistics into a geographic information system, and by interpolating between water levels at monitoring sites in the canals and water levels along the coastline. Raster surfaces were created from these data by using the triangular irregular network interpolation method. The raster surfaces were contoured by using geographic information system software. These contours were imprecise in some areas because the software could not fully evaluate the hydrology given available information; therefore, contours were manually modified where necessary. The ability to evaluate differences in water levels between 1990-1999 and 2000-2009 is limited in some areas because most of the monitoring sites did not have 80 percent complete records for one or both of these periods. The quality of the analyses was limited by (1) deficiencies in spatial coverage; (2) the combination of pre- and post-construction water levels in areas where canals, levees, retention basins, detention basins, or water-control structures were installed or removed; (3) an inability to address the potential effects of the vertical hydraulic head gradient on water levels in wells of different depths; and (4) an inability to correct for the differences between daily water-level statistics. Contours are dashed in areas where the locations of contours have been approximated because of the uncertainty caused by these limitations. Although the ability of the maps to depict differences in water levels between 1990-1999 and 2000-2009 was limited by missing data, results indicate that near the coast water levels were generally higher in May during 2000-2009 than during 1990-1999; and that inland water levels were generally lower during 2000-2009 than during 1990-1999. Generally, the 25th, 50th, and 75th percentiles of water levels from all months were also higher near the coast and lower inland during 2000-2009 than during 1990-1999. Mean October water levels during 2000-2009 were generally higher than during 1990-1999 in much of western Miami-Dade County, but were lower in a large part of eastern Miami-Dade County.

This parcels polygons feature class represents current city parcels within the City of Los Angeles. It shares topology with the Landbase parcel lines feature class. The Mapping and Land Records Division of the Bureau of Engineering, Department of Public Works provides the most current geographic information of the public right of way, ownership and land record information. The legal boundaries are determined on the ground by license surveyors in the State of California, and by recorded documents from the Los Angeles County Recorder's office and the City Clerk's office of the City of Los Angeles. Parcel and ownership information are available on NavigateLA, a website hosted by the Bureau of Engineering, Department of Public Works.Associated information about the landbase parcels is entered into attributes. Principal attributes include:PIN and PIND: represents the unique auto-generated parcel identifier and key to related features and tables. This field is related to the LA_LEGAL, LA_APN and LA_HSE_NBR tables. PIN contains spaces and PIND replaces those spaces with a dash (-).LA_LEGAL - Table attributes containing legal description. Principal attributes include the following:TRACT: The subdivision tract number as recorded by the County of Los AngelesMAP_REF: Identifies the subdivision map book reference as recorded by the County of Los Angeles.LOT: The subdivision lot number as recorded by the County of Los Angeles.ENG_DIST: The four engineering Districts (W=Westla, C=Central, V= Valley and H=Harbor).CNCL_DIST: Council Districts 1-15 of the City of Los Angeles. OUTLA means parcel is outside the City.LA_APN- Table attributes containing County of Los Angeles Assessors information. Principal attributes include the following:BPP: The Book, Page and Parcel from the Los Angeles County Assessors office. SITUS*: Address for the property.LA_HSE_NBR - Table attributes containing housenumber information. Principal attributes include the following:HSE_ID: Unique id of each housenumber record.HSE_NBR: housenumber numerical valueSTR_*: Official housenumber addressFor a complete list of attribute values, please refer to Landbase_parcel_polygons_data_dictionary.Landbase parcels polygons data layer was created in geographical information systems (GIS) software to display the location of the right of way. The parcels polygons layer delineates the right of way from Landbase parcels lots. The parcels polygons layer is a feature class in the LACityLandbaseData.gdb Geodatabase dataset. The layer consists of spatial data as a polygon feature class and attribute data for the features. The area inside a polygon feature is a parcel lot. The area outside of the parcel polygon feature is the right of way. Several polygon features are adjacent, sharing one line between two polygons. For each parcel, there is a unique identifier in the PIND and PIN fields. The only difference is PIND has a dash and PIN does not. The types of edits include new subdivisions and lot cuts. Associated legal information about the landbase parcels lots is entered into attributes. The landbase parcels layer is vital to other City of LA Departments, by supporting property and land record operations and identifying legal information for City of Los Angeles. The landbase parcels polygons are inherited from a database originally created by the City's Survey and Mapping Division. Parcel information should only be added to the Landbase Parcels layer if documentation exists, such as a Deed or a Plan approved by the City Council. When seeking the definitive description of real property, consult the recorded Deed or Plan.List of Fields:ID: A unique numeric identifier of the polygon. The ID value is the last part of the PIN field value.ASSETID: User-defined feature autonumber.MAPSHEET: The alpha-numeric mapsheet number, which refers to a valid B-map or A-map number on the Cadastral grid index map. Values: • B, A, -5A - Any of these alpha-numeric combinations are used, whereas the underlined spaces are the numbers. An A-map is the smallest grid in the index map and is used when there is a large amount of spatial information in the map display. There are more parcel lines and annotation than can fit in the B-map, and thus, an A-map is used. There are 4 A-maps in a B-map. In areas where parcel lines and annotation can fit comfortably in an index map, a B-map is used. The B-maps are at a scale of 100 feet, and A-maps are at a scale of 50 feet.OBJECTID: Internal feature number.BPPMAP_REFTRACTBLOCKMODLOTARBCNCL_DIST: LA City Council District. Values: • (numbers 1-15) - Current City Council Member for that District can be found on the mapping website http://navigatela.lacity.org/navigatela, click Council Districts layer name, under Boundaries layer group.SHAPE: Feature geometry.BOOKPAGEPARCELPIND: The value is a combination of MAPSHEET and ID fields, creating a unique value for each parcel. The D in the field name PIND, means "dash", and there is a dash between the MAPSHEET and ID field values. This is a key attribute of the LANDBASE data layer. This field is related to the APN and HSE_NBR tables.ENG_DIST: LA City Engineering District. The boundaries are displayed in the Engineering Districts index map. Values: • H - Harbor Engineering District. • C - Central Engineering District. • V - Valley Engineering District. • W - West LA Engineering District.PIN: The value is a combination of MAPSHEET and ID fields, creating a unique value for each parcel. There are spaces between the MAPSHEET and ID field values. This is a key attribute of the LANDBASE data layer. This field is related to the APN and HSE_NBR tables.

These maps are georeferenced versions of the maps produced by The University Museum, University of Pennsylvania, project at Tikal, Guatemala and published as Tikal Report 11. These georeferenced maps are intended for use with GIS (Geographic Information System) software. The maps should be useful for archaeologists, tourists and managers of Tikal National Park. This map set consists of eleven georeferenced maps. The set includes two versions of the overview map of the central sixteen square kilometers of Tikal—the "Ruins of Tikal" map. One version includes the map border. The other version is without the border. The nine remaining maps cover the inner nine square kilometers in detail, without borders. The maps were georeferenced as part of a University of Cincinnati project in Tikal, under permit of the Guatemalan government. The UC Project georeferenced the maps using land survey methods. We created transformation equations based on a point of beginning, a reference direction and a map scale. Directions and distances on the ground were transformed into UTM projected directions and distances. The point of beginning was the Petty Company benchmark shown on the "Camp Quad" map. In 2010 we determined the location with a GPS receiver. We accessed both the horizontal and vertical accuracy of the georeferenced maps. Based on 96 test points spread throughout the area of the maps, we found the median horizontal accuracy of the maps, compared to GPS, to be 5.6 meters. Based on 103 test points spread throughout the area of the maps, we found the median vertical accuracy of the maps, compared to a NASA radar altimetry mission, to be 2.1 meters. The borders of the maps were removed so the set of maps will “seamlessly” fit together in GIS. See Tikal Report No.11 for versions of the maps with borders (one version of the georeferenced "Ruins of Tikal" map includes the border). The georeferencing files are optimized for use in ArcGIS version 9.2 and beyond. The PDF file of TR11 from which these maps were extracted was made with the generous assistance of the University Museum Library and the Tikal Archives. Details of the georeferencing and accuracy check are in a report to the Dirección Patrimonio Cultural y Natural de Guatemala: Christopher Carr, Eric Weaver, Nicholas Dunning, and Vernon Scarborough (2011) EVALUACIÓN DE LA EXACTITUD DE LOS MAPAS DE TIKAL DE LA UNIVERSIDAD DE PENNSYLVANIA, POR GPS Y ESTACIÓN TOTAL (Accuracy assessment of the Penn Project maps of Tikal, by GPS and Total Station). In Lentz, D., C. Ramos, N. Dunning, V. Scarborough and L. Grazioso. PROYECTO DE SILVICULTURA Y MANEJO DE AGUAS DE LOS ANTIGUOS MAYAS DE TIKAL. Additional details of the strategies the Penn Project used to produce these high quality maps, the georeferencing methodology, and the accuracy check process are forthcoming in a book chapter. The book is on the UC project at Tikal, to be published by Cambridge University Press. The chapter is Carr, Weaver, Dunning and Scarborough. Bringing the University of Pennsylvania maps of Tikal into the era of electronic GIS. In Lentz, Dunning, Scarborough (eds). Tikal and Maya Ecology: Water, Landscapes and Resilience. Permission to publish these maps must be secured from: The University of Pennsylvania Museum of Archaeology and Anthropology, 3260 South Street, Philadelphia, PA 19104, Tel: (215) 898-4050, Fax: (215) 573-9369, Email: publications@pennmuseum.org. .................................................................................................................. Estos mapas son versiones georeferenciados de los mapas producidos por el Museo Universitario de la Universidad de Pennsylvania, Proyecto Tikal, Guatemala y publicado como Informe de Tikal No. 11. La intensión de estos mapas georeferenciados es para ser utilizados con el Sistema de Información Geográfica (SIG). Los mapas deben ser útiles para los arqueólogos, los turistas y los administradores del Parque Nacional Tikal. Este conjunto de mapas consta de once mapas georreferenciados. El juego incluye dos versiones del mapa general de los 16 km2 centrales del mapa de las "Ruins of Tikal". Una versión del mapa incluye sus encuadrados. La otra versión esta sin los encuadrados. Los nueve mapas restantes cubren los mapas interiores de 9 km2 en detalle, sin encuadrados. Los mapas fueron georeferenciados como parte de un proyecto de la Universidad de Cincinnati en Tikal, con permiso del Ministerio de Cultura y Deportes del Gobierno de Guatemala. El Proyecto de la Universidad de Cincinnati georeferenció los mapas utilizando métodos de reconocimiento de campo. Creamos ecuaciones de transformación basado en un punto de inicio, una dirección de referencia y un mapa a escala. Direcciones y distancias en el campo se transformaron en direcciones proyectadas UTM y distancias. El punto de inicio fue el punto de referencia que la empresa Petty muestra en el mapa "Camp Quad". En 2010 se determinó la ubicación de un receptor GPS. Verificamos tanto la precisión horizontal como vertical de los mapas georreferenciados. Sobre la base de 96 puntos de prueba dispersos en el área de los mapas, encontramos la exactitud promedio horizontal de los mapas, en comparación con GPS, a ser 5.6 metros. Sobre la base de 103 puntos de prueba dispersos en el área de los mapas, encontramos la exactitud vertical promedio de los mapas, en comparación con una misión de radar de altimetría de la NASA, que es de 2.1 metros. Los encuadres de los mapas fueron retirados por lo que el conjunto de mapas "sin encuadres" encajan en SIG. Véase el Informe de Tikal No. 11 para las versiones de los mapas con los encuadrados. (una versión de las "Ruins of Tikal" de mapas georeferenciados incluyen el encuadrado). Los archivos de georeferenciación están optimizados para su uso en ArcGIS versión 9.2 u otra superior. El archivo PDF de TR11 con que estos mapas fueron extraídos se realizó con la ayuda generosa de la University Museum Library y el Tikal Archives. Los detalles de la comprobación de georeferenciación y la precisión estarán en un informe a entregar a la Dirección General del Patrimonio Cultural y Natural del Ministerio de Cultura y Deportes de Guatemala: Christopher Carr, Eric Weaver, Dunning Nicholas, y Vernon Scarborough (2011) EVALUACION DE LA EXACTITUD DE LOS MAPAS DE TIKAL DE LA UNIVERSIDAD DE PENSILVANIA, POR GPS Y ESTACIÓN TOTAL (Accuracy assessment of the Penn Project maps of Tikal, by GPS and Total Station). En Lentz, D., C. Ramos, Dunning N., Scarborough V. y Grazioso L., PROYECTO DE SILVICULTURA Y MANEJO DE AGUAS DE LOS ANTIGUOS MAYAS DE TIKAL. Otros detalles de las estrategias del Proyecto de Pennsylvania utilizados para producir estos mapas de alta calidad, la metodología de georeferenciación y el proceso de control de precisión se publicarán en un capítulo de un libro. El libro está en proyecto en la Universidad de Cincinnati en Tikal, que será publicado por Cambridge University Press. El capítulo es: Carr, Weaver, Dunning y Scarborough. Bringing the University of Pennsylvania maps of Tikal into the era of electronic GIS. En Lentz, Dunning, Scarborough (eds). Tikal and Maya Ecology: Water, Landscapes and Resilience. El permiso para publicar estos mapas debe obtenerse de: The University of Pennsylvania Museum of Archaeology and Anthropology, 3260 South Street, Philadelphia, PA 19104, Tel: (215) 898-4050, Fax: (215) 573-9369, Email: publications@pennmuseum.org. Revisado por E. Ponciano Abril de 2013.

This street centerline lines feature class represents current right of way in the City of Los Angeles. It shows the official street names and is related to the official street name data. The Mapping and Land Records Division of the Bureau of Engineering, Department of Public Works provides the most current geographic information of the public right of way. The right of way information is available on NavigateLA, a website hosted by the Bureau of Engineering, Department of Public Works. Street Centerline layer was created in geographical information systems (GIS) software to display Dedicated street centerlines. The street centerline layer is a feature class in the LACityCenterlineData.gdb Geodatabase dataset. The layer consists of spatial data as a line feature class and attribute data for the features. City of LA District Offices use Street Centerline layer to determine dedication and street improvement requirements. Engineering street standards are followed to dedicate the street for development. The Bureau of Street Services tracks the location of existing streets, who need to maintain that road. Additional information was added to Street Centerline layer. Address range attributes were added make layer useful for geocoding. Section ID values from Bureau of Street Services were added to make layer useful for pavement management. Department of City Planning added street designation attributes taken from Community Plan maps. The street centerline relates to the Official Street Name table named EASIS, Engineering Automated Street Inventory System, which contains data describing the limits of the street segment. A street centerline segment should only be added to the Street Centerline layer if documentation exists, such as a Deed or a Plan approved by the City Council. Paper streets are street lines shown on a recorded plan but have not yet come into existence on the ground. These street centerline segments are in the Street Centerline layer because there is documentation such as a Deed or a Plan for the construction of that street. Previously, some street line features were added although documentation did not exist. Currently, a Deed, Tract, or a Plan must exist in order to add street line features. Many street line features were edited by viewing the Thomas Bros Map's Transportation layer, TRNL_037 coverage, back when the street centerline coverage was created. When TBM and BOE street centerline layers were compared visually, TBM's layer contained many valid streets that BOE layer did not contain. In addition to TBM streets, Planning Department requested adding street line segments they use for reference. Further, the street centerline layer features are split where the lines intersect. The intersection point is created and maintained in the Intersection layer. The intersection attributes are used in the Intersection search function on NavigateLA on BOE's web mapping application NavigateLA. The City of Los Angeles Municipal code states, all public right-of-ways (roads, alleys, etc) are streets, thus all of them have intersections. Note that there are named alleys in the BOE Street Centerline layer. Since the line features for named alleys are stored in the Street Centerline layer, there are no line features for named alleys in those areas that are geographically coincident in the Alley layer. For a named alley , the corresponding record contains the street designation field value of ST_DESIG = 20, and there is a name stored in the STNAME and STSFX fields.List of Fields:SHAPE: Feature geometry.OBJECTID: Internal feature number.STNAME_A: Street name Alias.ST_SUBTYPE: Street subtype.SV_STATUS: Status of street in service, whether the street is an accessible roadway. Values: • Y - Yes • N - NoTDIR: Street direction. Values: • S - South • N - North • E - East • W - WestADLF: From address range, left side.ZIP_R: Zip code right.ADRT: To address range, right side.INT_ID_TO: Street intersection identification number at the line segment's end node. The value relates to the intersection layer attribute table, to the CL_NODE_ID field. The values are assigned automatically and consecutively by the ArcGIS software first to the street centerline data layer and then the intersections data layer, during the creation of new intersection points. Each intersection identification number is a unique value.SECT_ID: Section ID used by the Bureau of Street Services. Values: • none - No Section ID value • private - Private street • closed - Street is closed from service • temp - Temporary • propose - Proposed construction of a street • walk - Street line is a walk or walkway • known as - • numeric value - A 7 digit numeric value for street resurfacing • outside - Street line segment is outside the City of Los Angeles boundary • pierce - Street segment type • alley - Named alleySTSFX_A: Street suffix Alias.SFXDIR: Street direction suffix Values: • N - North • E - East • W - West • S - SouthCRTN_DT: Creation date of the polygon feature.STNAME: Street name.ZIP_L: Zip code left.STSFX: Street suffix. Values: • BLVD - BoulevardADLT: To address range, left side.ID: Unique line segment identifierMAPSHEET: The alpha-numeric mapsheet number, which refers to a valid B-map or A-map number on the Cadastral tract index map. Values: • B, A, -5A - Any of these alpha-numeric combinations are used, whereas the underlined spaces are the numbers.STNUM: Street identification number. This field relates to the Official Street Name table named EASIS, to the corresponding STR_ID field.ASSETID: User-defined feature autonumber.TEMP: This attribute is no longer used. This attribute was used to enter 'R' for reference arc line segments that were added to the spatial data, in coverage format. Reference lines were temporary and not part of the final data layer. After editing the permanent line segments, the user would delete temporary lines given by this attribute.LST_MODF_DT: Last modification date of the polygon feature.REMARKS: This attribute is a combination of remarks about the street centerline. Values include a general remark, the Council File number, which refers the street status, or whether a private street is a private driveway. The Council File number can be researched on the City Clerk's website http://cityclerk.lacity.org/lacityclerkconnect/INT_ID_FROM: Street intersection identification number at the line segment's start node. The value relates to the intersection layer attribute table, to the CL_NODE_ID field. The values are assigned automatically and consecutively by the ArcGIS software first to the street centerline data layer and then the intersections data layer, during the creation of new intersection points. Each intersection identification number is a unique value.ADRF: From address range, right side.

Access National Hydrography ProductsThe National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.The NHD is a national framework for assigning reach addresses to water-related entities, such as industrial discharges, drinking water supplies, fish habitat areas, wild and scenic rivers. Reach addresses establish the locations of these entities relative to one another within the NHD surface water drainage network, much like addresses on streets. Once linked to the NHD by their reach addresses, the upstream/downstream relationships of these water-related entities--and any associated information about them--can be analyzed using software tools ranging from spreadsheets to geographic information systems (GIS). GIS can also be used to combine NHD-based network analysis with other data layers, such as soils, land use and population, to help understand and display their respective effects upon one another. Furthermore, because the NHD provides a nationally consistent framework for addressing and analysis, water-related information linked to reach addresses by one organization (national, state, local) can be shared with other organizations and easily integrated into many different types of applications to the benefit of all.Statements of attribute accuracy are based on accuracy statements made for U.S. Geological Survey Digital Line Graph (DLG) data, which is estimated to be 98.5 percent. One or more of the following methods were used to test attribute accuracy: manual comparison of the source with hardcopy plots; symbolized display of the DLG on an interactive computer graphic system; selected attributes that could not be visually verified on plots or on screen were interactively queried and verified on screen. In addition, software validated feature types and characteristics against a master set of types and characteristics, checked that combinations of types and characteristics were valid, and that types and characteristics were valid for the delineation of the feature. Feature types, characteristics, and other attributes conform to the Standards for National Hydrography Dataset (USGS, 1999) as of the date they were loaded into the database. All names were validated against a current extract from the Geographic Names Information System (GNIS). The entry and identifier for the names match those in the GNIS. The association of each name to reaches has been interactively checked, however, operator error could in some cases apply a name to a wrong reach.Points, nodes, lines, and areas conform to topological rules. Lines intersect only at nodes, and all nodes anchor the ends of lines. Lines do not overshoot or undershoot other lines where they are supposed to meet. There are no duplicate lines. Lines bound areas and lines identify the areas to the left and right of the lines. Gaps and overlaps among areas do not exist. All areas close.The completeness of the data reflects the content of the sources, which most often are the published USGS topographic quadrangle and/or the USDA Forest Service Primary Base Series (PBS) map. The USGS topographic quadrangle is usually supplemented by Digital Orthophoto Quadrangles (DOQs). Features found on the ground may have been eliminated or generalized on the source map because of scale and legibility constraints. In general, streams longer than one mile (approximately 1.6 kilometers) were collected. Most streams that flow from a lake were collected regardless of their length. Only definite channels were collected so not all swamp/marsh features have stream/rivers delineated through them. Lake/ponds having an area greater than 6 acres were collected. Note, however, that these general rules were applied unevenly among maps during compilation. Reach codes are defined on all features of type stream/river, canal/ditch, artificial path, coastline, and connector. Waterbody reach codes are defined on all lake/pond and most reservoir features. Names were applied from the GNIS database. Detailed capture conditions are provided for every feature type in the Standards for National Hydrography Dataset available online through https://prd-wret.s3-us-west-2.amazonaws.com/assets/palladium/production/atoms/files/NHD%201999%20Draft%20Standards%20-%20Capture%20conditions.PDF.Statements of horizontal positional accuracy are based on accuracy statements made for U.S. Geological Survey topographic quadrangle maps. These maps were compiled to meet National Map Accuracy Standards. For horizontal accuracy, this standard is met if at least 90 percent of points tested are within 0.02 inch (at map scale) of the true position. Additional offsets to positions may have been introduced where feature density is high to improve the legibility of map symbols. In addition, the digitizing of maps is estimated to contain a horizontal positional error of less than or equal to 0.003 inch standard error (at map scale) in the two component directions relative to the source maps. Visual comparison between the map graphic (including digital scans of the graphic) and plots or digital displays of points, lines, and areas, is used as control to assess the positional accuracy of digital data. Digital map elements along the adjoining edges of data sets are aligned if they are within a 0.02 inch tolerance (at map scale). Features with like dimensionality (for example, features that all are delineated with lines), with or without like characteristics, that are within the tolerance are aligned by moving the features equally to a common point. Features outside the tolerance are not moved; instead, a feature of type connector is added to join the features.Statements of vertical positional accuracy for elevation of water surfaces are based on accuracy statements made for U.S. Geological Survey topographic quadrangle maps. These maps were compiled to meet National Map Accuracy Standards. For vertical accuracy, this standard is met if at least 90 percent of well-defined points tested are within one-half contour interval of the correct value. Elevations of water surface printed on the published map meet this standard; the contour intervals of the maps vary. These elevations were transcribed into the digital data; the accuracy of this transcription was checked by visual comparison between the data and the map.

These maps are georeferenced versions of the maps produced by The University Museum, University of Pennsylvania, project at Tikal, Guatemala and published as Tikal Report 11. These georeferenced maps are intended for use with GIS (Geographic Information System) software. The maps should be useful for archaeologists, tourists and managers of Tikal National Park. This map set consists of eleven georeferenced maps. The set includes two versions of the overview map of the central sixteen square kilometers of Tikal—the "Ruins of Tikal" map. One version includes the map border. The other version is without the border. The nine remaining maps cover the inner nine square kilometers in detail, without borders. The maps were georeferenced as part of a University of Cincinnati project in Tikal, under permit of the Guatemalan government. The UC Project georeferenced the maps using land survey methods. We created transformation equations based on a point of beginning, a reference direction and a map scale. Directions and distances on the ground were transformed into UTM projected directions and distances. The point of beginning was the Petty Company benchmark shown on the "Camp Quad" map. In 2010 we determined the location with a GPS receiver. We accessed both the horizontal and vertical accuracy of the georeferenced maps. Based on 96 test points spread throughout the area of the maps, we found the median horizontal accuracy of the maps, compared to GPS, to be 5.6 meters. Based on 103 test points spread throughout the area of the maps, we found the median vertical accuracy of the maps, compared to a NASA radar altimetry mission, to be 2.1 meters. The borders of the maps were removed so the set of maps will “seamlessly” fit together in GIS. See Tikal Report No.11 for versions of the maps with borders (one version of the georeferenced "Ruins of Tikal" map includes the border). The georeferencing files are optimized for use in ArcGIS version 9.2 and beyond. The PDF file of TR11 from which these maps were extracted was made with the generous assistance of the University Museum Library and the Tikal Archives. Details of the georeferencing and accuracy check are in a report to the Dirección Patrimonio Cultural y Natural de Guatemala: Christopher Carr, Eric Weaver, Nicholas Dunning, and Vernon Scarborough (2011) EVALUACIÓN DE LA EXACTITUD DE LOS MAPAS DE TIKAL DE LA UNIVERSIDAD DE PENNSYLVANIA, POR GPS Y ESTACIÓN TOTAL (Accuracy assessment of the Penn Project maps of Tikal, by GPS and Total Station). In Lentz, D., C. Ramos, N. Dunning, V. Scarborough and L. Grazioso. PROYECTO DE SILVICULTURA Y MANEJO DE AGUAS DE LOS ANTIGUOS MAYAS DE TIKAL. Additional details of the strategies the Penn Project used to produce these high quality maps, the georeferencing methodology, and the accuracy check process are forthcoming in a book chapter. The book is on the UC project at Tikal, to be published by Cambridge University Press. The chapter is Carr, Weaver, Dunning and Scarborough. Bringing the University of Pennsylvania maps of Tikal into the era of electronic GIS. In Lentz, Dunning, Scarborough (eds). Tikal and Maya Ecology: Water, Landscapes and Resilience. Permission to publish these maps must be secured from: The University of Pennsylvania Museum of Archaeology and Anthropology, 3260 South Street, Philadelphia, PA 19104, Tel: (215) 898-4050, Fax: (215) 573-9369, Email: publications@pennmuseum.org. .................................................................................................................. Estos mapas son versiones georeferenciados de los mapas producidos por el Museo Universitario de la Universidad de Pennsylvania, Proyecto Tikal, Guatemala y publicado como Informe de Tikal No. 11. La intensión de estos mapas georeferenciados es para ser utilizados con el Sistema de Información Geográfica (SIG). Los mapas deben ser útiles para los arqueólogos, los turistas y los administradores del Parque Nacional Tikal. Este conjunto de mapas consta de once mapas georreferenciados. El juego incluye dos versiones del mapa general de los 16 km2 centrales del mapa de las "Ruins of Tikal". Una versión del mapa incluye sus encuadrados. La otra versión esta sin los encuadrados. Los nueve mapas restantes cubren los mapas interiores de 9 km2 en detalle, sin encuadrados. Los mapas fueron georeferenciados como parte de un proyecto de la Universidad de Cincinnati en Tikal, con permiso del Ministerio de Cultura y Deportes del Gobierno de Guatemala. El Proyecto de la Universidad de Cincinnati georeferenció los mapas utilizando métodos de reconocimiento de campo. Creamos ecuaciones de transformación basado en un punto de inicio, una dirección de referencia y un mapa a escala. Direcciones y distancias en el campo se transformaron en direcciones proyectadas UTM y distancias. El punto de inicio fue el punto de referencia que la empresa Petty muestra en el mapa "Camp Quad". En 2010 se determinó la ubicación de un receptor GPS. Verificamos tanto la precisión horizontal como vertical de los mapas georreferenciados. Sobre la base de 96 puntos de prueba dispersos en el área de los mapas, encontramos la exactitud promedio horizontal de los mapas, en comparación con GPS, a ser 5.6 metros. Sobre la base de 103 puntos de prueba dispersos en el área de los mapas, encontramos la exactitud vertical promedio de los mapas, en comparación con una misión de radar de altimetría de la NASA, que es de 2.1 metros. Los encuadres de los mapas fueron retirados por lo que el conjunto de mapas "sin encuadres" encajan en SIG. Véase el Informe de Tikal No. 11 para las versiones de los mapas con los encuadrados. (una versión de las "Ruins of Tikal" de mapas georeferenciados incluyen el encuadrado). Los archivos de georeferenciación están optimizados para su uso en ArcGIS versión 9.2 u otra superior. El archivo PDF de TR11 con que estos mapas fueron extraídos se realizó con la ayuda generosa de la University Museum Library y el Tikal Archives. Los detalles de la comprobación de georeferenciación y la precisión estarán en un informe a entregar a la Dirección General del Patrimonio Cultural y Natural del Ministerio de Cultura y Deportes de Guatemala: Christopher Carr, Eric Weaver, Dunning Nicholas, y Vernon Scarborough (2011) EVALUACION DE LA EXACTITUD DE LOS MAPAS DE TIKAL DE LA UNIVERSIDAD DE PENSILVANIA, POR GPS Y ESTACIÓN TOTAL (Accuracy assessment of the Penn Project maps of Tikal, by GPS and Total Station). En Lentz, D., C. Ramos, Dunning N., Scarborough V. y Grazioso L., PROYECTO DE SILVICULTURA Y MANEJO DE AGUAS DE LOS ANTIGUOS MAYAS DE TIKAL. Otros detalles de las estrategias del Proyecto de Pennsylvania utilizados para producir estos mapas de alta calidad, la metodología de georeferenciación y el proceso de control de precisión se publicarán en un capítulo de un libro. El libro está en proyecto en la Universidad de Cincinnati en Tikal, que será publicado por Cambridge University Press. El capítulo es: Carr, Weaver, Dunning y Scarborough. Bringing the University of Pennsylvania maps of Tikal into the era of electronic GIS. En Lentz, Dunning, Scarborough (eds). Tikal and Maya Ecology: Water, Landscapes and Resilience. El permiso para publicar estos mapas debe obtenerse de: The University of Pennsylvania Museum of Archaeology and Anthropology, 3260 South Street, Philadelphia, PA 19104, Tel: (215) 898-4050, Fax: (215) 573-9369, Email: publications@pennmuseum.org. Revisado por E. Ponciano Abril de 2013.

In May 2013, the Grand Canyon Monitoring and Research Center (GCMRC) of the U.S. Geological Survey’s (USGS) Southwest Biological Science Center (SBSC) acquired airborne multispectral high resolution data for the Colorado River in Grand Canyon in Arizona, USA. The imagery data consist of four bands (blue, green, red and near infrared) with a ground resolution of 20 centimeters (cm). These data are available to the public as 16-bit geotiff files. They are projected in the State Plane (SP) map projection using the central Arizona zone (202) and the North American Datum of 1983 (NAD83). The assessed accuracy for these data is based on 91 Ground Control Points (GCPs), and is reported at 95% confidence as 0.64 meters (m) and a Root Mean Square Error (RMSE) of 0.36m. The airborne data acquisition was conducted under contract by Fugro Earthdata Inc. using two fixed wing aircraft from May 25th to 30th, 2013 at altitudes between 2440 meters to 3350 meters above mean sea level. The data delivered by Fugro Earthdata Inc. were checked for smear, shadow extent and water clarity as described for previous image acquisitions in Davis (2012). We then produced a corridor-wide mosaic using the best possible tiles with the least amount of smear, the smallest shadow extent, and clearest, most glint-free water possible. During the mosaic process adjacent tiles sometimes had to be spectrally adjusted to account for differences in date, time, sun angle, weather, and environment. We used the same method as described in Davis (2012) for the spectral adjustment. A horizontal accuracy assessment was completed by Fugro Earthdata Inc. using 188 GCPs provided by GCMRC. The GCPs were marked during the image acquisition with 1m2 diagonally alternated black and white plastic panels centered on control points throughout the river corridor in the GCMRC survey control network (Hazel and others, 2008). The Root Mean Square Error (RMSE) accuracy reported by Fugro Earthdata Inc. is 0.17m Easting and 0.15m Northing, or better, depending on the acquisition zone. The 16-bit image data are stored as four band images in embedded geotiff format, which can be read and used by most geographic information system (GIS) and image-processing software. The TIFF world files (tfw) are provided, however they are not needed for many software to read an embedded geotiff image. The image files are projected in the State Plane (SP) 2011, map projection using the central Arizona zone (202) and the North American Datum of 1983 (NAD83). A complete detailed description of the methods can be found in the associated USGS Data Series 1027 for these data, https://pubs.er.usgs.gov/publication/ds1027.

The subspecies of American badger (Taxidea taxus berlandieri Baird, 1858), also called tlalcoyote (Figure 1), is distributed in north-central Mexico. However, its occurrence records are scarce and the few that exist are uncertain due to incorrect georeferencing or identification of the taxonomic unit. In view of this, we disgned a spatial sampling in part of the states of Coahuila de Zaragoza, Durango, Nuevo León, San Luis Potosí and Zacatecas. In this north-central protion of Mexico, we generated a grid of squares measuring 5 × 5 km over the entire study area using QGIS® 3.10 software. Subsequently, we excluded squares that included urban settlements, agricultural land, or water bodies in more than 30% of their extension; we also descarted squares located at an altitude over 2,250 meters above sea level. To perform this filtering, we used both the land use and vegetation chart of the INEGI [Instituto Nacional de Estadística, Geografía e Informática] (2018) and the Digital Elevation Model (DEM) downloaded from the USGS page [United States Geological Survey] (2019) as a basis. As result, we obtained 3,471 squares separated by at least 5 km. Then, through simple random sampling, 177 (≈5%) squares were selected, where we generated centroids to be used as sampling sites.

In field work, between 2009 and 2015, at these 177 sites we traced a 10 × 100 m transect, where we searched for T. t. berlandieri signs (i.e., burrows and scratching posts). In this case, their burrows and scratching posts are easily observed and quantified, and there is no chance of mistaking them for burrows of other species (Long 1973; Merlin 1999). Also, we recorded possible sightings, as other studies (e.g., Merlin 1999; Elbroch 2003). As result, we only found 33 with signs of occurrence.

Figure 1. Individual of tlalcoyote (Taxidea taxus Berlandieri). Photo obtained from Naturalista (2023) and uploaded by David Molina©. All rights reserved (CC BY-NC-ND).

To increase the number of records, we included occurrence data from GBIF [Global Biodiversity Information Facility portal] (2022). We downloaded only the records that included coordinates and that their basis of registration was "preserved specimen". This, because they are correctly identified as specimens from biological collections (Maldonado et al. 2015). In addition, we only selected records for Mexico. Subsequently, we filtered the downloaded database, discarding records that were incorrectly georeferenced, with atypical and duplicate coordinates, as well as with low geospatial accuracy (e.g., less than three decimals of precision).

We loaded the remaining data into the QGIS® software and performed a spatial filtering, where we excluded data that were outside the study area, located in unlikely areas (e.g., human settlements, bodies of water, agricultural areas) and with a distance of less than 5 km from the records obtained in the field. This gave a total of 10 records from the GBIF portal. Finally, we loaded the raster layers of elevation (Elev; INEGI 2007), normalized difference vegetation index (NDVI, USGS 2019) and the slope of the terrain into the software to extract the pixel values based on the GBIF records and those obtained in the field. With this, we generated a new global dataset to which we performed environmental filtering to find environmental outliers. We plotted the normality distribution of the data for each variable and the dispersion of the data among the variables. In this filtering, we conserve all records. Figure 2 shows the normality distribution of the records as a function of Elev. Figure 3 shows the dispersion of the data between Elev and NDVI.

Figure 2. Normality distribution of T. t. berlandieri occurrence records as a function of the elevation variable (Elev).

Figure 3. Scatter plot of T. t. berlandieri occurrence records as a function of elevation (Elev) and normalized difference vegetation index (NDVI).

For the north-central region of Mexico, we present the global database (i.e., Tatabe_joint.csv), as well as the database that contains only the field evidence records (i.e., Tatabe_first_order.csv) and another one with the filtered GBIF records (i.e., Tatabe_GBIF.csv).

Grape phylloxera (Daktulosphaira vitifoliae, syn. Viteus vitifoliae), a destructive root and foliar pest of grapevines, occurs in almost all viticulture regions worldwide. However, certain regions have remained “phylloxera free.” Until recently, this included Washington state (United States), where this insect is regulated as a quarantine pest by Washington State Department of Agriculture. In 2019, established phylloxera populations were discovered in Washington. Phylloxera is typically managed by using resistant or tolerant rootstocks. In Washington, most wine grapes are grown on their own roots of the susceptible species Vitis vinifera instead of grafted rootstock, and thus, are at high risk of vine death should they become infested with phylloxera. This article reports development of a phylloxera risk map for Washington state using geographical soil texture (sand content) and soil temperature data. Weighted averages of soil texture data (mapping year: 2016, depth: 0–100 cm) were obtained from United States Department of Agriculture-Natural Resource Conservation Service (USDA-NRCS) and soilgrids. Soil temperature data were obtained from over 200 weather stations of Washington State University’s AgWeatherNet network. Threshold-based classifications were performed in Quantum GIS software on the rasterized soil sand content and temperature independently to derive low, moderate, and high-risk areas, with risk defined as site suitability for optimal phylloxera development. The validation identified 22 out of 23 confirmed phylloxera-positive sites as “high risk,” and one site as “moderate risk” when considering soil sand content alone. Soil temperature data alone classified 10 sites as “high risk” and 13 sites as “low risk.” When soil sand content was combined with soil temperature (as a risk modifier), 10 sites were classified as “high risk,” 12 sites as “high-moderate risk” and one site as “moderate-low” risk. Ground-truth comparisons of confirmed positive sites for phylloxera agreed with past research suggesting that soil sand content is the dominant factor influencing phylloxera infestation. Pertinent risk assessment can be an important component for vineyard decision-making, including whether to use rootstocks in vineyard development or replant scenarios. It may also help to focus the initial scouting and identification efforts to sites and may be helpful when tracking and developing solutions for quarantine pests, such as phylloxera.

Statistical analyses and maps representing mean, high, and low water-level conditions in the surface water and groundwater of Miami-Dade County were made by the U.S. Geological Survey, in cooperation with the Miami-Dade County Department of Regulatory and Economic Resources, to help inform decisions necessary for urban planning and development. Sixteen maps were created that show contours of (1) the mean of daily water levels at each site during October and May for the 2000-2009 water years; (2) the 25th, 50th, and 75th percentiles of the daily water levels at each site during October and May and for all months during 2000-2009; and (3) the differences between mean October and May water levels, as well as the differences in the percentiles of water levels for all months, between 1990-1999 and 2000-2009. The 80th, 90th, and 96th percentiles of the annual maximums of daily groundwater levels during 1974-2009 (a 35-year period) were computed to provide an indication of unusually high groundwater-level conditions. These maps and statistics provide a generalized understanding of the variations of water levels in the aquifer, rather than a survey of concurrent water levels. Water-level measurements from 473 sites in Miami-Dade County and surrounding counties were analyzed to generate statistical analyses. The monitored water levels included surface-water levels in canals and wetland areas and groundwater levels in the Biscayne aquifer. Maps were created by importing site coordinates, summary water-level statistics, and completeness of record statistics into a geographic information system, and by interpolating between water levels at monitoring sites in the canals and water levels along the coastline. Raster surfaces were created from these data by using the triangular irregular network interpolation method. The raster surfaces were contoured by using geographic information system software. These contours were imprecise in some areas because the software could not fully evaluate the hydrology given available information; therefore, contours were manually modified where necessary. The ability to evaluate differences in water levels between 1990-1999 and 2000-2009 is limited in some areas because most of the monitoring sites did not have 80 percent complete records for one or both of these periods. The quality of the analyses was limited by (1) deficiencies in spatial coverage; (2) the combination of pre- and post-construction water levels in areas where canals, levees, retention basins, detention basins, or water-control structures were installed or removed; (3) an inability to address the potential effects of the vertical hydraulic head gradient on water levels in wells of different depths; and (4) an inability to correct for the differences between daily water-level statistics. Contours are dashed in areas where the locations of contours have been approximated because of the uncertainty caused by these limitations. Although the ability of the maps to depict differences in water levels between 1990-1999 and 2000-2009 was limited by missing data, results indicate that near the coast water levels were generally higher in May during 2000-2009 than during 1990-1999; and that inland water levels were generally lower during 2000-2009 than during 1990-1999. Generally, the 25th, 50th, and 75th percentiles of water levels from all months were also higher near the coast and lower inland during 2000–2009 than during 1990-1999. Mean October water levels during 2000-2009 were generally higher than during 1990-1999 in much of western Miami-Dade County, but were lower in a large part of eastern Miami-Dade County.