Introduction

Geographical scale, in terms of spatial extent, provide a basis for other branches of science. This dataset contains newly proposed geographical and geological GIS boundaries for the Pan-Tibetan Highlands (new proposed name for the High Mountain Asia), based on geological and geomorphological features. This region comprises the Tibetan Plateau and three adjacent mountain regions: the Himalaya, Hengduan Mountains and Mountains of Central Asia, and boundaries are also given for each subregion individually. The dataset will benefit quantitative spatial analysis by providing a well-defined geographical scale for other branches of research, aiding cross-disciplinary comparisons and synthesis, as well as reproducibility of research results.

The dataset comprises three subsets, and we provide three data formats (.shp, .geojson and .kmz) for each of them. Shapefile format (.shp) was generated in ArcGIS Pro, and the other two were converted from shapefile, the conversion steps refer to 'Data processing' section below. The following is a description of the three subsets:

(1) The GIS boundaries we newly defined of the Pan-Tibetan Highlands and its four constituent sub-regions, i.e. the Tibetan Plateau, Himalaya, Hengduan Mountains and the Mountains of Central Asia. All files are placed in the "Pan-Tibetan Highlands (Liu et al._2022)" folder.

(2) We also provide GIS boundaries that were applied by other studies (cited in Fig. 3 of our work) in the folder "Tibetan Plateau and adjacent mountains (Others’ definitions)". If these data is used, please cite the relevent paper accrodingly. In addition, it is worthy to note that the GIS boundaries of Hengduan Mountains (Li et al. 1987a) and Mountains of Central Asia (Foggin et al. 2021) were newly generated in our study using Georeferencing toolbox in ArcGIS Pro.

(3) Geological assemblages and characters of the Pan-Tibetan Highlands, including Cratons and micro-continental blocks (Fig. S1), plus sutures, faults and thrusts (Fig. 4), are placed in the "Pan-Tibetan Highlands (geological files)" folder.

Note: High Mountain Asia: The name ‘High Mountain Asia’ is the only direct synonym of Pan-Tibetan Highlands, but this term is both grammatically awkward and somewhat misleading, and hence the term ‘Pan-Tibetan Highlands’ is here proposed to replace it. Third Pole: The first use of the term ‘Third Pole’ was in reference to the Himalaya by Kurz & Montandon (1933), but the usage was subsequently broadened to the Tibetan Plateau or the whole of the Pan-Tibetan Highlands. The mainstream scientific literature refer the ‘Third Pole’ to the region encompassing the Tibetan Plateau, Himalaya, Hengduan Mountains, Karakoram, Hindu Kush and Pamir. This definition was surpported by geological strcture (Main Pamir Thrust) in the western part, and generally overlaps with the ‘Tibetan Plateau’ sensu lato defined by some previous studies, but is more specific.

More discussion and reference about names please refer to the paper. The figures (Figs. 3, 4, S1) mentioned above were attached in the end of this document.

Data processing

We provide three data formats. Conversion of shapefile data to kmz format was done in ArcGIS Pro. We used the Layer to KML tool in Conversion Toolbox to convert the shapefile to kmz format. Conversion of shapefile data to geojson format was done in R. We read the data using the shapefile function of the raster package, and wrote it as a geojson file using the geojson_write function in the geojsonio package.

Version

Version 2022.1.

Acknowledgements

This study was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDB31010000), the National Natural Science Foundation of China (41971071), the Key Research Program of Frontier Sciences, CAS (ZDBS-LY-7001). We are grateful to our coauthors insightful discussion and comments. We also want to thank professors Jed Kaplan, Yin An, Dai Erfu, Zhang Guoqing, Peter Cawood, Tobias Bolch and Marc Foggin for suggestions and providing GIS files.

Citation

Liu, J., Milne, R. I., Zhu, G. F., Spicer, R. A., Wambulwa, M. C., Wu, Z. Y., Li, D. Z. (2022). Name and scale matters: Clarifying the geography of Tibetan Plateau and adjacent mountain regions. Global and Planetary Change, In revision

Jie Liu & Guangfu Zhu. (2022). Geographical and geological GIS boundaries of the Tibetan Plateau and adjacent mountain regions (Version 2022.1). https://doi.org/10.5281/zenodo.6432940

Contacts

Dr. Jie LIU: E-mail: liujie@mail.kib.ac.cn;

Mr. Guangfu ZHU: zhuguangfu@mail.kib.ac.cn

Institution: Kunming Institute of Botany, Chinese Academy of Sciences

Address: 132# Lanhei Road, Heilongtan, Kunming 650201, Yunnan, China

Copyright

This dataset is available under the Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).

description: This tool provides a no-cost downloadable software tool that allows users to interact with professional quality GIS maps. Users access pre-compiled projects through a free software product called ArcReader, and are able to open and explore HUD-specific project data as well as design and print custom maps. No special software/map skills beyond basic computer skills are required, meaning users can quickly get started working with maps of their communities.; abstract: This tool provides a no-cost downloadable software tool that allows users to interact with professional quality GIS maps. Users access pre-compiled projects through a free software product called ArcReader, and are able to open and explore HUD-specific project data as well as design and print custom maps. No special software/map skills beyond basic computer skills are required, meaning users can quickly get started working with maps of their communities.

Empty geodatabase schema for GIS as-built submissions of new gathering pipeline or natural gas gathering system as defined in 19.15.28.9 NMAC.“Natural gas gathering system” means the gathering pipelines and associated facilities that compress, dehydrate, or treat natural gas after the custody transfer point and ending at the connection point with a natural gas processing plant or transmission or distribution system. 19.15.28.7 NMAC.“Gathering pipeline” means a pipeline that gathers natural gas within a natural gas gathering system. 19.15.28.7 NMAC.“Release” No later than July 1st of each year, the operator shall also file with the division an updated system map GIS digitally formatted as-built map of its gathering pipeline or natural gas gathering system, which shall include a GIS layer that identifies the date, location and volume of vented or flared natural gas of each emergency, malfunction and release reported to the division since 19.15.28 NMAC became applicable to the pipeline or system. System Maps will be submitted to OCD in the Esri file geodatabase format.Do not submit Esri shapefile, personal geodatabase, or other raw formats. Do not submit GIS files that were converted to a file geodatabase format without following the required database template.File Geodatabase and feature layers must use an underscore, rather than a period or space, when naming files. (ex. FacID_Date_NGGS)

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

The Digital Geologic-GIS Map of Everglades National Park and Vicinity, Florida is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (ever_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (ever_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (ever_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (ever_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (ever_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (ever_geology_metadata_faq.pdf). Please read the ever_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Florida Geological Survey and U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (ever_geology_metadata.txt or ever_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:675,000 and United States National Map Accuracy Standards features are within (horizontally) 342.9 meters or 1125 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

The historical GIS layers for the Tokugawa Period (circa 1664 and 1820) were developed for presentation at CEAL, Japanese Librarians Meeting, 2004. This paper will briefly outline existing examples of Japan Historical GIS, the methodology used to develop our demonstration GIS, and the means of searching the data online.

description: The Geopspatial Fabric provides a consistent, documented, and topologically connected set of spatial features that create an abstracted stream/basin network of features useful for hydrologic modeling.The GIS vector features contained in this Geospatial Fabric (GF) data set cover the lower 48 U.S. states, Hawaii, and Puerto Rico. Four GIS feature classes are provided for each Region: 1) the Region outline ("one"), 2) Points of Interest ("POIs"), 3) a routing network ("nsegment"), and 4) Hydrologic Response Units ("nhru"). A graphic showing the boundaries for all Regions is provided at http://dx.doi.org/doi:10.5066/F7542KMD. These Regions are identical to those used to organize the NHDPlus v.1 dataset (US EPA and US Geological Survey, 2005). Although the GF Feature data set has been derived from NHDPlus v.1, it is an entirely new data set that has been designed to generically support regional and national scale applications of hydrologic models. Definition of each type of feature class and its derivation is provided within the

This dataset consists of summer temperature metrics for Boston, MA. These heat metrics summarize six CAPA Urban Heat Watch program temperature and heat index datasets using geographical boundaries from the Census Tract (CT) layer. Heat datasets were created by Museum of Science, Boston, and the Helmuth Lab at Northeastern University. Heat metrics are presented in the attribute table as mean values of each Heat Watch program dataset for all hexagon features. The six heat values included in this table are July 2019 temperature and heat index in degrees Fahrenheit for each of 3 1-hour periods -- 6 a.m., 3 p.m., and 7 p.m. EDT. The geographic boundaries used to summarize the heat metrics are current as of 2019.

All data associated with the Town of Young Floodplain Risk Management Study and Plan. GIS Data Outputs, Hydraulics, Hydrology, Reporting, Survey. Data and Resources Data associated with Town of Young Floodplain Risk Management Study and PlanZIP (11.5 GB) All Data and GIS data associated with the Town of Young Floodplain Risk Management Study and Plan. Explore More information Download More info Creative Commons Attribution 4.0 International Public License By exercising the Licensed Rights (defined below), You accept and agree to be bound by the terms and conditions of this Creative Commons Attribution 4.0 International Public License (“Public License”). To the extent this Public License may be interpreted as a contract, You are granted the Licensed Rights in consideration of Your acceptance of these terms and conditions, and the Licensor grants You such rights in consideration of benefits the Licensor receives from making the Licensed Material available under these terms and conditions. Section 1 – Definitions. Adapted Material means material subject to Copyright and Similar Rights that is derived from or based upon the Licensed Material and in which the Licensed Material is translated, altered, arranged, transformed, or otherwise modified in a manner requiring permission under the Copyright and Similar Rights held by the Licensor. For purposes of this Public License, where the Licensed Material is a musical work, performance, or sound recording, Adapted Material is always produced where the Licensed Material is synched in timed relation with a moving image. 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Exceptions and Limitations means fair use, fair dealing, and/or any other exception or limitation to Copyright and Similar Rights that applies to Your use of the Licensed Material. Licensed Material means the artistic or literary work, database, or other material to which the Licensor applied this Public License. Licensed Rights means the rights granted to You subject to the terms and conditions of this Public License, which are limited to all Copyright and Similar Rights that apply to Your use of the Licensed Material and that the Licensor has authority to license. Licensor means the individual(s) or entity(ies) granting rights under this Public License. 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Subject to the terms and conditions of this Public License, the Licensor hereby grants You a worldwide, royalty-free, non-sublicensable, non-exclusive, irrevocable license to exercise the Licensed Rights in the Licensed Material to: reproduce and Share the Licensed Material, in whole or in part; and produce, reproduce, and Share Adapted Material. Exceptions and Limitations. For the avoidance of doubt, where Exceptions and Limitations apply to Your use, this Public License does not apply, and You do not need to comply with its terms and conditions. Term. The term of this Public License is specified in Section 6(a). Media and formats; technical modifications allowed. The Licensor authorizes You to exercise the Licensed Rights in all media and formats whether now known or hereafter created, and to make technical modifications necessary to do so. 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Where the Licensed Rights include Sui Generis Database Rights that apply to Your use of the Licensed Material: for the avoidance of doubt, Section 2(a)(1) grants You the right to extract, reuse, reproduce, and Share all or a substantial portion of the contents of the database; if You include all or a substantial portion of the database contents in a database in which You have Sui Generis Database Rights, then the database in which You have Sui Generis Database Rights (but not its individual contents) is Adapted Material; and You must comply with the conditions in Section 3(a) if You Share all or a substantial portion of the contents of the database. For the avoidance of doubt, this Section 4 supplements and does not replace Your obligations under this Public License where the Licensed Rights include other Copyright and Similar Rights. Section 5 – Disclaimer of Warranties and Limitation of Liability. Unless otherwise separately undertaken by the Licensor, to the extent possible, the Licensor offers the Licensed Material as-is and as-available, and makes no representations or warranties of any kind concerning the Licensed Material, whether express, implied, statutory, or other. This includes, without limitation, warranties of title, merchantability, fitness for a particular purpose, non-infringement, absence of latent or other defects, accuracy, or the presence or absence of errors, whether or not known or discoverable. Where disclaimers of warranties are not allowed in full or in part, this

WorldClim 2.1 provides downscaled estimates of climate variables as monthly means over the period of 1970-2000 based on interpolated station measurements. Here we provide analytical image services of precipitation for each month along with an annual mean. Each time step is accessible from a processing template.Time Extent: Monthly/Annual 1970-2000Units: mm/monthCell Size: 2.5 minutes (~5 km)Source Type: StretchedPixel Type: 16 Bit IntegerData Projection: GCS WGS84Mosaic Projection: GCS WGS84Extent: GlobalSource: WorldClim v2.1Using Processing Templates to Access TimeThere are 13 processing templates applied to this service, each providing access to the 12 monthly and 1 annual mean precipitation layers. To apply these in ArcGIS Online, select the Image Display options on the layer. Then pull down the list of variables from the Renderer options. Click Apply and Close. In ArcGIS Pro, go into the Layer Properties. Select Processing Templates from the left-hand menu. From the Processing Template pull down menu, select the version to display.What can you do with this layer?This layer may be added to maps to visualize and quickly interrogate each pixel value. The pop-up provides a graph of the time series along with the calculated annual mean value.This layer can be used in analysis. For example, the layer may be added to ArcGIS Pro and an area count of precipitation may be produced for a feature dataset using the zonal statistics tool. Statistics may be compared with the statistics from month to month to show seasonal patterns.To calculate precipitation by land area, or any other analysis, be sure to use an equal area projection, such as Albers or Equal Earth.Source Data: The datasets behind this layer were extracted from GeoTIF files produced by WorldClim at 2.5 minutes resolution. The mean of the 12 GeoTIFs was calculated (annual), and the 13 rasters were converted to Cloud Optimized GeoTIFF format and added to a mosaic dataset.Citation: Fick, S.E. and R.J. Hijmans, 2017. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37 (12): 4302-4315.

The Historic High Water Shorelines and Shoreline Rates of Change Transects are provided by MassCZM's 'Shoreline Change Project'. Please read their info carefully to fully understand how to interpret this data and know the accuracy associated with the long-term rate of change and the short-term rate of change.The USGS delineated the Mean High Water shoreline based on data collected in 2012 and 2013. This data was just released in 2018. Several USGS reports describe the various methodologies used to create this data.Profile MethodProfile Method 2013Contour MethodSince the USGS shoreline data for Martha's Vineyard and the Elizabeth Islands was not presented as one continuous shoreline (given varying processing techniques) the MVC merged the 3 datasets together for ease of cartographic display. The attribute table indicates the original source file.

This feature service contains data from the American Community Survey: 5-year Estimates Subject Tables for City of Bozeman, MT. The attributes come from the Means of Transportation to Work by Selected Characteristics table (S0802). Processing Notes:Data was downloaded from the U.S. Census Bureau and imported into FME to create an AGOL Feature Service. Each attribute has been given an abbreviated alias name derived from the American Community Survey (ACS) categorical descriptions. The Data Dictionary below includes all given ACS attribute name aliases. For example: PublicTransit_PovBelow100pct is the percentage of the population that is below 100% of the poverty level and uses public transportation to get to work. Data DictionaryACS_EST_YR: American Community Survey 5-Year Estimate Subject Tables data yearGEO_ID: Census Bureau geographic identifierNAME: Specified geographyDroveAlone: Means of Transportation to work is by driving aloneCarpool: Means of Transportation to work is by carpoolingPublicTransit: Means of Transportation to work is by using Public TransportationRace/Ethinicity:A: AsianAIAN: American Indian or Alaska NativeBAA: Black or African AmericanHL: Hispanic or LatinoNHPI: Native Hawaiian or other Pacific IslanderW: WhiteOther: Some other raceTwo: Two or more racesPoverty Status:Below100pct: Below 100% of the poverty level100to149pct: 100-149% of the poverty level150pct: at or above 150% of the poverty levelDownload ACS Means of Transportation to Work by Selected Characteristics data for Bozeman, MTAdditional LinksU.S. Census BureauU.S. Census Bureau American Community Survey (ACS)About the American Community Survey

Parcel boundaries exported nightly from the Assessor's Office managed parcel fabric and joined with attributes related to owner information, values and size, the water source (Public Health database) and a link to the SmartGov public portal (permitting database). Most features are within 3 feet however some features can be up to 20 feet off. Please read the full data disclaimer when using this dataset.

Prof. Robert Hartwell (1932 - 1996) created his China Historical GIS under the auspices of his company Chinese Historical Studies. His estate left the data to Harvard University. These materials include functional GIS datasets for the Chinese Dynasties, from Tang to Ming, which were based on the concept of "co-location," or the use of GIS representations of modern county-level administrative units as building blocks to depict the approximate shapes of historical areas. Making use of county boundary data for 1992, (obtained from Crissman's ACASIAN data), Hartwell represented historical units that occupied roughly the same areas by merging or splitting the 1992 counties. Where the contemporary boundaries could not be "co-located" in this fashion, Hartwell drew in approximate line boundaries to divide the contemporary units to fit the historical situations and therefore provide an approximation of the historical unit's area. Although the resulting boundaries are, in many cases, problematic representations, the GIS remains an interesting hueristic GIS tool for sorting, querying, and creating digital maps for selected areas within the major dynasties up to the Ming. Harvard University released the original Hartwell datasets on April 2nd, 2001, in conjunction with the CHGIS project, as a useful means of generating approximate spatial entities correlating to historical administrative units. For Version 5, the Hartwell Datasets were renamed according to a filenaming convention (described above) and projected to match the CHGIS V5 standard (2014).

According to many previous studies, application of remote sensing for the complex and heterogeneous urban environments in Sub-Saharan African countries is challenging due to the spectral confusion among features caused by diversity of construction materials. Resorting to classification based on spectral indices that are expected to better highlight features of interest and to be prone to unsupervised classification, this study aims (1) to evaluate the effectiveness of index-based classification for Land Use Land Cover (LULC) using an unsupervised machine learning algorithm Product Quantized K-means (PQk-means); and (2) to monitor the urban expansion of Luanda, the capital city of Angola in a Logistic Regression Model (LRM). Comparison with state-of-the-art algorithms shows that unsupervised classification by means of spectral indices is effective for the study area and can be used for further studies. The built-up area of Luanda has increased from 94.5 km2 in 2000 to 198.3 km2 in 2008 and to 468.4 km2 in 2018, mainly driven by the proximity to the already established residential areas and to the main roads as confirmed by the logistic regression analysis. The generated probability maps show high probability of urban growth in the areas where government had defined housing programs.

Some of the highest grade uranium (U) deposits in the United States are hosted by solution-collapse breccia pipes in the Grand Canyon region of northern Arizona. These structures are named for their vertical, pipe-like shape and the broken rock (breccia) that fills them. Hundreds, perhaps thousands, of these structures exist. Not all of the breccia pipes are mineralized; only a small percentage of the identified breccia pipes are known to contain an economic uranium deposit. An unresolved question is how many undiscovered U-bearing breccia pipes of this type exist in northern Arizona, in the region sometimes referred to as the “Arizona Strip”. Two principal questions remain regarding the breccia pipe U deposits of northern Arizona are: (1) What processes combined to form these unusual structures and their U deposits? and (2) How many undiscovered U deposits hosted by breccia pipes exist in the region? A piece of information required to answer these questions is to define the area where these types of deposits could exist based on available geologic information. In order to determine the regional processes that led to their formation, the regional distribution of U-bearing breccia pipes must be considered. These geospatial datasets were assembled in support of this goal.

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.

California State Lands Commission Offshore Oil Leases in the vicinity of Santa Barbara, Ventura, and Orange County.The polygons in this layer show the position of Offshore Oil Leases as documented by former State Lands Senior Boundary Determination Officer, Cris N. Perez and as reviewed and updated by GIS and Boundary staff.Background: This layer represents active offshore oil and gas agreements in California waters, which are what remain of the more than 60 originally issued. These leases were issued prior to the catastrophic 1969 oil spill from Platform A in federal waters off Santa Barbara County, and some predate the formation of the Commission. Between 2010 and 2014, the bulk of the approximately $300 million generated annually for the state's General Fund from oil and gas agreements was from these offshore leases.In 1921, the Legislature created the first tidelands oil and gas leasing program. Between 1921 and 1929, approximately 100 permits and leases were issued and over 850 wells were drilled in Santa Barbara and Ventura Counties. In 1929, the Legislature prohibited any new leases or permits. In 1933, however, the prohibition was partially lifted in response to an alleged theft of tidelands oil in Huntington Beach. It wasn't until 1938, and again in 1955, that the Legislature would allow new offshore oil and gas leasing. Except for limited circumstances, the Legislature has consistently placed limits on the areas that the Commission may offer for lease and in 1994, placed the entirety of California's coast off-limits to new oil and gas leases. Layer Creation Process:In 1997 Cris N. Perez, Senior Boundary Determination Officer of the Southern California Section of the State Lands Division, prepared a report on the Commission’s Offshore Oil Leases to:A. Show the position of Offshore Oil Leases. B. Produce a hard copy of 1927 NAD Coordinates for each lease. C. Discuss any problems evident after plotting the leases.Below are some of the details Cris included in the report:I have plotted the leases that were supplied to me by the Long Beach Office and computed 1927 NAD California Coordinates for each one. Where the Mean High Tide Line (MHTL) was called for and not described in the deed, I have plotted the California State Lands Commission CB Map Coordinates, from the actual field surveys of the Mean High Water Line and referenced them wherever used. Where the MHTL was called for and not described in the deed and no California State Lands Coordinates were available, I digitized the maps entitled, “Map of the Offshore Ownership Boundary of the State of California Drawn pursuant to the Supplemental Decree of the U.S. Supreme Court in the U.S. V. California, 382 U.S. 448 (1966), Scale 1:10000 Sheets 1-161.” The shore line depicted on these maps is the Mean Lower Low Water (MLLW) Line as shown on the Hydrographic or Topographic Sheets for the coastline. If a better fit is needed, a field survey to position this line will need to be done.The coordinates listed in Cris’ report were retrieved through Optical Character Recognition (OCR) and used to produce GIS polygons using Esri ArcGIS software. Coordinates were checked after the OCR process when producing the polygons in ArcMap to ensure accuracy. Original Coordinate systems (NAD 1927 California State Plane Zones 5 and 6) were used initially, with each zone being reprojected to NAD 83 Teale Albers Meters and merged after the review process.While Cris’ expertise and documentation were relied upon to produce this GIS Layer, certain polygons were reviewed further for any potential updates since Cris’ document and for any unusual geometry. Boundary Determination Officers addressed these issues and plotted leases currently listed as active, but not originally in Cris’ report. On December 24, 2014, the SLA boundary offshore of California was fixed (permanently immobilized) by a decree issued by the U.S. Supreme Court United States v. California, 135 S. Ct. 563 (2014). Offshore leases were clipped so as not to exceed the limits of this fixed boundary. Lease Notes:PRC 1482The “lease area” for this lease is based on the Compensatory Royalty Agreement dated 1-21-1955 as found on the CSLC Insider. The document spells out the distinction between “leased lands” and “state lands”. The leased lands are between two private companies and the agreement only makes a claim to the State’s interest as those lands as identified and surveyed per the map Tract 893, Bk 27 Pg 24. The map shows the State’s interest as being confined to the meanders of three sloughs, one of which is severed from the bay (Anaheim) by a Tideland sale. It should be noted that the actual sovereign tide and or submerged lands for this area is all those historic tide and submerged lands minus and valid tide land sales patents. The three parcels identified were also compared to what the Orange County GIS land records system has for their parcels. Shapefiles were downloaded from that site as well as two centerline monuments for 2 roads covered by the Tract 893. It corresponded well, so their GIS linework was held and clipped or extended to make a parcel.MJF Boundary Determination Officer 12/19/16PRC 3455The “lease area” for this lease is based on the Tract No. 2 Agreement, Long Beach Unit, Wilmington Oil Field, CA dated 4/01/1965 and found on the CSLC insider (also recorded March 12, 1965 in Book M 1799, Page 801).Unit Operating Agreement, Long Beach Unit recorded March 12, 1965 in Book M 1799 page 599.“City’s Portion of the Offshore Area” shall mean the undeveloped portion of the Long Beach tidelands as defined in Section 1(f) of Chapter 138, and includes Tract No. 1”“State’s Portion of the Offshore Area” shall mean that portion of the Alamitos Beach Park Lands, as defined in Chapter 138, included within the Unit Area and includes Tract No. 2.”“Alamitos Beach Park Lands” means those tidelands and submerged lands, whether filled or unfilled, described in that certain Judgment After Remittitur in The People of the State of California v. City of Long Beach, Case No. 683824 in the Superior Court of the State of California for the County of Los Angeles, dated May 8, 1962, and entered on May 15, 1962 in Judgment Book 4481, at Page 76, of the Official Records of the above entitled court”*The description for Tract 2 has an EXCEPTING (statement) “therefrom that portion lying Southerly of the Southerly line of the Boundary of Subsidence Area, as shown on Long Beach Harbor Department {LBHD} Drawing No. D-98. This map could not be found in records nor via a PRA request to the LBHD directly. Some maps were located that show the extents of subsidence in this area being approximately 700 feet waterward of the MHTL as determined by SCC 683824. Although the “EXCEPTING” statement appears to exclude most of what would seem like the offshore area (out to 3 nautical miles from the MHTL which is different than the actual CA offshore boundary measured from MLLW) the 1964, ch 138 grant (pg25) seems to reference the lands lying seaward of that MHTL and ”westerly of the easterly boundary of the undeveloped portion of the Long Beach tidelands, the latter of which is the same boundary (NW) of tract 2. This appears to then indicate that the “EXCEPTING” area is not part of the Lands Granted to City of Long Beach and appears to indicate that this portion might be then the “State’s Portion of the Offshore Area” as referenced in the Grant and the Unit Operating Agreement. Section “f” in the CSLC insider document (pg 9) defines the Contract Lands: means Tract No. 2 as described in Exhibit “A” to the Unit Agreement, and as shown on Exhibit “B” to the Unit Agreement, together with all other lands within the State’s Portion of the Offshore Area.Linework has been plotted in accordance with the methods used to produce this layer, with record lines rotated to those as listed in the descriptions. The main boundaries being the MHTL(north/northeast) that appears to be fixed for most of the area (projected to the city boundary on the east/southeast); 3 nautical miles from said MHTL on the south/southwest; and the prolongation of the NWly line of Block 50 of Alamitos Bay Tract.MJF Boundary Determination Officer 12-27-16PRC 4736The “lease area” for this lease is based on the Oil and Gas Lease and Agreement as found on the CSLC insider and recorded August 17, 1973 in BK 10855 PG 432 Official Records, Orange County. The State’s Mineral Interests are confined to Parcels “B-1” and “B-2” and are referred to as “State Mineral Lands” comprising 70.00 Acres. The lessee each has a right to certain uses including but not limited to usage of utility corridors, 110 foot radius parcels surrounding well-sites and roads. The State also has access to those same roads per this agreement/lease. Those uses are allowed in what are termed “State Lands”-Parcel E and “Leased Lands” which are defined as the “South Bolsa Lease Area”-Parcel C (2 parcels) and “North Bolsa Lease Area”-Parcel D. The “State Lands”-Parcel E are actually 3 parcels, 2 of which are within road right-of-ways. MJF Boundary Determination Officer 12-28-16

The GIS-based Time model of Gothenburg aims to map the process of urban development in Gothenburg since 1960 and in particular to document the changes in the spatial form of the city - streets, buildings and plots - through time. Major steps have in recent decades been taken when it comes to understanding how cities work. Essential is the change from understanding cities as locations to understanding them as flows (Batty 2013)1. In principle this means that we need to understand locations (or places) as defined by flows (or different forms of traffic), rather than locations only served by flows. This implies that we need to understand the built form and spatial structure of cities as a system, that by shaping flows creates a series of places with very specific relations to all other places in the city, which also give them very specific performative potentials. It also implies the rather fascinating notion that what happens in one place is dependent on its relation to all other places (Hillier 1996)2. Hence, to understand the individual place, we need a model of the city as a whole.

Extensive research in this direction has taken place in recent years, that has also spilled over to urban design practice, not least in Sweden, where the idea that to understand the part you need to understand the whole is starting to be established. With the GIS-based Time model for Gothenburg that we present here, we address the next challenge. Place is not only something defined by its spatial relation to all other places in its system, but also by its history, or its evolution over time. Since the built form of the city changes over time, often by cities growing but at times also by cities shrinking, the spatial relation between places changes over time. If cities tend to grow, and most often by extending their periphery, it means that most places get a more central location over time. If this is a general tendency, it does not mean that all places increase their centrality to an equal degree. Depending on the structure of the individual city’s spatial form, different places become more centrally located to different degrees as well as their relative distance to other places changes to different degrees. The even more fascinating notion then becomes apparent; places move over time! To capture, study and understand this, we need a "time model".

The GIS-based time model of Gothenburg consists of: • 12 GIS-layers of the street network, from 1960 to 2015, in 5-year intervals • 12 GIS-layers of the buildings from 1960 to 2015, in 5-year intervals - Please note that this dataset has been moved to a separate catalog post (https://doi.org/10.5878/t8s9-6y15) and unpublished due to licensing restrictions on its source dataset. • 12 GIS- layers of the plots from1960 to 2015, in 5-year intervals

In the GIS-based Time model, for every time-frame, the combination of the three fundamental components of spatial form, that is streets, plots and buildings, provides a consistent description of the built environment at that particular time. The evolution of three components can be studied individually, where one could for example analyze the changing patterns of street centrality over time by focusing on the street network; or, the densification processes by focusing on the buildings; or, the expansion of the city by way of occupying more buildable land, by focusing on plots. The combined snapshots of street centrality, density and land division can provide insightful observations about the spatial form of the city at each time-frame; for example, the patterns of spatial segregation, the distribution of urban density or the patterns of sprawl. The observation of how the interrelated layers of spatial form together evolved and transformed through time can provide a more complete image of the patterns of urban growth in the city.

The Time model was created following the principles of the model of spatial form of the city, as developed by the Spatial Morphology Group (SMoG) at Chalmers University of Technology, within the three-year research project ‘International Spatial Morphology Lab (SMoL)’.

The project is funded by Älvstranden Utveckling AB in the framework of a larger cooperation project called Fusion Point Gothenburg. The data is shared via SND to create a research infrastructure that is open to new study initiatives.

1. Batty, M. (2013), The New Science of Cities, Cambridge: MIT Press.
2. Hillier, B., (1996), Space Is the Machine. Cambridge: University of Cambridge

12 GIS-layers of the street network in Gothenburg, from 1960 to 2015, in 5-year intervals. File format: shapefile (.shp), MapinfoTAB (.TAB). The coordinate system used is SWEREF 99TM, EPSG:3006.

See the attached Technical Documentation for the description and further details on the production of the datasets. See the attached Report for the description of the related research project.