The reference spatial database for 2019 contains 5142 plots. We use it to calculate a land use map from satellite images. It is organized according to a nested 3-level nomenclature. This is an update of the 2018 database. The sources and techniques used to build the database by land use groups are described below: For agricultural areas, we use a land use database based on farmers' declarations (for EU subsidies). This is the "Registre Parcellaire Graphique" (RPG) published in France by the French Institute for Geographical and Forestry Informations (IGN). The description of this data is available here: http://professionnels.ign.fr/doc/DC_DL_RPG-2-0.pdf. These vector data localize the crops. The release times imply that we use the RPG for last year (2018). It is therefore necessary to verify the good coherence of the data with the image at very high spatial resolution (VHSR) Pleiades. The RPG provides little information on arboriculture. For these classes we called on colleagues specialized in mango, lychee and citrus crops who are familiar with their area and can locate plots in the VHSR image. The plots of the "greenhouse or shade cultivation" class are derived from the "industrial building" layer of the IGN's "BD Topo" product. A random selection of 20% of the polygons in the layer height field allows to keep a diversity of greenhouse types. Each polygon was verified by photo-interpretation of the Pleiades image. If the greenhouse or shade was not visible in the image, the polygon was removed. The distinction between mowed and grazed grasslands was completed through collaboration with colleagues from the SELMET joint research unit (Emmanuel Tillard, Expédit Rivière, Colas Gabriel Tovmassian and Jeanne Averna). For natural areas , there is no regularly updated mapping, but the main classes can be recognized from the GIS layers of government departments that manage these areas (ONF and DEAL). Two specific classes have been added (identified by photo-interpretation): a class of shadows due to the island's steep relief (areas not visible because of the cast shade) and a class of vegetation located on steep slopes facing the morning sun called "rampart moor". The polygons for the distinction of savannahs have been improved thanks to the knowledge of Xavier Amelot (CNRS), Béatrice Moppert and Quentin Rivière (University of La Réunion). For wet land areas , the "marsh" and "water" classes were obtained by photo-interpretation of the 2019 Pleiades image. These classes are easily recognizable on this type of image. For urban areas we randomly selected polygons from the IGN BD Topo product. For the housing type building, 4 building height classes have previously been created (depending on the height of the layer field) in order to preserve a good diversity of the types of buildings present on the island. A random selection of polygons from each class was then made. The "built" layer was completed by a random selection of industrial buildings from the "industrial built" layer of the IGN's BD TOPO product. This selection was made in the "nature" field of the layer (i‧e. the following types: silo, industrial and livestock). The "photovoltaic panel" class was obtained by photo-interpretation of the polygons on 2019 Pleiades image. La base de données spatiale de référence pour 2019, est constituée de 5142 polygones. Nous l'utilisons pour calculer une carte d'occupation du sol à partir d'images satellites. Elle est organisée selon une nomenclature emboitée à 3 niveaux. Il s'agit d'une mise à jour de la base de données pour 2018. Voici une brève description des sources et techniques utilisées pour la constituer en fonction des groupes d’occupation du sol : Pour les espaces agricoles , nous disposons d’une base de données d’occupation du sol basée sur les déclarations que font des agriculteurs pour demander les subventions de l’Union Européenne. Il s’agit du Registre Parcellaire Graphique (RPG) diffusé en France par l’Institut français de l’information géographique et forestière (IGN). La description de cette donnée est disponible ici : http://professionnels.ign.fr/doc/DC_DL_RPG-2-0.pdf. Ces données vecteur sont précises et peuvent servir de modèle pour localiser les cultures. Les délais de diffusion impliquent que nous utilisons le RPG de l’année N -1. Il est donc nécessaire de vérifier la bonne cohérence des données par photo-interprétation de l’image THRS. Le RPG fournit peu d’informations sur l’arboriculture. Pour ces classes nous avons fait appel aux collègues techniciens spécialisés dans les cultures de mangues, litchis et agrumes qui connaissent bien leur secteur et peuvent localiser des parcelles sur l’image THRS. Les parcelles de la classe « culture sous serre ou ombrage » sont issues de la couche « bâti industriel » de la BD Topo de l’IGN. Une sélection aléatoire de 20% des polygones dans le champ hauteur de la couche de l’IGN permet de conserver une diversité des types de serre. Chacun des polygones...

New York City’s comprehensive effort to reduce or eliminate potential losses from the hazards described in the Hazard Specific section of the website. The map includes existing and completed mitigation actions that will minimize the effects of a hazard event on New York City’s population, economy, property, building stock, and infrastructure. It is the result of a coordinated effort by 46 New York City agencies and partners to develop and implement a broad range of inventive and effective ways to mitigate hazards. Point, line, polygon features and a table for the Mitigation Actions map on the Hazard Mitigation website: www.nychazardmitigation.com/all-hazards/mitigation/actions-map/ This table contains more information on each project: https://data.cityofnewyork.us/City-Government/Hazard-Mitigation-Plan-Mitigation-Actions-Database/veqt-eu3t

This metadata record describes details of National Park Service (NPS) buildings spatial data contributed to the NPS buildings aggregated dataset by the park unit specified in the NPS Unit element of this metadata record. The data files referred to in this record are the NPS Buildings aggregated point and polygons shapefiles, not the individual park unit shapefiles. The NPS Buildings aggregated point and polygons shapefiles are in the NPS Buildings Data Transfer Standard. This data transfer standard describes a model for buildings depicted using points and polygons. This data set includes both buildings that currently exist as well as sites of those that have been relocated or destroyed. Only features of a non-sensitive nature and freely available to the public are included (although the model has been designed to accommodate sensitive data in the future, if needed). Multiple point and polygon geometries may coexist and are differentiated by point and polygon type. All buildings of interest to the National Park Service are included, including those not owned by the NPS but located within authorized park boundaries and facilities that are leased by the NPS and cooperators which are located outside park boundaries. Only structures with a roof, commonly enclosed by walls, and designed for storage, human occupancy or shelters for animals are included. Other structures not designed for occupancy such as fences or bridges are excluded. Buildings include offices, warehouses, post offices, hospitals, prisons, schools, housing and storage units. Attribute data are intentionally limited to those necessary for spatial data maintenance and stewardship. Data from external database systems are not included. The means to maintain unique identifiers for each building (BuildingID) is through use of Globally Unique Identifiers (GUIDs) assigned by the database. Information about the source and vintage of individual points and polygons are documented within the Edit_Date and Polygon_Notes attributes.

The reference spatial database for 2017 is composed of 6256 plots. We use it to calculate a land use map from satellite images.It is organized according to a nomenclature offering 3 levels of precision. We randomly selected 20% of the plots in each class to build a validation database while the remaining 80% is used for learning (5002 polygons for learning and 1254 for validation). The following is a brief description of the sources and techniques used to develop it according to land use types : For agricultural areas , we have a land use database based on farmers' declarations to apply for EU subsidies. This is the Registre Parcellaire Graphique (RPG) published in France by the French Institute of Geography (IGN). The description of this data is available here: http://professionnels.ign.fr/doc/DC_DL_RPG-2-0.pdf . These vector data are accurate and can be used as a model to locate crops. The release times imply that we use the RPG for year N -1. It is therefore necessary to check the correct consistency of the data by photo-interpretation of the VHR image. The RPG provides limited information on orchards. For these classes we called on colleagues specialised in mango, lychee and citrus fruit cultivation technicians who are familiar with their sector and can locate plots in the VHR image. Field surveys were conducted using GPS for market gardening crops. The plots of the "greenhouse or shade cultivation" class are derived from the "industrial building" layer of the IGN's "BD Topo" product of IGN. A random selection of 20% of the polygons in the height field of the IGN layer allows to keep a diversity of greenhouse types. Each of the polygons was verified by photo-interpretation of the Pleiades image. If the greenhouse or shade was not visible in the image, the polygon was deleted. For natural areas, there is no regularly updated mapping, but the main classes can be recognized from the GIS layers of the State services that manage these areas (ONF and DEAL). Two specific classes have been added (identified by photo-interpretation) to address the problems of satellite images: a class of shadows due to the island's steep terrain (areas not visible because of the shadow cast) and a class of vegetation located on steep slopes facing the morning sun called "savannah on cliffs". For wet areas, the "marsh", "water" and "hillside retention" classes were obtained by photo-interpretation of the 2017 Pleiades image. These classes are easily recognizable on this type of image. For urban areas we randomly selected polygons from the IGN's BD Topo layer. For the housing type building, 4 building height classes have previously been created (depending on the height of the layer field) in order to preserve a good diversity of the types of buildings present on the island. A random selection of polygons from each class was then made. The "built" layer was completed by a random selection of industrial buildings from the "industrial built" layer of the IGN's TOPO database. This selection was made in the "nature" field of the layer (i‧e. the following types: silo, industrial and livestock). The "photovoltaic panel" class was obtained by photo-interpretation of the polygons on the 2017 Pleiades image. La base de données spatiale de référence terrain pour 2017, est constituée de 6256 parcelles. Nous l'utilisons pour calculer une carte d'occupation du sol à partir d'image satellites. Elle est organisée selon une nomenclature emboitée à 3 niveaux. Nous avons sélectionné de façon aléatoire 20% des parcelles de chaque classe pour constituer une base de donnée de validation alors que les 80% restant sont utilisés pour l’apprentissage (5002 polygones pour l’apprentissage et 1254 pour la validation). Voici une brève description des sources et techniques utilisées pour la constituer en fonction des groupes d’occupation du sol : Pour les espaces agricoles , nous disposons d’une base de données d’occupation du sol basée sur les déclarations que font des agriculteurs pour demander les subventions de l’Union Européenne. Il s’agit du Registre Parcellaire Graphique (RPG) diffusé en France par l’Institut français de l’information géographique et forestière (IGN). La description de cette donnée est disponible ici : http://professionnels.ign.fr/doc/DC_DL_RPG-2-0.pdf. Ces données vecteur sont précises et peuvent servir de modèle pour localiser les cultures. Les délais de diffusion impliquent que nous utilisons le RPG de l’année N -1. Il est donc nécessaire de vérifier la bonne cohérence des données par photo-interprétation de l’image THRS. Le RPG fournit peu d’information sur l’arboriculture. Pour ces classes nous avons fait appel aux collègues techniciens spécialisés dans les cultures de mangues, litchis et agrumes qui connaissent bien leur secteur et peuvent localiser des parcelles sur l’image THRS. Des relevés de terrain ont été réalisés à l’aide d’un GPS pour les cultures de type maraichage. Les parcelles de la classe « culture sous...

The hydro polygon/arc coverages were created using TIGER/LINE 2000 shapefile data gathered from ESRI's Geography Network. The individual county hydrography line shapefiles were processed into Arc/Info coverages and then appended together to create complete state coverages. They were then edited to remove unwanted features, leaving a state-by-state database of both important and navigable water features. Attributes were added to denote navigable features and names. Also, an attribute was added to the polygons to denote which were water and which were land features. The state databases were then appended together to create a single, nationwide hydrography network containing named arcs and polygons. These features also contain a state FIPS. Because some of the hydro features are represented by lines instead of polygons, the complete hydro dataset consists of 2 shapefiles, one for lines and one for polygons. They must be used together to paint a complete picture.

This layer is sourced from maps.bts.dot.gov.

Atomic polygons serve as a set of basic building blocks for generating the polygons of many of the district types represented in the CSCL database. Feature classes such as election district, school district, census block, FDNY administrative company, and community district can be dissolved by combining the appropriate fields in atomic polygons.

The spatial learning database for 2018 contains 5620 plots. We use it to calculate a land use map from satellite images. It is organized according to a nested 3-level nomenclature. The sources and techniques used to build the database by land use groups are described below: For agricultural areas, we use a land use database based on farmers' declarations (for EU subsidies). This is the "Registre Parcellaire Graphique" (RPG) published in France by the French Institute for Geographical and Forestry Informations (IGN). The description of this data is available here: http://professionnels.ign.fr/doc/DC_DL_RPG-2-0.pdf. These vector data localize the crops. The release times imply that we use the RPG for last year (2017). It is therefore necessary to verify the good coherence of the data with the image at very high spatial resolution (VHSR) Pleiades. The RPG provides little information on arboriculture. For these classes we called on colleagues specialized in mango, lychee and citrus crops who are familiar with their area and can locate plots in the VHSR image. The plots of the "greenhouse or shade cultivation" class are derived from the "industrial building" layer of the IGN's "BD Topo" product. A random selection of 20% of the polygons in the layer height field allows to keep a diversity of greenhouse types. Each polygon was verified by photo-interpretation of the Pleiades image. If the greenhouse or shade was not visible in the image, the polygon was removed. The distinction between mowed and grazed grasslands was completed through collaboration with colleagues from the SELMET joint research unit (Emmanuel Tillard, Expédit Rivière, Colas Gabriel Tovmassian and Jeanne Averna). For natural areas For natural areas , there is no regularly updated mapping, but the main classes can be recognized from the GIS layers of government departments that manage these areas (ONF and DEAL). Two specific classes have been added (identified by photo-interpretation): a class of shadows due to the island's steep relief (areas not visible because of the cast shade) and a class of vegetation located on steep slopes facing the morning sun called "rampart moor". The polygons for the distinction of savannahs have been improved thanks to the knowledge of Xavier Amelot (CNRS), Béatrice Moppert and Quentin Rivière (University of La Réunion). For wet land areas , the "marsh" and "water" classes were obtained by photo-interpretation of the 2018 Pleiades image. These classes are easily recognizable on this type of image. For urban areas we randomly selected polygons from the IGN BD Topo product. For the housing type building, 4 building height classes have previously been created (depending on the height of the layer field) in order to preserve a good diversity of the types of buildings present on the island. A random selection of polygons from each class was then made. The "built" layer was completed by a random selection of industrial buildings from the "industrial built" layer of the IGN's BD TOPO product. This selection was made in the "nature" field of the layer (i‧e. the following types: silo, industrial and livestock). The "photovoltaic panel" class was obtained by photo-interpretation of the polygons on 2018 Pleiades image. La base de données spatiale d'apprentissage pour 2018, est constituée de 5620 parcelles. Nous l'utilisons pour calculer une carte d'occupation du sol à partir d'image satellites. Elle est organisée selon une nomenclature emboitée à 3 niveaux. Voici une brève description des sources et techniques utilisées pour la constituer en fonction des groupes d’occupation du sol : Pour les espaces agricoles , nous disposons d’une base de données d’occupation du sol basée sur les déclarations que font des agriculteurs pour demander les subventions de l’Union Européenne. Il s’agit du Registre Parcellaire Graphique (RPG) diffusé en France par l’Institut français de l’information géographique et forestière (IGN). La description de cette donnée est disponible ici : http://professionnels.ign.fr/doc/DC_DL_RPG-2-0.pdf. Ces données vecteur sont précises et peuvent servir de modèle pour localiser les cultures. Les délais de diffusion impliquent que nous utilisons le RPG de l’année N -1. Il est donc nécessaire de vérifier la bonne cohérence des données par photo-interprétation de l’image THRS. Le RPG fournit peu d’information sur l’arboriculture. Pour ces classes nous avons fait appel aux collègues techniciens spécialisés dans les cultures de mangues, litchis et agrumes qui connaissent bien leur secteur et peuvent localiser des parcelles sur l’image THRS. Les parcelles de la classe « culture sous serre ou ombrage » sont issues de la couche « bâti industriel » de la BD Topo de l’IGN. Une sélection aléatoire de 20% des polygones dans le champ hauteur de la couche de l’IGN permet de conserver une diversité des types de serre. Chacun des polygones a été vérifié par photo-interprétation de l’image Pléiades. Si la serre ou...

(Note: This description is taken from a draft report entitled "Creation of a Database of Lakes in the St. Johns River Water Management District of Northeast Florida" by Palmer Kinser.Introduction“Lakes are among the District’s most valued resources. Their aesthetic appeal adds substantially to waterfront property values, which in turn generate tax revenues for local governments. Fish camps and other businesses, that provide lake visitors with supplies and services, benefit local economies directly. Commercial fishing on the District’s larger lakes produces some income, , but far greater economic benefits are produced from sport fishing. Some of the best bass fishing lakes in the world occur in the District. Trophy fishing, guide services and high-stakes fishing tournaments, which they support, also generate substantial revenues for local economies. In addition, the high quality of District lakes has allowed swimming, fishing, and boating to become among the most popular outdoor activities for many District residents and attracts many visitors. Others frequently take advantage of the abundant opportunities afforded for duck hunting, bird watching, photography, and other nature related activities.”(from likelihood of harm to lakes report).ObjectiveThe objective of this work was to create a consistent database of natural lake polygon features for the St. Johns River Water Management District. Other databases examined contained point features only, polygons representing a wide range of dates, water bodies not separated or coded adequately by feature type (i.e. no distinctions were made between lakes, rivers, excavations, etc.), or were incomplete. This new database will allow users to better characterize and measure the lakes resource of the District, allowing comparisons to be made and trends detected; thereby facilitating better protection and management of the resource.BackgroundPrior to creation of this database, the District had 2 waterbody databases. The first of these, the 2002 FDEP Primary Lake Location database, contained 3859 lake point features, state-wide, 1418 of which were in SJRWMD. Only named lakes were included. Data sources were the Geographic Names Information System (GNIS), USGS 1:24000 hydrography data, 1994 Digital orthophoto quarter quadrangles (DOQQs), and USGS digital raster graphics (DRGs). The second was the SJRWMD Hydrologic Network (Lake / Pond and Reservoir classes). This data base contained 42,002 lake / pond and reservoir features for the SJRWMD. Lakes with multiple pools of open water were often mapped as multiple features and many man-made features (borrow pits, reservoirs, etc.) were included. This dataset was developed from USGS map data of varying dates.MethodsPolygons in this new lakes dataset were derived from a "wet period" landcover map (SJRWMD, 1999), in which most lake levels were relatively high. Polygons from other dates, mostly 2009, were used for lakes in regionally dry locations or for lakes that were uncharacteristically wet in 1999, e.g. Alachua Sink. Our intension was to capture lakes in a basin-full condition; neither unusually high nor low. To build the data set, a selection was made of polygons coded as lakes (5200), marshy lakes (5250, enclosed saltwater ponds in salt marsh (5430), slough waters (5600), and emergent aquatic vegetation (6440). Some large, regionally significant or named man-made reservoirs were also included, as well as a small number of named excavations. All polygons were inspected and edited, where appropriate, to correct lake shores and merge adjacent lake basin features. Water polygons separated by marshes or other low-ground features were grouped and merged to form multipart features when clearly associated within a single lake basin. The initial set of lake names were captured from the Florida Primary Lake Location database. Labels were then moved where needed to insure that they fell within the water bodies referenced. Additional lake names were hand entered using data from USGS 7.5 minute quads, Google Maps, MapQuest, Florida Department of Transportation (FDOT) county maps, and other sources. The final dataset contains 4892 polygons, many of which are multi-part.Operationally, lakes, as captured in this data base, are those features that were identified and mapped using the District’s landuse/landcover scheme in the 5200, 5250, 5430, 5600 classes referenced above; in addition to some areas mapped tin the 6440 class. Some additional features named as lakes, ponds, or reservoirs were also included, even when not currently appearing to be lakes. Some are now very marshy or even dry, but apparently held deeper pools of water in the past. A size limit of 1 acre or more was enforced, except for named features, 30 of which were smaller. The smallest lake was Fox Lake, a doline of 0.04 acres in Orange county. The largest lake, Lake George covered 43,212.8 acres.The lakes of the SJRWMD are a diverse set of features that may be classified in many ways. These include: by surrounding landforms or landcover, by successional stage (lacustrine to palustrine gradient), by hydrology (presence of inflows and/or outflows, groundwater linkages, permanence, etc.), by water quality (trophic state, water color, dissolved solids, etc.), and by origin. We chose to classify the lakes in this set by origin, based on the lake type concepts of Hutchinson (1957). These types are listed in the table below (Table 1). We added some additional types and modified the descriptions to better reflect Florida’s geological conditions (Table 2). Some types were readily identified, others are admittedly conjectural or were of mixed origins, making it difficult to pick a primary mechanism. Geological map layers, particularly total thickness of overburden above the Floridan aquifer system and thickness of the intermediate confining unit, were used to estimate the likelihood of sinkhole formation. Wind sculpting appears to be common and sometimes is a primary mechanism but can be difficult to judge from remotely sensed imagery. For these and others, the classification should be considered provisional. Many District lakes appear to have been formed by several processes, for instance, sinkholes may occur within lakes which lie between sand dunes. Here these would be classified as dune / karst. Mixtures of dunes, deflation and karst are common. Saltmarsh ponds vary in origin and were not further classified. In the northern coastal area they are generally small, circular in outline and appear to have been formed by the collapse and breakdown of a peat substrate, Hutchinson type 70. Further south along the coast additional ponds have been formed by the blockage of tidal creeks, a fluvial process, perhaps of Hutchinson’s Type 52, lateral lakes, in which sediments deposited by a main stream back up the waters of a tributary. In the area of the Cape Canaveral, many salt marsh ponds clearly occupy dune swales flooded by rising ocean levels. A complete listing of lake types and combinations is in Table 3. TypeSub-TypeSecondary TypeTectonic BasinsMarine BasinTectonic BasinsMarine BasinCompound dolineTectonic BasinsMarine BasinkarstTectonic BasinsMarine BasinPhytogenic damTectonic BasinsMarine BasinAbandoned channelTectonic BasinsMarine BasinKarstSolution LakesCompound dolineSolution LakesCompound dolineFluvialSolution LakesCompound dolinePhytogenicSolution LakesDolineSolution LakesDolineDeflationSolution LakesDolineDredgedSolution LakesDolineExcavatedSolution LakesDolineExcavationSolution LakesDolineFluvialSolution LakesKarstKarst / ExcavationSolution LakesKarstKarst / FluvialSolution LakesKarstDeflationSolution LakesKarstDeflation / excavationSolution LakesKarstExcavationSolution LakesKarstFluvialSolution LakesPoljeSolution LakesSpring poolSolution LakesSpring poolFluvialFluvialAbandoned channelFluvialFluvialFluvial Fluvial PhytogenicFluvial LeveeFluvial Oxbow lakeFluvial StrathFluvial StrathPhytogenicAeolianDeflationAeolianDeflationDuneAeolianDeflationExcavationAeolianDeflationKarstAeolianDuneAeolianDune DeflationAeolianDuneExcavationAeolianDuneAeolianDuneKarstShoreline lakesMaritime coastalKarst / ExcavationOrganic accumulationPhytogenic damSalt Marsh PondsMan madeExcavationMan madeDam

Atomic polygons serve as a set of basic building blocks for generating the polygons of many of the district types represented in the NYC Street Centerline (CSCL) database. Feature classes such as election district, school district, census block, FDNY administrative company, and community district can be dissolved by combining the appropriate fields in atomic polygons. All previously released versions of this data are available on the DCP Website: BYTES of the BIG APPLE.

Note: This description is taken from a draft report entitled "Creation of a Database of Lakes in the St. Johns River Water Management District of Northeast Florida" by Palmer Kinser. Introduction“Lakes are among the District’s most valued resources. Their aesthetic appeal adds substantially to waterfront property values, which in turn generate tax revenues for local governments. Fish camps and other businesses, that provide lake visitors with supplies and services, benefit local economies directly. Commercial fishing on the District’s larger lakes produces some income, , but far greater economic benefits are produced from sport fishing. Some of the best bass fishing lakes in the world occur in the District. Trophy fishing, guide services and high-stakes fishing tournaments, which they support, also generate substantial revenues for local economies. In addition, the high quality of District lakes has allowed swimming, fishing, and boating to become among the most popular outdoor activities for many District residents and attracts many visitors. Others frequently take advantage of the abundant opportunities afforded for duck hunting, bird watching, photography, and other nature related activities.”(from likelihood of harm to lakes report).ObjectiveThe objective of this work was to create a consistent database of natural lake polygon features for the St. Johns River Water Management District. Other databases examined contained point features only, polygons representing a wide range of dates, water bodies not separated or coded adequately by feature type (i.e. no distinctions were made between lakes, rivers, excavations, etc.), or were incomplete. This new database will allow users to better characterize and measure the lakes resource of the District, allowing comparisons to be made and trends detected; thereby facilitating better protection and management of the resource.BackgroundPrior to creation of this database, the District had 2 waterbody databases. The first of these, the 2002 FDEP Primary Lake Location database, contained 3859 lake point features, state-wide, 1418 of which were in SJRWMD. Only named lakes were included. Data sources were the Geographic Names Information System (GNIS), USGS 1:24000 hydrography data, 1994 Digital orthophoto quarter quadrangles (DOQQs), and USGS digital raster graphics (DRGs). The second was the SJRWMD Hydrologic Network (Lake / Pond and Reservoir classes). This data base contained 42,002 lake / pond and reservoir features for the SJRWMD. Lakes with multiple pools of open water were often mapped as multiple features and many man-made features (borrow pits, reservoirs, etc.) were included. This dataset was developed from USGS map data of varying dates.MethodsPolygons in this new lakes dataset were derived from a "wet period" landcover map (SJRWMD, 1999), in which most lake levels were relatively high. Polygons from other dates, mostly 2009, were used for lakes in regionally dry locations or for lakes that were uncharacteristically wet in 1999, e.g. Alachua Sink. Our intension was to capture lakes in a basin-full condition; neither unusually high nor low. To build the data set, a selection was made of polygons coded as lakes (5200), marshy lakes (5250, enclosed saltwater ponds in salt marsh (5430), slough waters (5600), and emergent aquatic vegetation (6440). Some large, regionally significant or named man-made reservoirs were also included, as well as a small number of named excavations. All polygons were inspected and edited, where appropriate, to correct lake shores and merge adjacent lake basin features. Water polygons separated by marshes or other low-ground features were grouped and merged to form multipart features when clearly associated within a single lake basin. The initial set of lake names were captured from the Florida Primary Lake Location database. Labels were then moved where needed to insure that they fell within the water bodies referenced. Additional lake names were hand entered using data from USGS 7.5 minute quads, Google Maps, MapQuest, Florida Department of Transportation (FDOT) county maps, and other sources. The final dataset contains 4892 polygons, many of which are multi-part.Operationally, lakes, as captured in this data base, are those features that were identified and mapped using the District’s landuse/landcover scheme in the 5200, 5250, 5430, 5600 classes referenced above; in addition to some areas mapped tin the 6440 class. Some additional features named as lakes, ponds, or reservoirs were also included, even when not currently appearing to be lakes. Some are now very marshy or even dry, but apparently held deeper pools of water in the past. A size limit of 1 acre or more was enforced, except for named features, 30 of which were smaller. The smallest lake was Fox Lake, a doline of 0.04 acres in Orange county. The largest lake, Lake George covered 43,212.8 acres.The lakes of the SJRWMD are a diverse set of features that may be classified in many ways. These include: by surrounding landforms or landcover, by successional stage (lacustrine to palustrine gradient), by hydrology (presence of inflows and/or outflows, groundwater linkages, permanence, etc.), by water quality (trophic state, water color, dissolved solids, etc.), and by origin. We chose to classify the lakes in this set by origin, based on the lake type concepts of Hutchinson (1957). These types are listed in the table below (Table 1). We added some additional types and modified the descriptions to better reflect Florida’s geological conditions (Table 2). Some types were readily identified, others are admittedly conjectural or were of mixed origins, making it difficult to pick a primary mechanism. Geological map layers, particularly total thickness of overburden above the Floridan aquifer system and thickness of the intermediate confining unit, were used to estimate the likelihood of sinkhole formation. Wind sculpting appears to be common and sometimes is a primary mechanism but can be difficult to judge from remotely sensed imagery. For these and others, the classification should be considered provisional. Many District lakes appear to have been formed by several processes, for instance, sinkholes may occur within lakes which lie between sand dunes. Here these would be classified as dune / karst. Mixtures of dunes, deflation and karst are common. Saltmarsh ponds vary in origin and were not further classified. In the northern coastal area they are generally small, circular in outline and appear to have been formed by the collapse and breakdown of a peat substrate, Hutchinson type 70. Further south along the coast additional ponds have been formed by the blockage of tidal creeks, a fluvial process, perhaps of Hutchinson’s Type 52, lateral lakes, in which sediments deposited by a main stream back up the waters of a tributary. In the area of the Cape Canaveral, many salt marsh ponds clearly occupy dune swales flooded by rising ocean levels. A complete listing of lake types and combinations is in Table 3. TypeSub-TypeSecondary TypeTectonic BasinsMarine BasinTectonic BasinsMarine BasinCompound dolineTectonic BasinsMarine BasinkarstTectonic BasinsMarine BasinPhytogenic damTectonic BasinsMarine BasinAbandoned channelTectonic BasinsMarine BasinKarstSolution LakesCompound dolineSolution LakesCompound dolineFluvialSolution LakesCompound dolinePhytogenicSolution LakesDolineSolution LakesDolineDeflationSolution LakesDolineDredgedSolution LakesDolineExcavatedSolution LakesDolineExcavationSolution LakesDolineFluvialSolution LakesKarstKarst / ExcavationSolution LakesKarstKarst / FluvialSolution LakesKarstDeflationSolution LakesKarstDeflation / excavationSolution LakesKarstExcavationSolution LakesKarstFluvialSolution LakesPoljeSolution LakesSpring poolSolution LakesSpring poolFluvialFluvialAbandoned channelFluvialFluvialFluvial Fluvial PhytogenicFluvial LeveeFluvial Oxbow lakeFluvial StrathFluvial StrathPhytogenicAeolianDeflationAeolianDeflationDuneAeolianDeflationExcavationAeolianDeflationKarstAeolianDuneAeolianDune DeflationAeolianDuneExcavationAeolianDuneAeolianDuneKarstShoreline lakesMaritime coastalKarst / ExcavationOrganic accumulationPhytogenic damSalt Marsh PondsMan madeExcavationMan madeDam

Shellfish Beds Managed Set: The Connecticut Department of Environmental Protection cooperated with the Department of Agriculture, Bureau of Aquaculture to publish the Connecticut Mananged Shellfish Bed data. More recent information may now be available from Department of Agriculture since the time this information was originally published in 2004. Connecticut Shellfish Bed Mapping - The Town_Merge data layer is one of four layers that were created in the mapping of all managed shellfish beds in Connecticut waters. These beds, as defined below, include state managed beds, municipally managed beds, natural beds and recreational beds. These four bed types were mapped as separate data layers. This project was undertaken to assist three agencies, The National Oceanic and Atmospheric Administration (NOAA), Connecticut Department of Environmental Protection (CTDEP) and the Connecticut Department of Agriculture Bureau of Aquaculture (DA/BA). While the over all goal of all three was the same, namely the protection of natural resources, each had different specific needs. The project was originally undertaken without NOAA involvement. In 2001Public Act PA01-115 An Act Concerning Recreational Fishing in Connecticut was passed. Information on this act can be found through the Connecticut State Library at http://www.cslib.org/psaindex.htm. This act required DA/DB and CTDEP to determine the "effects of commercial and recreational fishing" on eel grass beds. Harry Yamalis from the Office of Long Island Sound Programs (OLISP) initiated gathering information and mapping the beds on a part time basis in response to this Public Act. Later, NOAA requested assistance in building a national database of Marine Managed Areas (MMA) in accordance with federal Executive Order 13158 concerning Marine Protected Areas (MPA). NOAA and CTDEP agreed that the shellfish beds met the criteria for MMA's. Tom Ouellette from OLISP was the liaison between CTDEP and NOAA and became the project supervisor. Todd Coniff was hired as an intern through Coastal State Organization,which is overseeing the MMA inventory collection program for NOAA, to continue the work on a full time basis. Several people from the Environmental and Geographic Information Center at CTDEP provided technical and other guidance. As noted earlier each agency had its on agenda for mapping the shellfish beds. The follow paragraphs outline the wants and needs of NOAA, DA/BA and CTDEP. The following is a description of the process and function of the MMA inventory for NOAA. The following excerpt was taken from the MPA web site http://www.mpa.gov/. The Marine Managed Areas Inventory Database and Data Collection Process The inventory will contain a wide range of information on each site to help the U.S. develop a comprehensive picture of the nation's marine managed areas (MMAs). The data collected include a general description and site characteristics such as location, purpose, and type of site, along with detailed information on natural and cultural resources, legal authorities, site management, regulations, and restrictions (see MMA Inventory Database Description at http://www.mpa.gov/inventory/database_description.html). The data collection process begins with agencies or authorities that manage marine and Great Lakes areas in U.S. waters. Each agency reviews sites in their programs to identify those that meet the MMA working criteria. Data collection is then conducted for each site by the managing agency with an electronic data entry form. The managing agencies also review and approve the data before submission to the NOAA/Department of the Interior Inventory team. The data are then reviewed and made public on MPA.GOV. A data update and revision process is being developed to ensure that the information in the inventory is kept current over time Purposes of MMA Inventory The national inventory provides a range of data on all types of MMAs in the U.S. This database can help federal, regional, state, a

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Analysis of ‘Hazard Mitigation Plan - Mitigation Actions Database (Polygons)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/32bc1ac6-2a05-4701-ae2c-d480fccdf07e on 26 January 2022.

--- Dataset description provided by original source is as follows ---

New York City’s comprehensive effort to reduce or eliminate potential losses from the hazards described in the Hazard Specific section of the website. The map includes existing and completed mitigation actions that will minimize the effects of a hazard event on New York City’s population, economy, property, building stock, and infrastructure. It is the result of a coordinated effort by 46 New York City agencies and partners to develop and implement a broad range of inventive and effective ways to mitigate hazards. Point, line, polygon features and a table for the Mitigation Actions map on the Hazard Mitigation website: www.nychazardmitigation.com/all-hazards/mitigation/actions-map/

This table contains more information on each project: https://data.cityofnewyork.us/City-Government/Hazard-Mitigation-Plan-Mitigation-Actions-Database/veqt-eu3t

--- Original source retains full ownership of the source dataset ---

FRIBAS-DB is a database that comprises information about 312 buildings (237 reinforced concrete moment resisting frame, 71 unreinforced masonry, 4 mixed type). For each building 37 parameters related to the main building characteristics (age, height, structural typology and main vibrational period), and foundation soil characteristics (e.g. resonance frequency, outcropping geology, seismic soil class, topographic class) are reported. The 312 buildings are located in Basilicata and Friuli Venezia Giulia regions (Southern and North-eastern Italy, respectively) and in different geological and built-environment settings. The FRIBAS-DB allows studying the influence of these parameters for the building dynamic response. In the following the details of each field of the FRIBAS-DB are given. ID_GIS: unique identifier for each building; ID: building identifier containing a number and the province acronym (MT=Matera; PZ=Potenza; VdA=Villa d’Agri; FVG=Friuli Venezia Giulia); Municipality: name of the municipality; COD_COM: municipality code according to the national institute of statistics (ISTAT); LAT, LONG: coordinates of the building in the WGS84-UTM - zone 33N (EPSG:32633); Construction material: RC for Reinforced Concrete Moment Resisting Frame; Masonry for unreinforced masonry buildings; Mixed refers to buildings with both reinforced concrete and load-bearing masonry elements; Soft storey: presence of a soft storey, i.e. a floor that can activate a weak-floor failure mechanism; Building use: Residential, Public, Industrial, Turistic; Age of Construction: < 1919; <1988*; 1919-1945; 1946-1961; 1962-1971; 1972-1975; 1976-1981; 1982-1991; 1992-1996; 1992-2001; 1997-2001; 2002-2008; >2008; (*a more accurate class attribution has not been possible); # Floors: the last floor was included when its estimated volume was comparable with the those of other floors in the building; Presence of basement: all the floors that are partially or totally below ground are considered as basement; Building height from the ground to the top of the roof (m): if the building is located on a slope, the ground floor is considered to be the one at the higher side of the slope; Building height from the basement to the top of the roof (m): total height, including also the basement and the structures present at the top of the building; Building width B (m): the shorter dimension of a circumscribing polygon; Building length L (m): the longer dimension of a circumscribing polygon; B/L: ratio between building width and building length as a measure for regularity in plan; B/H: ratio between building width and building height (from the ground level to the top of the building); Floor area (m2): the building area calculated based on building footprints (e.g. from openstreetmaps or available national/regional digital maps); Polygon area (m2): the area of the circumscribing polygon; Area ratio: ratio between floor and polygon area Building shape: geometric shape of the building, R (rectangle), S (square), T (T-shape), L (L-shape), C (C-shape), H (H-shape), Tr (trapezoid); Seismic provisions (masonry): Description of any seismic provisions (e.g. additional pillars, ring beam, walls reinforcement, tie-rods) if present; Masonry openings (%): percentage of openings with respect to the building lateral surface; Masonry type: type of load-bearing masonry, including material (e.g. stone, bricks, concrete blocks), layout (regular, irregular) and quality; Slab: rigid or flexible, rigid floors often consist of reinforced concrete and hollow tiles; Roof type: wood (with or without hollow tiles), reinforced concrete (with or without hollow tiles); Additional floors: added afterwards to the building, but not included in the original project; Foundation type: shallow or deep; Position of the building: single block is for buildings that consist of a single unit; in case of multiple blocks (e.g. in the case of attached buildings), we distinguish between internal buildings (attached to two or more buildings) and buildings located at the edge (far end blocks attached only to one building). The presence of seismic joints or staircases is specified in the text field; F1_building (Hz): the experimental fundamental frequencies in two directions of the buildings (longitudinal and transversal) were considered, defined as F1_building (lower value) and as F2_buildings (higher value). The fundamental vibrational frequencies for all buildings have been estimated from single station ambient noise measurements analysed through the Horizontal-to-Vertical Spectral Ratio technique. The noise was recorded at the top of each building, aligning the horizontal axes of the sensor parallel to the two main building axes. Measurements were carried out using two different instruments (Tromino and Lunitek Sentinel GEO). The recording time varies between 10 and 30 minutes. The HVSRs have been estimated by the following procedure: each component was divided into non-overlapping windows...

Summary The RITA/BTS desires a single nationwide database that has polygons/attributes for major rivers and lakes, is consistent across state lines, and has the most accurate data available. This database will be used primarily for map production, basic queries, and will be distributed as part of the National Transportation Atlas Databases (NTAD) 2008. Description The hydro polygon/arc coverages were created using TIGER/LINE 2000 shapefile data gathered from ESRI's Geography Network. The individual county hydrography line shapefiles were processed into Arc/Info coverages and then appended together to create complete state coverages. They were then edited to remove unwanted features, leaving a state-by-state database of both important and navigable water features. Attributes were added to denote navigable features and names. Also, an attribute was added to the polygons to denote which were water and which were land features. The state databases were then appended together to create a single, nationwide hydrography network containing named arcs and polygons. These features also contain a state FIPS. Because some of the hydro features are represented by lines instead of polygons, the complete hydro dataset consists of 2 shapefiles, one for lines and one for polygons. They must be used together to paint a complete picture.

Shellfish Beds Managed Set:

The Connecticut Department of Environmental Protection cooperated with the Department of Agriculture, Bureau of Aquaculture to publish the Connecticut Mananged Shellfish Bed data. More recent information may now be available from Department of Agriculture since the time this information was originally published in 2004. Connecticut Shellfish Bed Mapping - The Town_Merge data layer is one of four layers that were created in the mapping of all managed shellfish beds in Connecticut waters. These beds, as defined below, include state managed beds, municipally managed beds, natural beds and recreational beds. These four bed types were mapped as separate data layers. This project was undertaken to assist three agencies, The National Oceanic and Atmospheric Administration (NOAA), Connecticut Department of Environmental Protection (CTDEP) and the Connecticut Department of Agriculture Bureau of Aquaculture (DA/BA). While the over all goal of all three was the same, namely the protection of natural resources, each had different specific needs. The project was originally undertaken without NOAA involvement. In 2001Public Act PA01-115 An Act Concerning Recreational Fishing in Connecticut was passed. Information on this act can be found through the Connecticut State Library at http://www.cslib.org/psaindex.htm. This act required DA/DB and CTDEP to determine the "effects of commercial and recreational fishing" on eel grass beds. Harry Yamalis from the Office of Long Island Sound Programs (OLISP) initiated gathering information and mapping the beds on a part time basis in response to this Public Act. Later, NOAA requested assistance in building a national database of Marine Managed Areas (MMA) in accordance with federal Executive Order 13158 concerning Marine Protected Areas (MPA). NOAA and CTDEP agreed that the shellfish beds met the criteria for MMA's. Tom Ouellette from OLISP was the liaison between CTDEP and NOAA and became the project supervisor. Todd Coniff was hired as an intern through Coastal State Organization,which is overseeing the MMA inventory collection program for NOAA, to continue the work on a full time basis. Several people from the Environmental and Geographic Information Center at CTDEP provided technical and other guidance. As noted earlier each agency had its on agenda for mapping the shellfish beds. The follow paragraphs outline the wants and needs of NOAA, DA/BA and CTDEP. The following is a description of the process and function of the MMA inventory for NOAA. The following excerpt was taken from the MPA web site http://www.mpa.gov/. The Marine Managed Areas Inventory Database and Data Collection Process The inventory will contain a wide range of information on each site to help the U.S. develop a comprehensive picture of the nation's marine managed areas (MMAs). The data collected include a general description and site characteristics such as location, purpose, and type of site, along with detailed information on natural and cultural resources, legal authorities, site management, regulations, and restrictions (see MMA Inventory Database Description at http://www.mpa.gov/inventory/database_description.html). The data collection process begins with agencies or authorities that manage marine and Great Lakes areas in U.S. waters. Each agency reviews sites in their programs to identify those that meet the MMA working criteria. Data collection is then conducted for each site by the managing agency with an electronic data entry form. The managing agencies also review and approve the data before submission to the NOAA/Department of the Interior Inventory team. The data are then reviewed and made public on MPA.GOV. A data update and revision process is being developed to ensure that the information in the inventory is kept current over time Purposes of MMA Inventory The national inventory provides a range of data on all types of MMAs in the U.S. This database can help federal, regional, state, a

Part of the Nova Scotia Topographic Database (NSTDB), the buildings theme layer is updated and maintained from aerial photography. Buildings over 30m (one side) are collected as polygons, all others as points. Selected buildings are inspected in the field or interpreted from aerial photography and classified according to use such as hospitals, schools, police stations or community centres. Building feature codes and their descriptions are provided with the download in a NSTDB feature code table. Data download also available via GeoNova: https://nsgi.novascotia.ca/WSF_DDS/DDS.svc/DownloadFile?tkey=fhrTtdnDvfytwLz6&id=20 Map service view also available via GeoNova: https://nsgiwa.novascotia.ca/arcgis/rest/services/BASE/BASE_NSTDB_10k_Buildings_UT83/MapServer?f=jsapi

FRIBAS-DB is a database that comprises information about 312 buildings (237 reinforced concrete moment resisting frame, 71 unreinforced masonry, 4 mixed type). For each building 37 parameters related to the main building characteristics (age, height, structural typology and main vibrational period), and foundation soil characteristics (e.g. resonance frequency, outcropping geology, seismic soil class, topographic class) are reported. The 312 buildings are located in Basilicata and Friuli Venezia Giulia regions (Southern and North-eastern Italy, respectively) and in different geological and built-environment settings. The FRIBAS-DB allows studying the influence of these parameters for the building dynamic response.

In the following the details of each field of the FRIBAS-DB are given.

ID_GIS: unique identifier for each building;

ID: building identifier containing a number and the province acronym (MT=Matera; PZ=Potenza; VdA=Villa d’Agri; FVG=Friuli Venezia Giulia);

Municipality: name of the municipality;

COD_COM: municipality code according to the national institute of statistics (ISTAT);

LAT, LONG: coordinates of the building in the WGS84-UTM - zone 33N (EPSG:32633);

Construction material: RC for Reinforced Concrete Moment Resisting Frame; Masonry for unreinforced masonry buildings; Mixed refers to buildings with both reinforced concrete and load-bearing masonry elements;

Soft storey: presence of a soft storey, i.e. a floor that can activate a weak-floor failure mechanism;

Building use: Residential, Public, Industrial, Turistic;

Age of Construction: < 1919; <1988*; 1919-1945; 1946-1961; 1962-1971; 1972-1975; 1976-1981; 1982-1991; 1992-1996; 1992-2001; 1997-2001; 2002-2008; >2008; (*a more accurate class attribution has not been possible);

# Floors: the last floor was included when its estimated volume was comparable with the those of other floors in the building;

Presence of basement: all the floors that are partially or totally below ground are considered as basement;

Building height from the ground to the top of the roof (m): if the building is located on a slope, the ground floor is considered to be the one at the higher side of the slope;

Building height from the basement to the top of the roof (m): total height, including also the basement and the structures present at the top of the building;

Building width B (m): the shorter dimension of a circumscribing polygon;

Building length L (m): the longer dimension of a circumscribing polygon;

B/L: ratio between building width and building length as a measure for regularity in plan;

B/H: ratio between building width and building height (from the ground level to the top of the building);

Floor area (m²): the building area calculated based on building footprints (e.g. from openstreetmaps or available national/regional digital maps);

Polygon area (m²): the area of the circumscribing polygon;

Area ratio: ratio between floor and polygon area

Building shape: geometric shape of the building, R (rectangle), S (square), T (T-shape), L (L-shape), C (C-shape), H (H-shape), Tr (trapezoid);

Seismic provisions (masonry): Description of any seismic provisions (e.g. additional pillars, ring beam, walls reinforcement, tie-rods) if present;

Masonry openings (%): percentage of openings with respect to the building lateral surface;

Masonry type: type of load-bearing masonry, including material (e.g. stone, bricks, concrete blocks), layout (regular, irregular) and quality;

Slab: rigid or flexible, rigid floors often consist of reinforced concrete and hollow tiles;

Roof type: wood (with or without hollow tiles), reinforced concrete (with or without hollow tiles);

Additional floors: added afterwards to the building, but not included in the original project;

Foundation type: shallow or deep;

Position of the building: single block is for buildings that consist of a single unit; in case of multiple blocks (e.g. in the case of attached buildings), we distinguish between internal buildings (attached to two or more buildings) and buildings located at the edge (far end blocks attached only to one building). The presence of seismic joints or staircases is specified in the text field;

F1_building (Hz): the experimental fundamental frequencies in two directions of the buildings (longitudinal and transversal) were considered, defined as F1_building (lower value) and as F2_buildings (higher value). The fundamental vibrational frequencies for all buildings have been estimated from single station ambient noise measurements analysed through the Horizontal-to-Vertical Spectral Ratio technique. The noise was recorded at the top of each building, aligning the horizontal axes of the sensor parallel to the two main building axes. Measurements were carried out using two different instruments (Tromino and Lunitek Sentinel GEO). The recording time varies between 10 and 30 minutes. The HVSRs have been estimated by the following procedure: each component was divided into non-overlapping windows of 20 s; each window was detrended, tapered (set to 0.5), padded, Fast Fourier Transformed and smoothed with triangular windows with a width equal to 5% of the central frequency. For each of the 20 s windows, the arithmetic mean of the two horizontal component’s spectra was used to combine E-W and N-S components in the single horizontal (H) spectrum; then the HVSR is computed. Finally, the average HVSR spectrum is obtained, providing also the relative ± 2 standard deviations.

F2_building (Hz): see above

F0_ Foundation Soil (Hz): the main resonance frequency obtained from HVSR analysis of single station ambient noise measurements. Measurements were carried out using two different instruments, Tromino or Reftek datalogger equipped with Lennartz 3D-Lite. The recording time varies between 10 and 30 minutes. For data analysis see “F1_building (Hz)” field. For some cases, the HVSR from microzonation studies were used;

Geology: the geological classification was inferred from field surveys or the detailed geological maps of microzonation studies at the scale of 1:5000 or 1:10.000, if available. Otherwise the geological map at the scale 1:50.000 was considered. The outcropping geology classes present are: Gravina Calcarenite (coarse-grained carbonate sandstone); Calcari M.te Viggiano (Limestones and carbonate sandstones); Marsicovetere Breccia (massive calcareous breccias); Subappennine clay; Conglomeratic deposits; Sands and sandstones; Clean Gravels; Silts and Clays; Sand; Silty Gravels; Gravels and Sands, with silt and clay; Alluvial deposits; Colluvial deposits; Eluvial and colluvial deposits; Anthropic deposits;

Soft soil/rigid soil: soils with Vs > 360 m/s have been considered as rigid soil. This class is composed mainly by outcropping bedrock (limestones, sandstones and breccias of the South-Appennine Units) and by clean coarse gravels of the Upper Friulian Plain. Soils with Vs < 360 m/s have been considered as soft soils. These are loose sediments (silts, clays, sands, gravels and their mixture) of different origin (alluvial, colluvial, eluvial or antropic).

Seismic soil class: this soil classification refers to the national building code (NTC2018, § 3.2.2) based on Vs30. Vs profiles have been measured nearby the studied buildings. If deduced by microzonation studies, they are marked by star (*).

Topographic class: The topographic class refers to the national building code classification (NTC2018, § 3.2.2).

This data release presents geologic map data for the surficial geology of the Aztec 1-degree by 2-degree quadrangle. The map area lies within two physiographic provinces of Fenneman (1928): the Southern Rocky Mountains province, and the Colorado Plateau province, Navajo section. Geologic mapping is mostly compiled from published geologic map data sources ranging from 1:24,000 to 1:250,000 scale, with limited new interpretive contributions. Gaps in map compilation are related to a lack of published geologic mapping at the time of compilation, and not necessarily a lack of surficial deposits. Much of the geology incorporated from published geologic maps is adjusted based on digital elevation model and natural-color image data sources to improve spatial resolution of the data. Spatial adjustments and new interpretations also eliminate mismatches at source map boundaries. This data set represents only the surficial geology, defined as generally unconsolidated to moderately consolidated sedimentary deposits that are Quaternary or partly Quaternary in age, and faults that have documented Quaternary offset. Bedrock and sedimentary material directly deposited as a result of volcanic activity are not included in this database, nor are faults that are not known to have moved during the Quaternary. Map units in the Aztec quadrangle include alluvium, glacial, eolian, mass-wasting, colluvium, and alluvium/colluvium deposit types. Alluvium map units, present throughout the map area, range in age from Quaternary-Tertiary to Holocene and form stream-channel, floodplain, terrace, alluvial-fan, and pediment deposits. Along glaciated drainages terraces are commonly made up of glacial outwash. Glacial map units are concentrated in the northeast corner of the map area and are mostly undifferentiated till deposited in mountain valleys during Pleistocene glaciations. Eolian map units are mostly middle Pleistocene to Holocene eolian sand deposits forming sand sheets and dunes. Mass-wasting map units are concentrated in the eastern part of the map area, and include deposits formed primarily by slide, slump, earthflow, and rock-fall processes. Colluvium and alluvium/colluvium map units form hillslope and undifferentiated valley floor/hillslope deposits, respectively. The detail of geologic mapping varies from about 1:50,000- to 1:250,000-scale depending on the scale of published geologic maps available at the time of compilation, and for new mapping, the resolution of geologic features on available basemap data. Map units are organized within geologic provinces as described by the Seamless Integrated Geologic Mapping (SIGMa) (Turner and others, 2022) extension to the Geologic Map Schema (GeMS) (USGS, 2020). For this data release, first order geologic provinces are the physiographic provinces of Fenneman (1928), which reflect the major geomorphological setting affecting depositional processes. Second order provinces are physiographic sections of Fenneman (1928) if present. Third and fourth order provinces are defined by deposit type. Attributes derived from published source maps are recorded in the map unit polygons to preserve detail and allow database users the flexibility to create derivative map units. Map units constructed by the authors are based on geologic province, general deposit type and generalized groupings of minimum and maximum age to create a number of units typical for geologic maps of this scale. Polygons representing map units were assigned a host of attributes to make that geology easily searchable. Each polygon contains a general depositional process (‘DepositGeneral’) as well as three fields that describe more detailed depositional processes responsible for some deposition in that polygon (‘LocalGeneticType1’ – ‘LocalGeneticType3’). Three fields describe the materials that make up the deposit (‘LocalMaterial1’ – ‘LocalMaterial3’) and the minimum and maximum chronostratigraphic age of a deposit is stored in the ‘LocalAgeMin’ and ‘LocalAgeMax’ fields, respectively. Where a polygon is associated with a prominent landform or a formal stratigraphic name the ‘LocalLandform’ and ‘LocalStratName’ fields are populated. The field ‘LocalThickness’ provides a textual summary of how thick a source publication described a deposit to be. Where three fields are used to describe the contents of a deposit, we attempt to place descriptors in a relative ordering such that the first field is most prominent, however for remotely interpreted deposits and some sources that provide generalized descriptions this was not possible. Values within these searchable fields are generally taken directly from source maps, however we do perform some conservative adjustments of values based on observations from the landscape and/or adjacent source maps. Where new features were interpreted from remote observations, we derive polygon attributes based on a conservative correlation to neighboring maps. Detail provided at the polygon level is simplified into a map unit by matching its values to the DescriptionOfMapUnits_Surficial table. Specifically, we construct map units within each province based on values of ‘DepositGeneral’ and a set of chronostratigraphic age bins that attempt to capture important aspects of Quaternary landscape evolution. Polygons are assigned to the mapunit with a corresponding ‘DepositGeneral’ and the narrowest chronostratigraphic age bin that entirely contains the ‘LocalAgeMin’ and ‘LocalAgeMax’ values of that polygon. Therefore, users may notice some mismatch between the age range of a polygon and the age range of the assigned map unit, where ‘LocalAgeMin’ and ‘LocalAgeMax’ (e.g., Holocene – Holocene) may define a shorter temporal range than suggested by the map unit (e.g., Holocene – late Pleistocene). This apparent discrepancy allows for detailed information to be preserved in the polygons, while also allowing for an integrated suite of map units that facilitate visualization over a large region.

Building Footprint is a Polygon FeatureClass representing the building footprints for the City of Cupertino, California. The mapped geographic area includes 11.3 square miles of western Santa Clara County in California. The building footprints data layer was originally based on aerial photographs from 2011. Continual updates are made as needed. Most updates come from digitized plat/plan approvals or from completed City project plans. Mapping accuracy meets National Map Accuracy Standards for +/-2.5 US feet. Spatial coordinate system is California State Plane West, zone III Fipszone 0403 Adszone 3326, NAD83. Scale of true display is 1:1200 (100' scale). Building Footprints has the following fields: OBJECTID: Unique identifier automatically generated by Esri type: OID, length: 4, domain: none

LEVEL_DESC: A general description of what type of structure the polygon represents type: String, length: 18, domain: none

BLDG_HIGH: The height of the highest point on the polygon - feet above sea level type: String, length: 50, domain: none

BLDG_LOW: The height of the lowest point on the polygon - feet above see level type: String, length: 50, domain: none

FloorNumbe: The number of floors the building has type: Integer, length: 4, domain: none

AssetID: Cupertino maintained GIS primary key type: String, length: 50, domain: none

Year_Built: The year the building was built type: Date, length: 8, domain: none

Bldg_Age: The age of the building type: Single, length: 4, domain: none

LegacyID: Old identifiers used to track asset migration type: Integer, length: 4, domain: none

Shape: Field that stores geographic coordinates associated with feature type: Geometry, length: 4, domain: none

GlobalID: Unique identifier automatically generated for features in enterprise database type: GlobalID, length: 38, domain: noneShape.STArea():The area of the building footprinttype: double, length: none, domain: none Shape.STLength(): The length of the perimeter of the building footprinttype: double, length: none, domain: none BLDG_HEIGHT: The height of the building, calculated by subtracting the highest and lowest points type: double, length: none, domain: none

last_edited_date: The date the database row was last updated type: Date, length: 8, domain: none

created_date: The date the database row was initially created type: Date, length: 8, domain: none