This layer shows total population counts by sex, age, and race groups data from the 2020 Census Demographic and Housing Characteristics. This is shown by Nation, Consolidated City, Census Designated Place, Incorporated Place boundaries. Each geography layer contains a common set of Census counts based on available attributes from the U.S. Census Bureau. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.   To see the full list of attributes available in this service, go to the "Data" tab above, and then choose "Fields" at the top right. Each attribute contains definitions, additional details, and the formula for calculated fields in the field description.Vintage of boundaries and attributes: 2020 Demographic and Housing Characteristics Table(s): P1, H1, H3, P2, P3, P5, P12, P13, P17, PCT12 (Not all lines of these DHC tables are available in this feature layer.)Data downloaded from: U.S. Census Bureau’s data.census.gov siteDate the Data was Downloaded: May 25, 2023Geography Levels included: Nation, Consolidated City, Census Designated Place, Incorporated PlaceNational Figures: included in Nation layer The United States Census Bureau Demographic and Housing Characteristics: 2020 Census Results 2020 Census Data Quality Geography & 2020 Census Technical Documentation Data Table Guide: includes the final list of tables, lowest level of geography by table and table shells for the Demographic Profile and Demographic and Housing Characteristics.News & Updates This layer is ready to be used in ArcGIS Pro, ArcGIS Online and its configurable apps, Story Maps, dashboards, Notebooks, Python, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the U.S. Census Bureau when using this data. Data Processing Notes: These 2020 Census boundaries come from the US Census TIGER geodatabases. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For Census tracts and block groups, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract and block group boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are unchanged and available as attributes within the data table (units are square meters).  The layer contains all US states, Washington D.C., and Puerto Rico. Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Block groups that fall within the same criteria (Block Group denoted as 0 with no area land) have also been removed.Percentages and derived counts, are calculated values (that can be identified by the "_calc_" stub in the field name). Field alias names were created based on the Table Shells file available from the Data Table Guide for the Demographic Profile and Demographic and Housing Characteristics. Not all lines of all tables listed above are included in this layer. Duplicative counts were dropped. For example, P0030001 was dropped, as it is duplicative of P0010001.To protect the privacy and confidentiality of respondents, their data has been protected using differential privacy techniques by the U.S. Census Bureau.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The TIGER/Line shapefiles include both incorporated places (legal entities) and census designated places or CDPs (statistical entities). An incorporated place is established to provide governmental functions for a concentration of people as opposed to a minor civil division (MCD), which generally is created to provide services or administer an area without regard, necessarily, to population. Places always nest within a state, but may extend across county and county subdivision boundaries. An incorporated place usually is a city, town, village, or borough, but can have other legal descriptions. CDPs are delineated for the decennial census as the statistical counterparts of incorporated places. CDPs are delineated to provide data for settled concentrations of population that are identifiable by name, but are not legally incorporated under the laws of the state in which they are located. The boundaries for CDPs often are defined in partnership with state, local, and/or tribal officials and usually coincide with visible features or the boundary of an adjacent incorporated place or another legal entity. CDP boundaries often change from one decennial census to the next with changes in the settlement pattern and development; a CDP with the same name as in an earlier census does not necessarily have the same boundary. The only population/housing size requirement for CDPs is that they must contain some housing and population. The boundaries of most incorporated places in this shapefile are as of January 1, 2015, as reported through the Census Bureau's Boundary and Annexation Survey (BAS). The boundaries of all CDPs were delineated as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2010 Census.

This polygon shapefile represents 2010 census designated places (CDP) in Monterey County, California. Places, for the reporting of decennial census data, include census designated places, consolidated cities, and incorporated places. Each place is assigned a five-digit Federal Information Processing Standards (FIPS) code, based on the alphabetical order of the place name within each state. If place names are duplicated within a state, and they represent distinctly different areas, a separate code is assigned to each place name alphabetically by primary county in which each place is located, or if both places are in the same county, alphabetically by their legal description (for example, ''city'' before ''village''). Census designated places (CDPs) are delineated for each decennial census as the statistical counterparts of incorporated places. CDPs are delineated to provide census data for concentrations of population, housing, and commercial structures that are identifiable by name but are not within an incorporated place. CDP boundaries usually are defined in cooperation with state, local, and tribal officials. These boundaries, which usually coincide with visible features or the boundary of an adjacent incorporated place or other legal entity boundary, have no legal status, nor do these places have officials elected to serve traditional municipal functions. CDP boundaries may change from one decennial census to the next with changes in the settlement pattern; a CDP with the same name as in an earlier census does not necessarily have the same boundary.Consolidated City. An incorporated place that has combined its governmental functions with a county or subcounty entity but contains one or more other incorporated places that continue to function as local governments within the consolidated government. This layer is part of a collection of GIS data for Monterey County in California.

The Census Designated Places 2020 (CDP 2020) boundary usually is defined by the Census Bureau in cooperation with state, local or tribal officials. The boundaries are updated prior to each decennial census. These boundaries, which usually coincide with visible features or the boundary of an adjacent incorporated place or another legal entity boundary, have no legal status, nor do these places have officials elected to serve traditional municipal functions. CDP boundaries may change from one decennial census to the next with changes in the settlement pattern; a CDP with the same name as in an earlier census does not necessarily have the same boundary. CDPs must be contained within a single state and may not extend into an incorporated place. There are no population size requirements for CDPs. incorporatedCDP data is download from Census Bureau's TIGER 2020 website (https://www2.census.gov/geo/tiger/TIGER2020/PLACE/) and extracted for Los Angeles County. This data includes LA County 88 incorporated cities and 54 CDPs.

This dataset represents point locations of cities and towns in Arizona. The data contains point locations for incorporated cities, Census Designated Places and populated places. Several data sets were used as inputs to construct this data set. A subset of the Geographic Names Information System (GNIS) national dataset for the state of Arizona was used for the base location of most of the points. Polygon files of the Census Designated Places (CDP), from the U.S. Census Bureau and an incorporated city boundary database developed and maintained by the Arizona State Land Department were also used for reference during development. Every incorporated city is represented by a point, originally derived from GNIS. Some of these points were moved based on local knowledge of the GIS Analyst constructing the data set. Some of the CDP points were also moved and while most CDP's of the Census Bureau have one point location in this data set, some inconsistencies were allowed in order to facilitate the use of the data for mapping purposes. Population estimates were derived from data collected during the 2010 Census. During development, an additional attribute field was added to provide additional functionality to the users of this data. This field, named 'DEF_CAT', implies definition category, and will allow users to easily view, and create custom layers or datasets from this file. For example, new layers may created to include only incorporated cities (DEF_CAT = Incorporated), Census designated places (DEF_CAT = Incorporated OR DEF_CAT = CDP), or all cities that are neither CDP's or incorporated (DEF_CAT= Other). This data is current as of February 2012. At this time, there is no planned maintenance or update process for this dataset.This data is created to serve as base information for use in GIS systems for a variety of planning, reference, and analysis purposes. This data does not represent a legal record.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This dataset is part of the Geographical repository maintained by Opendatasoft. This dataset contains data for places and equivalent entities in United States of America.This layer both incorporated places (legal entities) and census designated places or CDPs (statistical entities). An incorporated place is established to provide governmental functions for a concentration of people as opposed to a minor civil division (MCD), which generally is created to provide services or administer an area without regard, necessarily, to population. Places always nest within a state, but may extend across county and county subdivision boundaries. An incorporated place usually is a city, town, village, or borough, but can have other legal descriptions. CDPs are delineated for the decennial census as the statistical counterparts of incorporated places. CDPs are delineated to provide data for settled concentrations of population that are identifiable by name, but are not legally incorporated under the laws of the state in which they are located. The boundaries for CDPs often are defined in partnership with state, local, and/or tribal officials and usually coincide with visible features or the boundary of an adjacent incorporated place or another legal entity. CDP boundaries often change from one decennial census to the next with changes in the settlement pattern and development; a CDP with the same name as in an earlier census does not necessarily have the same boundary. The only population/housing size requirement for CDPs is that they must contain some housing and population. Processors and tools are using this data. Enhancements Add ISO 3166-3 codes. Simplify geometries to provide better performance across the services. Add administrative hierarchy.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Context

Copy Detection Patterns (CDP) are noisy, black-and-white, maximum entropy image, generated with a secret key. CDPs are designed to be sensitive to countefeit attempts, and have received significant attention from academia and industry as a practical means to facilitate detection of counterfeits. Their security level against sophisticated attacks has been studied theoretically and practically in different research papers, but it is not clear as of today whether it is possible to counterfeit effectively a CDP.

We therefore created this dataset to 1) stimulate research on the security of CDPs, 2) evaluate the security level against different types of copies, and 3) develop enhanced algorithms to improve detection performance (e.g. over the commonly used bit error rate which is often used in the literature).

It was shown in prior arts that the simple duplication using a copy machine is not an effective way to copy a CDP. Therefore, the most promising solution appears to be the estimation of CDP from printed-and-scanned image either by using image processing techniques, or by doing the CDP estimation using a neural network approach.

The second question is “what is the efficient CDP detector?”. Indeed, depending on the specific processing involved,

Content

The digital binary (template) CDPs have size of 52×52 pixels, with 1 pixel per element which is defined at 600 ppi, printed with 600 dpi and scanned with 2400 dpi using printer Canon IR-ADV C5535i. Therefore, the printed and scanned CDPs have the size of 208 × 208 pixels (that corresponds to 4 pixels per element) and are grayscale images. The estimation methods used in this work are the following: 1) Binarization using Otsu thresholding (called Otsu). 2) Unsharp masking followed by binarization using Otsu thresholding (called unsharp+Otsu). 3) Binarization using fully connected neural network with 2 hidden layers (called FC2). 4) Binarization using fully connected neural network with 3 hidden layers (called FC3). 5) Binarization using fully connected neural network with 4 hidden layers (called FC4). 6) Binarization using bottleneck DNN (called BN DNN). 7) Unsharp masking followed by binarization using bottleneck DNN (called unsharp+BN DNN).

The unique_cdp dataset consists of 5000 unique CDPs printed once and then estimated using six attacks (methods 1-6 from the list). It consists of 5000 digital templates and the corresponding 5000 original prints (authentic CDPs), and 4 folders of copies of the last 1500 original CDPs (the first 3500 were used for training the counterfeiting algorithm).

The batch_cdp dataset consists of CDP printed per batch, i.e. each CDP is printed multiple times. This is representative of the application of CDPs with industrial printers such as offset, flexo and rotogravure. This dataset consists of 50 unique CDPs, and each CDP is printed-and-scanned 50 times. That gives us in total 2500 printed and scanned versions of 50 unique CDP. After that we have applied 4 estimation attacks (methods 1, 2, 6 and 7 in the list) in fusion with averaging attack. The folder “fake batch” consists of 4 sub-folders with fakes obtained using estimation methods (1), (2), (6) and (7).

Acknowledgements

This research was presented in article “Can Copy Detection Patterns be copied? Evaluating the performance of attacks and highlighting the role of the detector” published in WIFS 2021. Please cite the corresponding reference while using one of these databases in your academic work. E. Khermaza, I. Tkachenko, J. Picard, “Can Copy Detection Patterns be copied? Evaluating the performance of attacks and highlighting the role of the detector”, WIFS 2021, December 2021, Montpellier, France

Inspiration

These datasets are provided for academic use only with the objective of improving our understanding on the security aspects of CDPs, and can be used to address these questions: • How can we improve the detection performance? How to most efficiently separate the fake (estimated) CDPs from the original CDPs? • Id it possible to generate CDP copies that can be undetectable ?

If you would like to test your own copies, you can try on your printer by printing them at 600ppi. You may also reach out to us so we can print and scan them in comparable conditions as used for this dataset.

Shapefile contains legal names and boundaries of places - incorporated and Census Designated Places (CDPs) - as of January 1, 2022 for the five counties that are included in the Bay Area Regional Climate Action Planning Initiative Frontline Communities Map.

The original shapefile was downloaded from the U.S. Census Bureau, 2022 TIGER/Line Shapefiles webpage. The “Clip” tool in ArcMap was used to select only those features which are located within the boundaries of the five Bay Area counties included in the Frontline Communities Map. No display filters were used to visualize the features in the final map.

The Frontline Communities Map is meant to help identify communities that are considered frontline communities for the purpose of the USEPA’s Climate Pollution Reduction Grant (CPRG) program’s planning effort, which is a five-county climate action planning process led by the Air District. USEPA refers to these communities as low-income and disadvantaged communities (LIDACs).

The strategy and policies for retailing set out in this plan have been prepared having regard to the guidance set out in the ‘Retail Planning Guidelines for planning authorities’ (DoECLG, 2012). This development plan addresses the list of matters to be considered in a plan, as required by ‘Section 3.3 Development Plans and Retailing’ of the Guidelines.