Residential Property Attribute data provides the most current building attributes available for residential properties as captured within Landgate's Valuation Database. Attribute information is captured as part of the Valuation process and is maintained via a range of sources including building and sub division approval notifications. This data set should not be confused with Sales Evidence data which is based on property attributes as at the time of last sale. This dataset has been spatially enabled by linking cadastral land parcel polygons, sourced from Landgatge's Spatial Cadastral Database (SCDB), to the Residential Property Attribute data sourced from the Valuation database. Customers wishing to access this data set should contact Landgate on +61 (0)8 9273 7683 or email businesssolutions@landgate.wa.gov.au © Western Australian Land Information Authority (Landgate). Use of Landgate data is subject to Personal Use License terms and conditions unless otherwise authorised under approved License terms and conditions. Changes will be applied to this dataset resulting from the implementation of the Community Titles Act 2018 please refer to the Data Dictionary below.

Maintaining accurate data is a concern of all GIS users. The geodatabase offers you the ability to create geographic features that represent the real world. As the real world changes, you must update these features and their attributes. When creating or updating data, you can add behavior to your features and other objects to minimize the potential for errors.After completing this course, you will be able to:Define the two types of attribute domains and discuss how they differ.Create attribute domains and use them when editing data.Create subtypes and use them when editing data.Explain the difference between an attribute domain and a subtype.

Including dependent variables: likes, comments, collects, shares; Independent variables: perception of advertising disclosure, proportion of negative reviews, proportion of women, proportion of Gen Z audience, proportion of middle age audience, proportion of middle-aged and elderly audience; And control variables: release days, video duration, price

This data set contains a collection of attributes associated with CloudSat identified echo objects (or contiguous regions of radar/dBZ echo) from15 June 2006 till 17 January 2013. CloudSat is a NASA satellite that carries a 94 GHz (3 mm) nadir pointing cloud profiling radar (CPR). CloudSat makes approximately 14 orbits per day with an equator passing time of 0130 and 1330 local time. Echo objects were identified using CloudSat's 2B-GEOPROF product that includes 2D arrays (alongtrack x vertical) of the radar reflectivity factor and gaseous attenuation correction. Also included in the product is a "cloud mask" with values ranging between 0 and 40 with higher values indicating a greater likelihood of cloud detection. An EO was defined as a contiguous region of cloud mask greater than or eaqual to 20, consisting of at least three pixels with their edges and not merely their corners touching. Each echo object (EO) is assigned multiple attributes. The geographic attributes include minimum, mean, and maximum latitude and longitude, minimum and maximium location along the CloudSat orbit track, and the underlying surface altitude and land mask data, which allows the EOs to be catagorized as occuring over land, sea, or the coast. The geometric attributes include top, mean, and bottom height, width, and the total number of pixels within the EO. Attributes describing the internal structure of the EO are also available including the number of pixels and cells (i.e., group of pixels) greater than 0 dBZ and -17 dBZ. Finally, the time of day of occurance was also recorded to compare the statistics of EOs ocurring during the daytime versus nighttime. In total, we identified 15,181,193 EOs from 15 June 2006 to 17 January 2013. After 17 April 2011, data were only collected during the day due to a battery failure onboard CloudSat. Each attribute is organized as a 1D array where the size of the array corresponds to the number of EOs. This organization allows subsets of EOs to be easily identified using simple "where" statements when writing code. The attributes were used to identify cloud types and analyze global cloud climatology according to season, surface type, and region (i.e., Riley 2009; Riley and Mapes 2009). The varability of EOs across the MJO was also analyzed (Riley et al. 2011). Methods Data:

Raw files were downloaded from ftp1.cloudsat.cira.colostate.edu in directory 2B-GEOPROF.R04 Processed files are in netcdf format

Processing:

Data were processed and analyzed using IDL. See CloudSat_code_README.txt for details The initial processing was done while I was a graduate student at the Univerisity of Miami working on my masters from 2006-2009 Code is available at https://github.com/erileydellaripa/CYGNSS_code

Data file description:

Once the tar.gz file is unpacked, the EO attributes are provided in the EO_masterlistYYYY.nc files, where YYYY corresponds to the different years. I transferred the EO attributes from IDL .save files to netcdf files for sharing. A description of each EO attribute is provide in the README.md and if you do an ncdump -h in a terminal window.

The attributes are organized in 1D arrays, where the element of each array corresponds to a unique EO and the total size of the array corresponds to the total number of EOs identified.

Data are processed from the start of CloudSat 15 June 2006 till 17 January 2013 for the EO attributes.

In total, there are 15,181,193 EOs.

There was a battery failure 17 April 2011. CloudSat resumed collecting data 27 October 2011, but only during the day.

References:

Riley, E. M., B. E. Mapes, and S. N. Tulich, 2011: Clouds Associated with the Madden-Julian Oscillation: A New Perspective from CloudSat. J. Atmos. Sci., 68, 3032-3051, https://doi.org/10.1175/JAS-D-11-030.1.

Riley, E. M., and B. E. Mapes, 2009: Unexpected peak near -15°C in CloudSat echo top climatology. Geophys. Res. Lett., 36, L09819, https://doi.org/10.1029/2009GL037558.

Riley, E. M., 2009: A global survey of clouds by CloudSat. M.S. thesis, Division of Meteorology and Physical Oceanography, University of Miami, 134 pp, https://scholarship.miami.edu/esploro/outputs/991031447848002976.

The multiple attribute mapping process provides a vector based inventory of the landscape in terms of slope, terrain, landuse, vegetation, presence of tree regrowth, tree and shrub canopy density, …Show full descriptionThe multiple attribute mapping process provides a vector based inventory of the landscape in terms of slope, terrain, landuse, vegetation, presence of tree regrowth, tree and shrub canopy density, presence of understorey, soil erosion condition, and rockiness. Mass movement and soil conservation measures are mapped where they exist, as is a selected range of weed species. These characteristics of the land are part of the larger set of characteristics that can be mapped using the NSW Dept. of Land and Water Conservation's full set of attribute codes. This set of codes are termed the Standard Classification for Attributes of Land (SCALD). The value of the attribute mapping is that the data objectively characterises the land and can be used for a range of land uses and land management purposes. This system of mapping maximises the efficiency of GIS operation by describing a number of attributes into one polygon, avoiding problems caused by overlaying of different data sets. Mapping is carried out at 1:25000 scale using base maps from the NSW Land Information Centre medium scale topographic series. Outputs are most useful at the sub-catchment or regional scale but not at property level. The data are extremely valuable at the river basin scale for integrated catchment planning programmes The information can, however, be useful as a first level of information in property planning exercises.

Food purchases differ substantially across countries. We use detailed household level data from the US, France and the UK to (i) document these differences; (ii) estimate a demand system for food and nutrients, and (iii) simulate counterfactual choices if households faced prices and nutritional characteristics from other countries. We find that differences in prices and characteristics are important and can explain some difference (e.g., US-France difference in caloric intake), but generally cannot explain many of the compositional patterns by themselves. Instead, it seems an interaction between the economic environment and differences in preferences is needed to explain cross country differences.

Description

This dataset is the "additional training dataset" for the DCASE 2024 Challenge Task 2.

The data consists of the normal/anomalous operating sounds of nine types of real/toy machines. Each recording is a single-channel audio that includes both a machine's operating sound and environmental noise. The duration of recordings varies from 6 to 10 seconds. The following nine types of real/toy machines are used in this task:

3DPrinter

AirCompressor

BrushlessMotor

HairDryer

HoveringDrone

RoboticArm

Scanner

ToothBrush

ToyCircuit

Overview of the task

Anomalous sound detection (ASD) is the task of identifying whether the sound emitted from a target machine is normal or anomalous. Automatic detection of mechanical failure is an essential technology in the fourth industrial revolution, which involves artificial-intelligence-based factory automation. Prompt detection of machine anomalies by observing sounds is useful for monitoring the condition of machines.

This task is the follow-up from DCASE 2020 Task 2 to DCASE 2023 Task 2. The task this year is to develop an ASD system that meets the following five requirements.

1. Train a model using only normal sound (unsupervised learning scenario) Because anomalies rarely occur and are highly diverse in real-world factories, it can be difficult to collect exhaustive patterns of anomalous sounds. Therefore, the system must detect unknown types of anomalous sounds that are not provided in the training data. This is the same requirement as in the previous tasks.

2. Detect anomalies regardless of domain shifts (domain generalization task) In real-world cases, the operational states of a machine or the environmental noise can change to cause domain shifts. Domain-generalization techniques can be useful for handling domain shifts that occur frequently or are hard-to-notice. In this task, the system is required to use domain-generalization techniques for handling these domain shifts. This requirement is the same as in DCASE 2022 Task 2 and DCASE 2023 Task 2.

3. Train a model for a completely new machine typeFor a completely new machine type, hyperparameters of the trained model cannot be tuned. Therefore, the system should have the ability to train models without additional hyperparameter tuning. This requirement is the same as in DCASE 2023 Task 2.

4. Train a model using a limited number of machines from its machine typeWhile sounds from multiple machines of the same machine type can be used to enhance the detection performance, it is often the case that only a limited number of machines are available for a machine type. In such a case, the system should be able to train models using a few machines from a machine type. This requirement is the same as in DCASE 2023 Task 2.

5 . Train a model both with or without attribute informationWhile additional attribute information can help enhance the detection performance, we cannot always obtain such information. Therefore, the system must work well both when attribute information is available and when it is not.

The last requirement is newly introduced in DCASE 2024 Task2.

Definition

We first define key terms in this task: "machine type," "section," "source domain," "target domain," and "attributes.".

"Machine type" indicates the type of machine, which in the additional training dataset is one of nine: 3D-printer, air compressor, brushless motor, hair dryer, hovering drone, robotic arm, document scanner (scanner), toothbrush, and Toy circuit.

A section is defined as a subset of the dataset for calculating performance metrics.

The source domain is the domain under which most of the training data and some of the test data were recorded, and the target domain is a different set of domains under which some of the training data and some of the test data were recorded. There are differences between the source and target domains in terms of operating speed, machine load, viscosity, heating temperature, type of environmental noise, signal-to-noise ratio, etc.

Attributes are parameters that define states of machines or types of noise. For several machine types, the attributes are hidden.

Dataset

This dataset consists of nine machine types. For each machine type, one section is provided, and the section is a complete set of training data. A set of test data corresponding to this training data will be provided in another seperate zenodo page as an "evaluation dataset" for the DCASE 2024 Challenge task 2. For each section, this dataset provides (i) 990 clips of normal sounds in the source domain for training and (ii) ten clips of normal sounds in the target domain for training. The source/target domain of each sample is provided. Additionally, the attributes of each sample in the training and test data are provided in the file names and attribute csv files.

File names and attribute csv files

File names and attribute csv files provide reference labels for each clip. The given reference labels for each training clip include machine type, section index, normal/anomaly information, and attributes regarding the condition other than normal/anomaly. The machine type is given by the directory name. The section index is given by their respective file names. For the datasets other than the evaluation dataset, the normal/anomaly information and the attributes are given by their respective file names. Note that for machine types that has its attribute information hidden, the attribute information in each file names are only labeled as "noAttributes". Attribute csv files are for easy access to attributes that cause domain shifts. In these files, the file names, name of parameters that cause domain shifts (domain shift parameter, dp), and the value or type of these parameters (domain shift value, dv) are listed. Each row takes the following format:

[filename (string)], [d1p (string)], [d1v (int | float | string)], [d2p], [d2v]...

For machine types that have their attribute information hidden, all columns except the filename column are left blank for each row.

Recording procedure

Normal/anomalous operating sounds of machines and its related equipment are recorded. Anomalous sounds were collected by deliberately damaging target machines. For simplifying the task, we use only the first channel of multi-channel recordings; all recordings are regarded as single-channel recordings of a fixed microphone. We mixed a target machine sound with environmental noise, and only noisy recordings are provided as training/test data. The environmental noise samples were recorded in several real factory environments. We will publish papers on the dataset to explain the details of the recording procedure by the submission deadline.

Directory structure

• /eval_data

  • /raw - /3DPrinter - /train (only normal clips) - /section_00_source_train_normal_0001_.wav - ... - /section_00_source_train_normal_0990_.wav - /section_00_target_train_normal_0001_.wav - ... - /section_00_target_train_normal_0010_.wav - attributes_00.csv (attribute csv for section 00) - /AirCompressor (The other machine types have the same directory structure as 3DPrinter.) - /BrushlessMotor - /HairDryer - /HoveringDrone - /RoboticArm - /Scanner - /ToothBrush - /ToyCircuit

Baseline system

The baseline system is available on the Github repository . The baseline systems provide a simple entry-level approach that gives a reasonable performance in the dataset of Task 2. They are good starting points, especially for entry-level researchers who want to get familiar with the anomalous-sound-detection task.

Condition of use

This dataset was created jointly by Hitachi, Ltd., NTT Corporation and STMicroelectronics and is available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.

Citation

Contact

If there is any problem, please contact us:

Tomoya Nishida, tomoya.nishida.ax@hitachi.com

Keisuke Imoto, keisuke.imoto@ieee.org

Noboru Harada, noboru@ieee.org

Daisuke Niizumi, daisuke.niizumi.dt@hco.ntt.co.jp

Yohei Kawaguchi, yohei.kawaguchi.xk@hitachi.com

The multiple attribute mapping process provides a vector based inventory of the landscape in terms of slope, terrain, landuse, vegetation, presence of tree regrowth, tree and shrub canopy density, …Show full descriptionThe multiple attribute mapping process provides a vector based inventory of the landscape in terms of slope, terrain, landuse, vegetation, presence of tree regrowth, tree and shrub canopy density, presence of understorey, soil erosion condition, and rockiness. Mass movement and soil conservation measures are mapped where they exist, as is a selected range of weed species. These characteristics of the land are part of the larger set of characteristics that can be mapped using the NSW Dept. of Land and Water Conservation's full set of attribute codes. This set of codes are termed the Standard Classification for Attributes of Land (SCALD). The value of the attribute mapping is that the data objectively characterises the land and can be used for a range of land uses and land management purposes. This system of mapping maximises the efficiency of GIS operation by describing a number of attributes into one polygon, avoiding problems caused by overlaying of different data sets. Mapping is carried out at 1:25000 scale using base maps from the NSW Land Information Centre medium scale topographic series. Outputs are most useful at the sub-catchment or regional scale but not at property level. The data are extremely valuable at the river basin scale for integrated catchment planning programmes The information can, however, be useful as a first level of information in property planning exercises.

The mapping process as applied in this dataset provides a vector based inventort of the landscape in terms of landuse, vegetation, presence of tree regrowth, tree and shrub canopy density, presence …Show full descriptionThe mapping process as applied in this dataset provides a vector based inventort of the landscape in terms of landuse, vegetation, presence of tree regrowth, tree and shrub canopy density, presence of understorey and soil erosion condition. Mass movement is mapped where it exists, as is a selected range of weed species in pasture areas. These characteristics of the land are part of the larger set of characteristics that can be mapped using the NSW Dept. of Land and Water Conservation’s full set of attribute codes. This set of codes are termed the Standard Classification for Attributes of Land (SCALD). The value of the attribute mapping is that the data objectively characterises the land and can be used for a range of land uses and land management purposes. This system of mapping maximises the efficiency of GIS operation by describing a number of attributes into one polygon, avoiding problems caused by overlaying go different data sets. The full SCALD programme permits the coding of slope, terrain, land use, vegetation community, vegetation regeneration, tree and shrub canopy density, understorey status, projective foliage cover (McDonald et al. 1990), Western Region vegetation, soil erosion, mass movement, soil conservation earthworks, extent of rock outcrops, geology and Great soil groups., geology, great soil group, soil landscapes, physical limitations, land capability, soil depth, user defined attributes and Northwest vegetation associations. Soil landscapes information from the DLWC mapping program of the same name can be incorporated into the SCALD code set. Mapping is carried out at 1:25000 scale using base maps from the NSW Land Information Centre medium scale topographic series. Outputs are most useful at the sub-catchment or regional scale but not at property level. The data are extremely valuable at the river basin scale for integrated catchment planning programmes.

Data Description: This data set contains all records of payments made to vendors by the City of Cincinnati from fiscal year 2014 to present. It includes information such as the department who paid for the service, the reason for payment, and vendor name.

Data Creation: This data is pulled directly from the City's financial software; which centralizes all department financial transactions city wide.

Data Created By: The Cincinnati Financial System (CFS)

Refresh Frequency: Weekly

Data Dictionary: A data dictionary providing definitions of columns and attributes is available as an attachment to this data set.

Processing: The City of Cincinnati is committed to providing the most granular and accurate data possible. In that pursuit the Office of Performance and Data Analytics facilitates standard processing to most raw data prior to publication. Processing includes but is not limited: address verification, geocoding, decoding attributes, and addition of administrative areas (i.e. Census, neighborhoods, police districts, etc.).

Data Usage: For directions on downloading and using open data please visit our How-to Guide: https://data.cincinnati-oh.gov/dataset/Open-Data-How-To-Guide/gdr9-g3ad

Data Set Information: * The articles were published by Mashable (www.mashable.com) and their content as the rights to reproduce it belongs to them. Hence, this dataset does not share the original content but some statistics associated with it. The original content be publicly accessed and retrieved using the provided urls. * Acquisition date: January 8, 2015 * The estimated relative performance values were estimated by the authors using a Random Forest classifier and a rolling windows as assessment method. See their article for more details on how the relative performance values were set. ##Attribute Information: Number of Attributes: 61 (58 predictive attributes, 2 non-predictive, 1 goal field) 0. url: URL of the article (non-predictive) 1. timedelta: Days between the article publication and the dataset acquisition (non-predictive) 2. n_tokens_title: Number of words in the title 3. n_tokens_content: Number of words in the content 4. n_unique_tokens: Rate of unique words in the conte

Fiscal Service business messages (schemas) define the structure of XML documents, specify data types for attribute values and data element content that allow machines to carry out rules made by the Bureau. XML Schema - A specification to define the structure of XML documents and to specify data types for attribute values and data element content.

The Task: The challenge will use an extension of the UPAR Dataset [1], which consists of images of pedestrians annotated for 40 binary attributes. For deployment and long-term use of machine-learning algorithms in a surveillance context, the algorithms must be robust to domain gaps that occur when the environment changes. This challenge aims to spotlight the problem of domain gaps in a real-world surveillance context and highlight the challenges and limitations of existing methods to provide a direction for future research.

The Dataset: We will use an extension of the UPAR dataset [1]. The challenge dataset consists of the harmonization of three public datasets (PA100K [2], PETA [3], and Market1501-Attributes [4]) and a private test set. 40 binary attributes have been unified between those for which we provide additional annotations. This dataset enables the investigation of PAR methods' generalization ability under different attribute distributions, viewpoints, varying illumination, and low resolution.

The Tracks: This challenge is split into two tracks associated with semantic pedestrian attributes, such as gender or clothing information: Pedestrian Attribute Recognition (PAR) and attribute-based person retrieval. Both tracks build on the same data sources but will have different evaluation criteria. There are three different dataset splits for both tracks that use different training domains. Each track evaluates how robust a given method is to domain shifts by training on limited data from a specific limited domain and evaluating using data from unseen domains.

Track 1: Pedestrian Attribute Recognition: The task is to train an attribute classifier that accurately predicts persons’ semantic attributes, such as age or clothing information, under domain shifts. Track 2: Attribute-based Person Retrieval: Attribute-based person retrieval aims to find persons in a huge database of images called gallery that match a specific attribute description. The goal of this track is to develop an approach that takes binary attribute queries and gallery images as input and ranks the images according to their similarity to the query. The Phases: Each track will be composed of two phases, i.e., the development and test phases. During the development phase, public training data will be released, and participants must submit their predictions concerning a validation set. At the test (final) phase, participants will need to submit their results for the test data, which will be released just a few days before the end of the challenge. As we progress into the test phase, validation annotations will become available together with the test images for the final submission. At the end of the challenge, participants will be ranked using the public test data and additional data that is kept private. It is important to note that this competition involves submitting results and code. Therefore, participants will be required to share their code and trained models after the end of the challenge (with detailed instructions) so that the organizers can reproduce the results submitted at the test phase in a code verification stage. Verified code will be applied to a private test dataset for final ranking. The organizers will evaluate the top submissions on the public leaderboard on the private test set to determine the 3 top winners of the challenge. At the end of the challenge, top-ranked methods that pass the code verification stage will be considered valid submissions and compete for any prize that may be offered.

The degree of significance of all attributes from Suraj’s LEMS Data Set.

Analysis of ‘ Student Performance Data Set’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/larsen0966/student-performance-data-set on 28 January 2022.

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

If this Data Set is useful, and upvote is appreciated. This data approach student achievement in secondary education of two Portuguese schools. The data attributes include student grades, demographic, social and school related features) and it was collected by using school reports and questionnaires. Two datasets are provided regarding the performance in two distinct subjects: Mathematics (mat) and Portuguese language (por). In [Cortez and Silva, 2008], the two datasets were modeled under binary/five-level classification and regression tasks. Important note: the target attribute G3 has a strong correlation with attributes G2 and G1. This occurs because G3 is the final year grade (issued at the 3rd period), while G1 and G2 correspond to the 1st and 2nd-period grades. It is more difficult to predict G3 without G2 and G1, but such prediction is much more useful (see paper source for more details).

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

The 2015 LU/LC data set is the sixth in a series of land use mapping efforts that was begun in 1986. Revisions and additions to the initial baseline layer were done in subsequent years from imagery captured in 1995/97, 2002, 2007, 2012 and 2015. This present 2015 update was created by comparing the 2012 LU/LC layer from NJDEP's Geographic Information Systems (GIS) database to 2015 color infrared (CIR) imagery and delineating and coding areas of change. Work for this data set was done by Aerial Information Systems, Inc., Redlands, CA, under direction of the New Jersey Department of Environmental Protection (NJDEP), Bureau of Geographic Information System (BGIS). LU/LC changes were captured by adding new line work and attribute data for the 2015 land use directly to the base data layer. All 2012 LU/LC polygons and attribute fields remain in this data set, so change analysis for the period 2012-2015 can be undertaken from this one layer. The classification system used was a modified Anderson et al., classification system. An impervious surface (IS) code was also assigned to each LU/LC polygon based on the percentage of impervious surface within each polygon as of 2015. Minimum mapping unit (MMU) is 1 acre. ADVISORY: This metadata file contains information for the 2015 Land Use/Land Cover (LU/LC) data sets, which were mapped by USGS Subbasin (HU8). There are additional reference documents listed in this file under Supplemental Information which should also be examined by users of these data sets. As stated in this metadata record's Use Constraints section, NJDEP makes no representations of any kind, including, but not limited to, the warranties of merchantability or fitness for a particular use, nor are any such warranties to be implied with respect to the digital data layers furnished hereunder. NJDEP assumes no responsibility to maintain them in any manner or form. By downloading this data, user agrees to the data use constraints listed within this metadata record.

From the site: "This data set includes all soil map units that are defined as Hydric in the SSURGO data base.The SSURGO data set is a digital soil survey and is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was collected by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. The SSURGO data set consists of georeferenced digital map data and computerized attribute data. The map data are in a full county format and include a detailed, field verified inventory of soils and nonsoil areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. Sometimes a special soil features layer (point and line features) is included. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the Map Unit Interpretations Record relational data base, which gives the proportionate extent of the component soils and their properties. | The data set has been provided to Chester County Departments and PASDA as an ArcView shapefile by the County of Chester, Department of Computer and Information Services. The theme has been reprojected to PA Stateplane (South) NAD83 from its original datum in accordance with the base map standards of the County of Chester. The County of Chester serves as the secondary organization in providing this shapefile, as compared to its originator and primary organization, the Natural Resources Conservation Service."

Group members gathered.

This data set was generated through the 2020 LU/LC update mapping effort. The 2020 update is the seventh in a series of land use mapping efforts that was begun in 1986. Revisions and additions to the initial baseline layer were done in subsequent years from imagery captured in 1995/97, 2002, 2007, 2012, 2015 and now, 2020. This present 2020 update was created by comparing the 2015 LU/LC layer from NJDEP's Geographic Information Systems (GIS) database to 2020 color infrared (CIR) imagery and delineating and coding areas of change. Work for this data set was done by Aerial Information Systems, Inc., Redlands, CA, under direction of the New Jersey Department of Environmental Protection (NJDEP), Bureau of Geographic Information System (BGIS). LU/LC changes were captured by adding new line work and attribute data for the 2020 land use directly to the base data layer. All 2015 LU/LC polygons and 2015 LU/LC coding remains in this data set, so change analysis for the period 2015-2020 can be undertaken from this one layer. The mapping was done by USGS HUC8 basins, 13 of which cover portions of New Jersey. This statewide layer is composed of the final data sets generated for each HUC8 basin. Initial QA/QC was done on each HUC8 data set as it was produced with final QA/QC and basin-to-basin edgematching done on this statewide layer. The classification system used was a modified Anderson et al., classification system. Minimum mapping unit (MMU) is 1 acre for changes to non-water and non-wetland polygons. Changes to these two categories were mapped using .25 acres as the MMU. (See entry under the Advisory section concerning additional review being done on NHD waterbody attribute coding and impervious surface estimation.) ADVISORY This data set, edition 20231120, is a statewide layer that includes updated land use/land cover data for all HUC8 basins in New Jersey. The polygon delineations and associated land use code assignments are considered the final versions for this mapping effort. Note, however, that there is continuing review being done on this layer to update several additional attributes not presently evaluated in this edition. These attributes include several from the National Hydrography Database (NHD) that are specific to the waterbodies mapped in this layer, and several attributes containing impervious surface estimates for each polygon. Evaluating the NHD codes facilitates extracting the water features mapped in this layer and using them to update the New Jersey portion of the NHD. Those NHD specific attributes are still being evaluated and may be added to a future edition of this base data set. Similarly, additional review is being done to assess the feasibility of incorporating data on impervious surface (IS) amounts generated from two independent projects, one of which was just completed by NOAA, into this base land use layer. While the NHD and IS attributes will enhance the use of this base layer in several types of analyses, this present layer can be used for doing all primary land use analyses without having those attributes evaluated. Further, evaluating these extra attributes will result in few, if any, changes to the polygon delineations and standard land use coding that are the primary features of this layer. As such, the layer is being provided in its present edition for general use. As the additional attributes are evaluated, they may be added to a future edition of this data set. The basic land use features and codes, however, as mapped in this version of the data set will serve as the base 2020 LU/LC update. As stated in this metadata record's Use Constraints section, NJDEP makes no representations of any kind, including, but not limited to, the warranties of merchantability or fitness for a particular use, nor are any such warranties to be implied with respect to the digital data layers furnished hereunder. NJDEP assumes no responsibility to maintain them in any manner or form. By downloading this data, user agrees to the data use constraints listed within this metadata record.The data for Somerset County data was extracted & processed from the latest dataset by the Somerset County Office of GIS Services (SCOGIS).

The multiple attribute mapping process provides a vector based inventory of the landscape in\r terms of slope, terrain, landuse, vegetation, presence of tree regrowth, tree and shrub canopy\r density, presence of understorey, soil erosion condition, and rockiness. Mass movement and\r soil conservation measures are mapped where they exist, as is a selected range of weed\r species. These characteristics of the land are part of the larger set of characteristics that can\r be mapped using the NSW Dept. of Land and Water Conservation's full set of attribute codes.\r This set of codes are termed the Standard Classification for Attributes of Land (SCALD). The\r value of the attribute mapping is that the data objectively characterises the land and can be\r used for a range of land uses and land management purposes. This system of mapping\r maximises the efficiency of GIS operation by describing a number of attributes into one\r polygon, avoiding problems caused by overlaying of different data sets.\r Mapping is carried out at 1:25000 scale using base maps from the NSW Land Information\r Centre medium scale topographic series. Outputs are most useful at the sub-catchment or\r regional scale but not at property level. The data are extremely valuable at the river basin scale\r for integrated catchment planning programmes The information can, however, be useful as a\r first level of information in property planning exercises.