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Creating Stream Order

Creating Stream Order


All of cell's values are "1" into stream network. Later, stream link seperates the segments using stream order tool. And, each stream segment has different grid codes. How do Stream Order discriminate the pixels of the same value "1"? If it considers the junction between links, how is it getting the value?


I gave a fairly detailed description of how these types of stream network operations work in my previous answer here. But if you're looking for the exact algorithmic solution for how stream cells are recognized in the stream link raster and how confluences (junctions) are spotted in the network then you may look at the following source code as examples:

https://code.google.com/p/whitebox-geospatial-analysis-tools/source/browse/trunk/StreamNetworkAnalysisTools/src/plugins/StreamOrder.java

https://code.google.com/p/whitebox-geospatial-analysis-tools/source/browse/trunk/StreamNetworkAnalysisTools/src/plugins/StreamLinkID.java

The first is a stream ordering tool and the second is a stream link tool. All of these tools work in a similar fashion. You'll notice that stream cells are identified as being any grid cell with a value greater than zero (if (streams.getValue(row, col) > 0)) and that confluence cells are those where the number of inflowing stream cells are greater than one ( if (numNeighbouringStreamCells > 1)). The number of inflowing stream cells is determined using the combination of the flow direction grid values and the stream network grid values. Basically the algorithms work by tracing a flow path starting at a stream head cell downstream until it finds a junction cell. Then a new value is assigned to the newly encountered stream segment.


Mapping the Future of GIS

When Hurricane Irma hit, responders reached for interactive maps—to see and share a real-time picture, to record storm impacts, to direct responders to those in need, and to mark progress on recovery.

When food-borne illness spreads, growers who map their supply chain from the field to the checkout line can pinpoint the precise part of the field that caused the problem.

When increasing demands for higher speed internet service requires the extension of fiber-optic cables to homes, telecommunication companies use a map to prioritize construction, communicate with crews, and market to consumers.

In business or government, when operations require an ‘awhereness’ of quickly changing conditions, today’s smart maps empower quick and authoritative communications.

Growing Coordination

Since ancient times, maps have provided the means to capture knowledge and share information with others in a simple and easily comprehensible way. Maps capture reality using a visual language that communicates understanding, shares perspectives, and encourages participation.

Maps also form the basic unit of information output from a geographic information system (GIS)—providing the canvas for GIS data to be shaped and displayed. Technical advancements and new capabilities of GIS manifest themselves in the way we author and use maps, and how we apply them to problem solving.

In business, maps create competitive advantage, drive growth, and improve efficiency:

  • A nationwide real estate company maps changing markets to help its clients quickly seize opportunities in target areas.
  • A global fast food chain decides where to introduce new products by mapping customer segments and their preferences.
  • A technology manufacturer eliminates overlaps in its sales channels by mapping consumer demand against retail availability.

In government, maps help cities strengthen operational efficiency, transparency, and constituent engagement:

  • A large city police department uses maps to coordinate response during large events, such as road races, combining the map with sensor inputs for real-time analytics that fuel a quick response.
  • National public health officials use and share maps that track the spread of influenza.
  • A mid-sized US city used a map and a detailed city model to energize a community about redevelopment plans, leading to economic revitalization.

In infrastructure-heavy organizations, smart maps help boost operations and productivity:

  • One of the world’s busiest ports uses a map to orchestrate the flow of commerce.
  • A global oil and gas conglomerate has centralized its map-powered views across all operations—exploration, drilling, and distribution.
  • A state transportation department mobilized an ambitious statewide infrastructure improvement project to tackle bottlenecks and fuel economic development.

Underpinning Advancements

In the same way that video games have progressed in their ability to transport us to imagined worlds due to technology advancements, GIS has progressed in its ability to guide our geographic understanding. Many technologies that underpin GIS have taken leaps forward, leading to dramatic improvements in the speed and capabilities of what and how we model and map.

  • Data management: In this era of big data, our ability to map increasing volumes of data reveals patterns, trends, and relationships in ways that reports can’t.
  • Analytical tools: With the trend toward data science, more sophisticated tools enable spatial analytics across an organization using the common denominator of location.
  • Sensor inputs: With the Internet of Things and an exponential expansion of sensors, alongside the ubiquity of drones and satellite-based Earth observation, geographic data has become much more readily available, leading to live content from sensors and measurement systems from across a city, country, or even the globe.
  • Computing power: The rise of distributed computing and the Cloud has led to increasingly flexible GIS systems that can quickly scale both data storage capacity and processing power to answer questions that were previously insurmountable.
  • Data collection: With the ubiquity of smartphones and tablets and increasing mobility, field workers can collect data and visualize it with ease and clarity, creating a quickly shared common view for everyone in the field and office.
  • Automation: Advancements in machine learning and artificial intelligence have greatly accelerated analysis of even massive volumes of imagery.

With this framework of technological advancements, GIS users are analyzing larger volumes of data, adding more frequent inputs from sensors, and gaining insight into how the world works. It is not an overstatement to say that GIS now underwrites the discovery and delivery of a new level of understanding about cities, businesses, organizations, and even complex earth systems.

Simple to Complex

At the start, GIS cataloged and mapped what was where. Today’s GIS-powered smart maps provide dynamic displays of information, create ecosystems for interactions, aid real-time awareness and help plan and create the future.

Instead of static displays of information, GIS allows users to amass data about a place and to dig deep to unlock information about people, nature, the built environment, as well as interactions and impacts. Data stores are readily available to detail businesses, profile people, assess ecosystems, and unlock the flow of things—including transportation, waterways, and even commerce. GIS provides the means to tap geographic knowledge and build evidence to support decision making.

Maps in Hands

Maps only have power when we put them in the hands of people. As GIS has moved to mobile devices and online, mapping apps have exponentially increased the number of people who use maps. Apps provide the means for personal exploration and input, and the data created within apps feed dashboard views that provide a single comprehensive view for decision makers to monitor interactions, events, and assess daily operations.

As those mapping disasters understand, today’s maps change the way we communicate and collaborate. First responders have transitioned from a series of paper maps to interactive map-based solutions and that has greatly elevated their importance. Today, it’s common for all of the screens in an emergency operation center to display some aspect of the ongoing response via a map, and for every responder to have a map-based app open to record what they see and do. All organizations can benefit from a similar approach for critical tasks—sharing individual perspectives and seeing the work of others—to act in unison to resolve the problem or crisis at hand.

Maps are becoming increasingly dynamic with more inputs from sensors and individuals, leading to improved real-time understanding. Businesses will benefit from empowered field workers that can make quick evidence-based decisions out in the field to improve outcomes thanks to the increased fluidity of information—decisions at the edge. Cities that wrap initiatives around maps and apps provide a common place to inform citizens while also capturing their feedback—increasing constituent engagement. Infrastructure organizations that deploy apps tied to detailed 3D models improve workflows and help eliminate costly delays that result from poor coordination—filling in the gaps.

While spatial analytics provide the next-level of map-based exploration, Dr. Michael F. Goodchild, a noted professor of geography, provides a good summary of why that exploration always starts with a map:

“…analysis does not have to involve complex mathematical operations but begins in the human mind as soon as the map is in view, because the eye and brain are enormously efficient at detecting patterns and finding anomalies in maps and other visual displays.

“GIS works best when the computer and the brain combine forces, and when GIS is used to augment human intuition by manipulating and displaying data in ways that reveal things that would otherwise be invisible.”

Learn how smart mapping makes mapping easier and more impactful.


GIS Applications for Socio-Economics and Humanity

Abstract

The main social problem associated with the Internal Armed Conflict (IAC) in Colombia is Internal Forced Displacement (IFD), a feature with few spatial economic analyses. The Colombian case is highly relevant for this type of research because (a) the country’s geographic and environmental diversity has determined its development path (Safford and Palacios, 2002) and the unfolding of the IAC itself (Rangel, 1998), and (b) Colombia has the largest number of forcefully displaced people in the world. This article introduces a theoretical framework of the relationship between municipal development and IFD, and tests it in a 2000–10 semipanel for 1042 municipalities under two different sets of spatial controls: spatial econometrics and topography controls.


Epidemiological Research

GIS is a powerful tool when it comes to tracking the spread of infectious diseases such as Ebola and Measles.

The World Health Organisation (WHO) has prepared Ebola situation reports which contain Ebola distribution maps in Guinea, Liberia, and Sierra Leone. With such visualizations it is now easier to make preventive and prescriptive measures for this regions of Africa.

Application of geographical information systems (GIS) in public health may include spatial mapping — a useful tool for displaying epidemiological data. Mapping is commonly applied to larger geographic areas, such as a region or county. Presenting the data at such a high level may not allow program managers to fully understand available data. Using GIS techniques at a more local level allows for multiple approaches to interpreting data. To understand local epidemics, CDC Kenya is using spatial data and mapping of HIV disease in smaller geographic regions (sub-county, facility or even household location) to identify trends over time and possible underlying risk factors for contracting HIV.CDC Kenya epidemiologists used program data, such as the number of children living with HIV and the number of pre-natal care services delivered to show changes in mother-to-child transmission rates over time and space. The results showed that from 2007–2013 there was a reduction in mother-to-child transmission of HIV from 20.1% to 8.3%.


Geography-Anthropology

The Bachelor of Arts in Geography-Anthropology fuses Geography’s and Anthropology’s common interests in both applied field work and in the relationship between human populations and their environments, both natural and built. The combined program explores global issues through community engagement. Students learn the methodologies and “ways of knowing” of each discipline and integrate them in an interdisciplinary framework to foster their appreciation of their humanistic and scientific responsibilities as global citizens.

Our long history of and strong commitment to environmental and social knowledge, applied learning, and experiential education positions our students well for internships, graduate programs, and the workforce. Undergraduates are involved in our community-engaged teaching and research activities. The major is an interdisciplinary degree program. Students enrolled in the major may specialize in one of three tracks:

  • Sustainable Cultures and Communities
  • Cultural and Natural Heritage Management
  • Applied Geographic Information Systems (GIS) and Geospatial Analysis

Upon graduation, students find employment in fields that involve archaeology work, cultural resource management, historic preservation, heritage and conservation management, museum, curation and archival work, education, environmental and land use management, community development work, non-profit advocacy, city and regional public service, tourism and recreation, and Geographic Information Systems (GIS) related careers in federal, state, local government, and private sector industries.

We focus on developing strong analytical, writing, oral and technical skills and prepare our students to enter the workforce or for future graduate work. Courses emphasize both conceptual and applied learning. Students engage in case studies, community-engaged and client-based projects, and intensive field and lab analysis.

Students have the opportunity to design, develop, research and communicate professional level projects with faculty mentors. As a result of data collection in the field or analysis in our specialized learning laboratories, students also have the opportunity to engage in scholarship through publications and conference presentations, and often receive assistantships and fellowships funded by such organizations as the National Science Foundation, NASA, and the Maine Space Grant Consortium.

The program of study beyond the basic requirements should be planned carefully, in close consultation with the student's program advisor, and should be approved by the latter. Such an arrangement allows for flexibility according to the student's interests while also providing close guidance and a control of educational quality by the Geography-Anthropology program.

Applied Geographic Information Systems (GIS) & Geospatial Analysis track is for those interested in developing theoretical and conceptual knowledge, and analytical and technical skills in GIS, remote sensing and geospatial analysis and engaged in workforce fields in federal, state and local government, and private sector industries including construction, engineering, energy, environmental & land use planning, utilities & transportation, real estate development and surveying.

Program Requirements

All students with majors or specific discipline minors in the social sciences must achieve at least six credits with grades of B or better in the requirements of those majors or minors. No grades of D will be counted toward fulfillment of the major or minor requirements. Except for Independent Studies, no required course may be repeated more than one time.

All students are reminded that, in addition to meeting departmental requirements for the major, they must also meet the University's minimum readiness requirements and the Core curriculum requirements.

The minimum number of credits (exclusive of the University's Core curriculum) required for this track is 39 credits.

Students who specialize in Applied GIS and Geospatial Analysis track must take:

  • GEO 105 or ANT 105 Society, Environment, and Change
  • GEO 107 Maps and Math or
    GEO 270 Mapping Environments and People: Data Visualization and Analysis
  • GEO 370 Maps, Territory, Power
  • MAT 120 Introduction to Statistics

Methods (12 credits):

  • GEO 305/GEO 505/GEO 605 Remote Sensing
  • GEO 308/GEO 508/GEO 608 GIS Applications I
  • GEO 408/GEO 518/GEO 618 GIS Applications II
  • GEO 340/GEO 540/GEO 640 Digital Mapping

Topical Electives (Select courses from the following list to total at least 12 credits. Select at least three credits from each of the groups below and at least 6 credits at or above 300 level):

  • BUS 301 Business Analytics
  • BUS 345 Information Technology/Management Information Systems
  • BUS 377 Information Visualization
  • COS 160 Structured Problem Solving: Java (3 cr.) and COS 170 Structured Programming Laboratory (1 cr.)
  • COS 184 Python Programming
  • COS 246 Programming Topics: Programming Handhold Devices
  • COS 246 Programming Topics: Web Mapping Systems
  • COS 375 Web Applications Development
  • COS 457 Database Systems
  • LOS 318/LAC 318 Database Management
  • ANT 204 Gulf of Maine: Archaeology, Ecology, and Environmental Change
  • ANT 306 Analysis of Archaeological Materials
  • ANT 308/ANT 508 Environmental Archaeology
  • ANT 315/ANT 515 Ethnography: Methods, Ethics, and Practice
  • ANT 360/ANT 560 Public Archaeology
  • GEO 203 Urban & Regional Development
  • GEO 204 Coastal and Marine Geography
  • GEO 209 Introduction to Land Use Planning
  • GEO 210 Planning Maine Communities: Current Issues & Directions
  • GEO 303/GEO 503 Economic Geography
  • GEO 304/504 Arctic and North Atlantic Regions
  • GEO 445/GEO 545/GEO 645 Drone Mapping
  • GEO 455/GEO 555 Gender, Race, and Class in the City
  • GEO 481/GEO 581 Megacities and Global Planning Issues
  • ESP 285 Global Environmental Issues and Sustainability

Capstone (3 credits minimum):

Any one of the following will count toward the capstone requirement:

  • GEO 438/GEO 538/GEO 638 Independent Study in GIS
  • GEO 448/GEO 548/GEO 648 GIS Internship
  • GEO 458/GEO 558/GEO 658 Research Applications in GIS
  • GYA 300 Archaeology Field School
  • GYA 400 Independent Study in Anthropology or Geography, summer travel course, Study Abroad, or existing capstones.

Additionally, all Geography-Anthropology majors are required to demonstrate writing competence by completing either two research papers or one research paper and one research product (e.g., poster, media project) in the major with grades of C or better, from two different professors, at least one semester prior to graduation.

The maximum number of credits of internships, field experience, and/or independent studies that can be applied toward the major is nine hours. All students must meet with their advisors before registering for courses each semester.

Admission Information

For the best overall Financial Aid package, submit your USM application and complete your FAFSA by January 15. However, you can apply at any time. Undergraduate applications are reviewed on a rolling basis.


Geographical Information Systems (GIS) Mapping

Geographic Information System or GIS is technology that offers a radically different way in which we produce and use the maps required to manage our communities and industries. GIS creates intelligent super maps through which sophisticated planning and analysis can be performed at the touch of a button.

GIS Mapping Services

"SIC's Expertise Prevents Potential Problems. "

Satellite Imaging Corporation (SIC) combines orthorectified satellite imagery with extracted vector data and client-supplied geospatial data to create single, GIS data-rich maps for various industry applications including agriculture, disaster management, energy and environmental monitoring. SIC incorporates GIS data to achieve a multi-layered result for many types of analysis and management pertaining to your project.The expertise and accuracy of our GIS mapping services precludes nearly all potential problems associated with GIS maps.

By utilizing machine learning, neural networks algorithms and satellite remote sensing techniques, automated extraction of objects detected on satellite imagery can expedite and reduce the cost of e.g. monitoring and counting wildlife, detection of solar panel arrays on commercial and residential buildings, detection of objects similar in shape or materials or other suitable applications, such as precision agriculture mapping etc.

QuickBird (0.6m) Satellite Image

(Image Copyright © DigitalGlobe and GIS Data by Satellite Imaging Corporation)

Our GIS mapping services allows us to capture, store, manipulate and analyze geospatial data to combine database, mapping and statistical methods to integrate georeferenced data for data collection, processing, and management ensuring accurate solutions that allow you to overcome tough challenges pertaining to your project. Our focus is to deliver accurate quality geospatial products to help manage your mapping goals.

Some projects are hampered by coordinate problems of different satellite image and vector data layers, which are caused by one or a combination of the following:

  • Improper orthorectification of satellite imagery or aerial photography
  • Use of different survey datums and/or geodetic parameters
  • Poor quality of GPS derived ground control points (GCPs)
  • Improper rectification of digital source raster maps
  • Importation of vector data or shape files for source data with incorrect coordinates
  • Improper use of units or unit convergence factors for source data
  • Utilization of source data from a corrupt coordinate database

Our team is committed to quality control and provides continuing geodesy, mapping, ArcGIS 2D and 3D geospatial support services to our clients. We ensure that the geospatial data sets provided to our clients are utilized in only the most effective manner. On every GIS project, whether it is large or small, we implement ongoing data quality control to ensure that coordinate databases, foreign source data such as geological and topographic maps, GIS data attributes and layers create a truly seamless GIS mapping environment.

We use our in-house knowledge in geodesy, land and hydrographic surveying, 2D/3D digital mapping, GIS, and satellite remote sensing applications to provide the most professional services and products to our clients.

One of the primary services provided by SIC during the implementation of a GIS project is the georeferencing of various GIS data layers for mapping projection. SIC has developed comprehensive policy and procedures to include QA and QC in the planning stage of every project involving the use of satellite image data for geographical information systems mapping including:

For more information or for a consultation on any of our products or services, please contact us.


Creating Stream Order - Geographic Information Systems

NOTE: We’re currently transitioning our downloadable GIS data to our new Open Data webpage . Please visit it to download our most up to date data. Any data not available on our Open Data page can be found below.

Statewide Layers

Some older data files may not have a NJ State Plane feet projection file. If the zip file does not contain a .prj file, please download the njsp83.prj file and rename it to the filename in the zip. For example, the Head of Tide shapefile is called hot.shp. In this case, please download, unzip, and rename the njsp83.prj file to hot.prj.

These files can be used with your GIS or can be viewed with a free GIS Data Viewer from ESRI called ArcGIS Explorer.

Ambient Air Quality Monitors are strategically located stations throughout the state of New Jersey and collect and analyze certain air pollutant data.

Ambient Lakes Monitoring Network - lakesmon.zip (20 KB, 64 KB unzipped)

This data was created from the 95/97 LULC coverage created by the NJDEP. Natural and artificial lakes were selected from the coverage and then dissolved. Name attributes from the USGS coverage (lake) were attached via a centroid coverage. Other sources of lake names include non-digital lake monitoring data, atlases, digital datasets and a NJ dams coverage.

Ambient Stream Quality Monitoring Sites - swpts.zip (.05 MB, .25 MB unzipped)

This dataset is a GIS layer of points representing ambient stream sites monitored cooperatively by the New Jersey Department of Environmental Protection (NJDEP) and the US Geological Survey (USGS) for water quality parameters. It includes fields identifying the type of station for each monitoring year as well as the presence and type of water level gauge, the associated land use, the method of locating the points and the availability of flow data.

AMNET Reference Monitoring Sites (2000) - amnetref.zip (10 KB, 82 KB unzipped)

This GIS data layer is a dataset of points representing reference sites for the AMNET project at NJDEP. The NJDEP AMNET database supplied the list of sites (ecoregion table). The locations were selected because they were minimally impacted, had sampling data for 4 seasons, and provided a good point of comparison for other sites.

Aquatic Pesticides 2007 - aquapest.zip (0.13 MB, 2.64 MB unzipped)

The 2007 Aquatic Pesticides Sites List for New Jersey are those sites that were permitted through the NJDEP Pesticide Control Program for aquatic pesticide use in the waterbodies of the state. Site location names are provided by pesticide applicators, and may differ from state/county/municipality recognized names. Point locations do not indicate area of pesticide usage, only indicate the waterbody/aquatic site where treatment was permitted. Sites with multiple waterbodies under one permit have just one point location, usually at the waterbody closest to the outlet/receiving water source. There were a total of 1108 permit applications submitted to the department in 2007, however only 1080 sites are included in this dataset. Thirteen applications were withdrawn or denied, 11 were special local needs permits for natural bottom swimming pools, and 4 permits were for right-of-way maintenance and could not be expressed by a single point. These sites were intentionally removed. If there are questions about specific sites please refer to listed responsible NJDEP contacts. This data was compiled and manipulated for there is a general interest in the trends of aquatic pesticide use for the control of aquatic weeds throughout the state of New Jersey. The information is intended to be a starting point for those individuals interested in the aquatic pesticide usage in their area or at a specific site.

Brownfield Development Areas (Extents) - Envr_mon_soil_brownfield_ext.zip (.37 MB, 1.69 MB unzipped)

The data included in the layer enables GIS to map, as polygons, the extents of all current Brownfield Development Areas (BDAs) in New Jersey. A brownfield is any former or current commercial or industrial site that is currently vacant or underutilized and on which there has been, or there is suspected to have been, a discharge of contamination.

Brownfield Development Areas (Outline) - Envr_mon_soil_brownfield_out.zip (.007 MB, .18 MB unzipped)

This is a graphical representation of the outline boundary for Brownfield Development Areas (BDA) in New Jersey. A brownfield is any former or current commercial or industrial site that is currently vacant or underutilized and on which there has been, or there is suspected to have been, a discharge of contamination.

CAFRA Boundary (line) - cafra2.zip (58 KB, 163 KB unzipped)

The Coastal Area Facilities Review Act (CAFRA) legislates land use within the coastal area. Thus the boundary of the area was delineated. This shape file, CAFRA_2 has been modified by the Pinelands commission and updates the boundary (from the Bureau of Tidelands 1988 boundary) to reflect 1993 modifications to CAFRA.

CAFRA Boundary (poly) - cafra.zip (1922 KB, 2368 KB unzipped)

This data set is a graphical representation of the Coastal Areas Facilities Review Act (CAFRA) boundary, which legislates land use within the coastal area. This boundary was dissolved from the Coastal Planning Areas in New Jersey data set.

Census Blocks (2010) - Govt_census_block_2010.zip (99.0 MB, 385.0 MB unzipped)

This data contains Census 2010 TIGER information at the Block Level for New Jersey. It was obtained from the U.S. Census Bureau's web site along with the Census 2010 Summary File 1 (SF1). The SF1 file was joined to the Census Block coverage by NJDEP.

Census Block Groups (2010) - Govt_census_group_2010.zip (9.61 MB, 14.1 MB unzipped)

This data contains Census 2010 TIGER information at the Block Group Level for New Jersey. It was obtained from the U.S. Census Bureau's web site. Census 2010 Block Groups was derived from the Redistricting Census 2010 TIGER/Line files.

Census Tracts (2010) - Govt_census_tract_2010.zip (5.76 MB, 10.7 MB unzipped)

This data contains Race, Gender, Housing and Household Income (SF1 Tables) information extracted from the Census 2010 TIGER tables at the Tract Level for New Jersey. The data was obtained from the NJ Department of Labor and the U.S. Census Bureau's web site. Census 2010 Tracts was derived from the Redistricting Census 2010 TIGER/Line files.

Census Blocks (2000) - cenblk2000.zip (34.1 MB, 101.7 MB unzipped)

This data contains Census 2000 TIGER information at the Block Level for New Jersey. It was obtained from the U.S. Census Bureau's web site along with the Census 2000 Summary File 1 (SF1). The SF1 file was joined to the Census Block coverage by NJDEP.

Census Block Groups (2000) - cengrp2000.zip (4.4 MB, 6.3 MB unzipped)

This data contains Census 2000 TIGER information at the Block Group Level for New Jersey. It was obtained from the U.S. Census Bureau's web site. Census 2000 Block Groups was derived from the Redistricting Census 2000 TIGER/Line files.

Census Tracts (2000) - centrt2000.zip (2904 KB, 6550 KB unzipped)

This data contains Race, Gender, Housing and Household Income (SF1 Tables) information extracted from the Census 2000 TIGER tables at the Tract Level for New Jersey. The data was obtained from the NJ Department of Labor and the U.S. Census Bureau's web site. Census 2000 Tracts was derived from the Redistricting Census 2000 TIGER/Line files.

Included are those sites within New Jersey where chromate contamination of soil or ground water has been identified This list of chromate waste sites include sites where remediation is either currently under way, required but not yet initiated or has been completed. The data included here dates from 1995.

Coastal Flooding (100 year) - hihaz.zip (1025 KB, 1387 KB unzipped)

This dataset is a graphical representation of high hazard lines in 100-year flood areas of coastal New Jersey to help protect against major coastal flooding. The coverage was built as a network with line and polygon attributes.

Coastline of New Jersey (2012)- Land_coastline_2012.zip (24.8 MB, 54.8 MB unzipped)

The data was created by extracting water polygons which represented Rivers, Bays and Oceans from the 2012 land use/land cover (LU/LC) layer from NJ DEP's geographical information systems (GIS) database. The source file contains land use and water body polygon information which was transferred from several sources to lines extracted from the 2012 Land Use/Land Cover data set.

Coastline of New Jersey (2007)- Land_coastline_2007.zip (24.5 MB, 51 MB unzipped)

The data was created by extracting water polygons which represented Rivers, Bays and Oceans from the 2007 land use/land cover (LU/LC) layer from NJ DEP's geographical information systems (GIS) database. The source file contains land use and water body polygon information which was transferred from several sources to lines extracted from the 2007 Land Use/Land Cover data set.

Coastline of New Jersey (2002)- Land_coastline_2002.zip (18.5 MB, 60 MB unzipped)

The data was created by extracting water polygons which represented Rivers, Bays and Oceans from the 2002 land use/land cover (LU/LC) layer from NJ DEP's geographical information systems (GIS) database. The source file contains land use and water body polygon information which was transferred from several sources to lines extracted from the 2002 Land Use/Land Cover data set.

The NJDEP is no longer the data steward of the Congressional Districts layer. This layer has been removed from our site. The Office of GIS is now distributing this data layer on NJGIN on behalf of the Office of Legislative Services (OLS) at: https://njgin.state.nj.us/NJ_NJGINExplorer/DataDownloads.jsp

County Boundaries of New Jersey

The NJDEP is no longer the data steward of the state, county, and municipal boundary layers. These layers have been removed from our site. The Office of GIS is now distributing these data layers on NJGIN at: https://njgin.state.nj.us/NJ_NJGINExplorer/DataDownloads.jsp

Deed Notice Extent Polygons - Envr_mon_soil_DNA.zip (.768 MB, 5.44 MB unzipped)

This data layer identifies those Known Contaminates Sites (KCS) or sites on Site Remediations Programs' (SRP) Comprehensive Site List (CSL) that have been assigned a Deed notice.

Deer Management Units - Grid_deer_unit.zip (0.04 MB, 0.19 MB unzipped)

This grid data represents Deer Management Units (DMU) in New Jersey. Each numbered grid is 14.288 square miles and the data is used in conjunction with Deer Management Zones (DMZ). Hunters can use DMU to identify their location in the DMZ. The DMU is the most smallest and most detailed spatial reference used in deer management, i.e. monitoring disease outbreaks. Please note that initial data generation and creation procedures produced various missing grid numbers (222, 231, 244, 414, 550-559) and some grid order issues. Because of pre-existing use of that data in hunting and for data continuity, these have not been corrected.

Digital Elevation Grid (10 meter) for New Jersey

Digital Elevation Grid (100 meter) for New Jersey - nj100mlat.zip (4163 KB, 17938 KB unzipped)

A lattice is the ESRI GRID raster file generated from USGS DEM files. Digital Elevation Model (DEM) is the terminology adopted by the USGS to describe terrain elevation data sets in a digital raster form. 7.5-minute DEM (10-meter x 10-meter data spacing, cast on Universal Transverse Mercator (UTM) projection) were merged and resampled at 100-meter x 100-meter for this data set.

Digital Elevation Hillshade Grid (100 meter) of New Jersey - nj100mhill.zip (1992 KB, 2943 KB unzipped)

A hillshade is a raster file generated from a lattice. A lattice is the ESRI GRID raster file generated from USGS DEM files. Digital Elevation Model (DEM) is the terminology adopted by the USGS to describe terrain elevation data sets in a digital raster form. 7.5-minute DEM (10-meter by 10-meter data spacing, cast on Universal Transverse Mercator (UTM) projection) were merged and resampled to 100-meter by 100-meter for this data set. The ArcInfo Hillshade command was used with the defaults except for the z-factor which was exagerated by 50 to produce this hillshade.

Elevation Contours - stcon.zip (6331 KB, 15324 KB unzipped)

This dataset is a graphical representation of New Jersey's statewide elevation contours with twenty foot intervals. It was created from the USGS DEM 100 meter lattice.

Fish Index of Biotic Integrity Sampling Points (2000 - 2011) - Envr_mon_water_fibi.zip (.040 MB, .204 MB unzipped)

This data represents the NJDEP Fish Index of Biotic Integrity Monitoring Network active sample point locations for the years 2000 to 2011. A FIBI is an index that measures the health of a stream based on multiple attributes of the resident fish assemblage. Each site sampled is scored based on its deviation from reference conditions (i.e., what would be found in an unimpacted stream) and classified as "poor", "fair", "good" or "excellent".

Golf Courses (Statewide) - njgolf.zip (615 KB, 1495 KB unzipped)

This data represents the fairway, green and tee areas of all the golf courses in New Jersey. It was created by selecting all recreation polygons from the 1995/97 NJDEP land use/land cover (LU/LC) file. There are 256 courses identified and 553 polygons (Many courses show as discontinuous polygons because fairways/green/tee zones are separated by tracts of wetland, forest or other land covers. The purpose of the file is to determine course acreage to assist in estimating the amount of pesticide, fertilizer, and herbicide used on an annual basis. Therefore substantial (1 acre or more) tracts of forest or wetlands are not included in a course's polygons, although these areas may be owned by the Golf Course.

Groundwater Contamination Areas (CEA)

This data identifies those sites where groundwater contamination has been identified and, where appropriate, the NJDEP has established a Classification Exception Area (CEA) in accordance with N.J.A.C. 7:9-1.6 and 1.9(b). CEAs are institutional controls in geographically defined areas within which the New Jersey Ground Water Quality Standards (NJGWQS) for specific contaminants have been exceeded. CEAs are established in order to provide notice that the constituent standards for a given aquifer classification are not or will not be met in a localized area due to natural water quality or anthropogenic influences, and that designated aquifer uses are suspended in the affected area for the term of the CEA.

Groundwater Contamination Areas (CKE) - Envr_mon_gw_CKE.zip (.099 MB, .305 MB unzipped)

This data layer contains information about areas in the state which are specified as the Currently Known Extent (CKE) of ground water pollution. CKE areas are geographically defined areas within which the local ground water resources are known to be compromised because the water quality exceeds drinking water and ground water quality standards for specific contaminants.

Head of Tide (hot) - hot.zip (30 KB, 121 KB unzipped)

This is a graphical representation of the head of tide (hot) points for watercourses of New Jersey. This includes the tributaries of these watercourses as well. The HOT is the point on a tidal watercourse at which measurement of the water surface vertical movement at MEAN HIGH WATER (MHW) becomes no longer practical. All points seaward of the HOT on a tidal watercourse are tidal.

Highlands Preservation Sewer Service Area highpresssa.zip (97 KB, 260 KB unzipped)

The Highlands Water Protection and Planning Act (Highlands Act), N.J.S.A. 13:20-1 et seq. signed on August 10, 2004, repealed all Sewer Service Area (SSA) in the Highlands Preservation Area where collection pipes had not been constructed. This is a graphical representation of the revised SSA mapping in the Highlands Preservation Area. The SSA mapping shows the planned method of wastewater disposal for specific areas, i.e. whether the wastewater will be collected to a regional treatment facility or treated on site and disposed of through a Surface Water (SW) discharge or a groundwater (GW) discharge. Individual subsurface disposal systems discharging less than 2,000 gallons/day (gpd) can be placed anywhere in the state where the site conditions allow and therefore are not mapped.

Historic Archaeological Site Grid - Land_use_HPO_arch_grid.zip (0.338 MB, 2.72 MB unzipped)

This dataset includes a vector grid of approximately 1/2 mile cells indicating the presence of archaeological sites that: 1. Are included in the New Jersey or National Registers of Historic Places, 2. Have been determined Eligible for inclusion through federal or state processes as administered by the New Jersey Historic Preservation Office (HPO), or 3. Have been identified through cultural resource survey or other documentation on file at the HPO.

Historic Districts - Land_use_HPO_district.zip (4.39 MB, 8.36 MB unzipped)

This dataset represents those Historic Districts that: 1. Are included in the New Jersey or National Registers of Historic Places, 2. Have been determined Eligible for inclusion through federal or state processes as administered by the New Jersey Historic Preservation Office (HPO), or 3. Have been identified through cultural resource survey or other documentation on file at the HPO.

Historic Properties - Land_use_HPO_property.zip (22.1 MB, 222 MB unzipped)

This dataset represents those Historic Properties that: 1. Are included in the New Jersey or National Registers of Historic Places, 2. Have been determined Eligible for inclusion through federal or state processes as administered by the New Jersey Historic Preservation Office (HPO), or 3. Have been identified through cultural resource survey or other documentation on file at the HPO.

Historical Shorelines - histshore.zip (1057 KB, 3331 KB unzipped)

This dataset is a graphical representation of the historic shorelines for the four Atlantic Ocean counties (Atlantic, Cape May, Ocean, and Monmouth). It details the 11 different Atlantic Ocean shorelines in New Jersey from the years of 1836-1977. The shorelines are from 1836-42, 1855, 1866-68, 1871-75, 1879-85, 1899, 1932-36, 1943, 1951-53, 1971, and 1977. Not all years are complete or run the entire length of the four Atlantic counties. The coast coverage can be used as a 1986 shoreline.

Hydrography (state/3rd order or higher) - stateriv.zip (2184 KB, 5858 KB unzipped)

This dataset is a graphical representation of New Jersey's State Rivers that are third order or higher. These rivers were reselected from each county's stream coverage. The original map source is the USGS 1:24,000 hydrography digital line graph (DLG) files.

Integrated Water Quality Monitoring and Assessment Report

The Integrated Water Quality Monitoring and Assessment Report is a biennial report that identifies the waters of the State attaining water quality standards, and waters that are impaired and need Total Maximum Daily Loads (TMDLs) as required under the Federal Clean Water Act. The associated GIS files provide the spatial component of the report and include the waterbodies of the state, assessment results, as well as, monitoring station locations.

Known Contaminated Sites List (KCSL)

The Known Contaminated Sites List (KCSNJ) for New Jersey (Non-Homeowner) are those non-homeowner sites and properties within the state where contamination of soil or ground water has been confirmed at levels equal to or greater than applicable standards. This list of Known Contaminated Sites may include sites where remediation is either currently under way, required but not yet initiated or has been completed.

The NJDEP is no longer the data steward of the Legislative Districts layer. This layer has been removed from our site. The Office of GIS is now distributing this data layer on NJGIN on behalf of the Office of Legislative Services (OLS) at: https://njgin.state.nj.us/NJ_NJGINExplorer/DataDownloads.jsp

Municipal Tier Assignments - msrp_tier.zip (1683 KB, 4346 KB unzipped)

This map is a geographic depiction of Tier A and Tier B municipalities as adopted in NJPDES Municipal Stormwater Regulation Program in N.J.A.C. 7:14A-25.3(a) and the associated table. (See 36 N.J.R. 813(a), 2419(a), and 4133(a) February 2, May 17, and September 7, 2004).

Municipal Boundaries of New Jersey

The NJDEP is no longer the data steward of the state, county, and municipal boundary layers. These layers have been removed from our site. The Office of GIS is now distributing these data layers on NJGIN at: https://njgin.state.nj.us/NJ_NJGINExplorer/jviewer.jsp?pg=DataDownloads

Municipalities of New Jersey (Clipped to Coast) - Govt_admin_mun_coast_bnd.zip (24.9 MB, 37.1 MB unzipped)

This data set is a spatial representation of the most current version of municipalities in New Jersey provided by OIT/OGIS. NJDEP clipped the data using the detailed and updated layer "NJDEP Coastline of New Jersey" for various uses. Spatial accuracy was improved upon from earlier municipalities data by integrating features that are coincident with municipal boundaries from other high quality source data sets. This current version of Municipalitites (clipped to coast) shows various updates (listed in processing steps) along with showing the merger of Princeton Borough and Princeton Township to create Princeton.

Natural Heritage Grid Map - nhpgrid.zip (.68 MB, 12.69 MB unzipped)

Through its Natural Heritage Database, the Office of Natural Lands Management (ONLM) documents rare plant species and rare ecological community habitat to inform decision-makers who need to address the conservation of natural resources. The Natural Heritage Grid Map is a geographic information system (GIS) file that provides a general portrayal of the geographic locations of rare plant species and rare ecological communities for the entire state without providing sensitive detailed information.

Natural Heritage Priority Sites - prisites.zip (1063 KB, 4083 KB unzipped)

The Natural Heritage Priority Sites Coverage was created to identify critically important areas to conserve New Jersey's biological diversity, with particular emphasis on rare plant species and ecological communities. Natural Heritage Priority Sites are based on analysis of information in the New Jersey Natural Heritage Database. However, these sites do not cover all the known habitat for endangered and threatened species in New Jersey. If information is needed on whether or not endangered or threatened species have been documented from a particular piece of land, a Natural Heritage Database search can be requested by contacting the Office of Natural Lands Management.

NJPDES Combined Sewer Overflow (CSO)- DRAFT Edition 201202 - cso.zip (.4 MB, .23 MB unzipped)

This is a geographical representation of the locations of CSO points statewide. Combined Sewer Overflows (CSO) are sewers that are designed to collect rainwater runoff, domestic sewage, and industrial wastewater in the same pipe. Most of the time, combined sewer systems transport all of their wastewater to a sewage treatment plant, where it is treated and then discharged to a water body. During periods of heavy rainfall or snowmelt, however, the wastewater volume in a combined sewer system can exceed the capacity of the sewer system or treatment plant.

This data provides information regarding the location of permitted CSO Points, the applicable NJPDES Permit number, the assigned 3-digit discharge serial number, the latitude and longitude, the name (also the street address) of the CSO point, the CSO water region and a unique identifier for each point consisting of the permit number and outfall number.

NJPDES Ground Water Discharges - njpdesgwd.zip (34 KB, 130 KB unzipped)

This layer includes permitted facilities for having sanitary wastewater and industrial wastewater discharges though various methods such as lagoons, spray irrigation, or overland flow. NJPDES permits are issued by the NJDEP and are authorized according to a specific set of rules governing discharges within the state of New Jersey.

NJPDES Surface Water Discharges - Strc_NJPDES_sw_pipe.zip (380 KB, 2.12 MB unzipped)

New Jersey Pollutant Discharge Elimination System (NJPDES) surface water discharge pipe GIS point coverage compiled from GPSed locations, NJPDES databases, and permit applications. This coverage contains the surface water discharge points and the receiving waters coordinates for the active as well as terminated pipes.

Open Space - County Owned - Land_owner_openspace_county.zip (3.84 MB, 15.0 MB unzipped)

This data set contains protected New Jersey open space and recreation areas that are either owned in fee simple interest by a county or are managed by a county but owned in fee by another governmental agency or nonprofit. These open space lands have either received funding through the Green Acres Local Assistance Program or are listed on a Green Acres approved Recreation and Open Space Inventory (ROSI). Types of open space property in this data layer include parks, conservation areas, preserves, historic sites, recreational fields, beaches, etc. This data set provides users with a manageable graphic inventory of county protected open space throughout New Jersey. It serves as a valuable tool in land acquisition decisions and is NOT to be used for describing actual or true property ownership title.

Open Space - State Owned - newstate.zip (3.05 MB, 9.98 MB unzipped)

This data set contains protected open space and recreation areas owned in fee simple interest by the State of New Jersey Department of Environmental Protection (NJDEP). Types of property in this data layer include parcels such as parks, forests, historic sites, natural areas and wildlife management areas.

Parcel data is an important spatial framework data layer against which other spatial data can be developed. This layer can also be a valuable resource in supporting management, planning and analysis activities throughout the state. It is the responsibility of each municipality/county to provide current parcel data to the public. As such, the NJDEP does not create nor distribute parcel data. Those interested in obtaining the most recent parcel data are instructed to contact the respective county GIS department. Another option to check the availability of parcel data is to search the New Jersey Geospatial Data Clearinghouse (NJGIN) at: https://njgin.state.nj.us/ . Begin by launching the NGIN Explorer and then entering the keyword "parcel" in the OPTIONAL KEYWORD field. Click the Full Text option and then click the Search button.

Pinelands Area Boundary - pinelands.zip (76 KB, 200 KB unzipped)

This is an ArcView shapefile of the New Jersey Pinelands boundary (PA_Boundary). The Pinelands boundary shapfile was created by digitizing 68 photo quarterquads. The photo quarterquads were then appended together to create the final shapefile (PA_Boundary).

Place Name Locations - placenam04.zip (266 KB, 4961 KB unzipped)

All place names were taken from the USGS 7.5' topoquad series revised in 2004. This data includes points that represent municipal and other official boundaries as well as various federally recognized neighborhoods and communities.

This is a graphical representation of the States Sewer Service Area (SSA) mapping. The SSA mapping shows the planned method of wastewater disposal for specific areas, i.e. whether the wastewater will be collected to a regional treatment facility or treated on site and disposed of through a Surface Water (SW) discharge or a groundwater (GW) discharge.

Shellfish Classification - Envr_admin_shellfish_bnd.zip (3.39 MB, 4.84 MB unzipped)

This data is a graphic representation of NJ coastal waters classified according to regulations of shellfish harvest. Waters are classified in one of five categories: Prohibited - harvest not allowed under any conditions. Special Restricted - harvest allowed with a special permit requiring further purification of the shellfish before sale. Seasonal (Nov - Apr) - and Seasonal (Jan - Apr) - where harvest is permitted only during certain seasons of the year. The final category is Approved - harvest permitted under any conditions. Classification of the waters is based on the National Shellfish Sanitation Program. As specified in this program, classifications are based on three components: 1) Regular monitoring of water quality 2) Field surveys of shoreline conditions 3) Study of water currents and flows (hydrography). These functions are performed by New Jersey's Bureau of Marine Water Monitoring which monitors about 2,500 locations a minimum of five times a year.

Shoreline Structures - shorstrc.zip (199 KB, 581 KB unzipped)

The shore protection structures project involved the identification, interpretation, and the plotting of all shoreline protection structures located along the New Jersey coastline and within the New Jersey Coastal Areas Facilities Review Act (CAFRA) zone. The structure that were identified include Jetties, Groins, Revetments, Sea Walls, Breakwater. Jetties and groins are protective structures (usually built from rock, wood, or concrete) which extend outward from the shoreline.

Shoreline Type - shoretype.zip (199 KB, 581 KB unzipped)

The shoreline type project involved the identification and coding of the entire New Jersey shoreline within the Coastal Areas Facilities Review Act (CAFRA) zone from Keyport to Hieslerville. The data can be used to delineate different classifications of shoreline based on particular landforms.

South Jersey Marsh - sjmarsh.zip (1814 KB, 4632 KB unzipped)

Southern New Jersey marsh habitat base maps were created in 1991, using 1986 aerial photography. Habitat data was collected and entered into the GIS by Endangered and Nongame Species Program (ENSP) staff, using moneys donated by The New Jersey Natural Lands Trust (NJNLT). This dataset was created most specifically for use in protecting and managing migrating shorebirds on Delaware Bay.

Sport Ocean Fishing Grounds - sportfishing.zip (.67 MB, 1.32 MB unzipped)

Prime fishing areas have a demonstrable history of supporting a significant local quantity of recreational and commercial fishing activity. The Department first mapped prime fishing areas in the 1980s. Since the map is over 20 years old, it was determined in 2003 that an update was needed. Charter boat, party boat and private boat captains were surveyed to identify the areas they consider recreationally significant fishing areas or prime fishing areas. This information was then compiled and refined into a digital format.

State Boundary of New Jersey

The NJDEP is no longer the data steward of the state, county, and municipal boundary layers. These layers have been removed from our site. The Office of GIS is now distributing these data layers on NJGIN at: https://njgin.state.nj.us/NJ_NJGINExplorer/DataDownloads.jsp

STORET Water Quality Monitoring Stations - storet.zip (272 KB, 2468 KB unzipped)

The STORET data maintains the locations of water quality monitoring stations from NJDEP's NJ STORET (Modernized) database. A station is a location at which a data collection event takes place, such a collection of a field sample, measurement of field parameters or evaluation of environmental habitats. NJ STORET maintains NJDEP's water quality monitoring data from January 1, 1999 to the present. Note: water quality monitoring data sampled prior to this date is stored in EPA's Legacy STORET database.

Streams 2002 (National Hydrography Dataset)

This data layer contains information for Flowlines delineated for NJ from 2002 color infrared (CIR) imagery with attributes extracted from the National Hydrography Dataset (NHD).

Supplemental Ambient Surfacewater Monitoring Network - sasmn.zip (.05 MB, .26 MB unzipped)

This data represents sampling points for the Supplemental Ambient Surfacewater Monitoring Network (formerly EWQ) project at NJDEP. The SASMN Network was designed to provide supplemental data for water quality for the entire state.

Surface Water Quality Standards - swqs.zip (73.17 MB, 208.2 MB unzipped)

Surface Water Quality Standards: This data is a digital representation of New Jersey's Surface Water Quality Standards in accordance with "Surface Water Quality Standards for New Jersey Waters" as designated in N.J.A.C. 7:9 B. The Surface Water Quality Standards (SWQS) establish the designated uses to be achieved and specify the water quality (criteria) necessary to protect the State's waters. Designated uses include potable water, propagation of fish and wildlife, recreation, agricultural and industrial supplies, and navigation. These are reflected in use classifications assigned to specific waters. The line-work has been broken/altered to reflect the descriptions specified at N.J.A.C. 7:9B-1.15. When interpreting the stream classifications and anti-degradation designations, the descriptions specified in the SWQS at N.J.A.C. 7:9B-1.15 always take precedence. The GIS layer reflects the stream classifications and anti-degradation designations adopted as of December 21, 2009 is supplemental only and is not legally binding.

The Tidelands claims line depicts areas now or formerly flowed at or below mean high tide. Since the mean high water line may change because of rises in sea level, the line does not represent the current mean high water line. Rather it depicts the mean high water line at the time of mapping and the historic mean high water line predating artificial alterations.

Tidelands Grid - tidegrid.zip (103 KB, 434 KB unzipped)

This dataset is a graphical representation of riparian tidelands grid for New Jersey's Atlantic Coast. It was automated at a scale of 1:24000. Coverage was partially attributed by IEP and the BGIA and Tidelands completed the coding and checked the codes.

Total Maximum Daily Loads (TMDL) Lakesheds - Envr_mon_TMDL_lakeshed.zip (1.5 MB, 3.84 MB unzipped)

The pollutants of concern for these lake TMDLs are phosphorus and fecal coliform. The TMDLs are derived from waste load allocations from point sources plus load allocations from non-point sources and a margin of safety to account for uncertainty in the model.

Total Maximum Daily Loads (TMDL) Shellfish-Impaired Waters - Envr_mon_TMDL_shellfish.zip (10 MB, 16.7 MB unzipped)

The pollutant of concern for these shellfish TMDLs is total coliform. Nonpoint and stormwater point sources are the primary sources of total coliform/fecal coliform loads in these waterbodies.

Total Maximum Daily Loads (TMDL) Streamsheds - Envr_mon_TMDL_streamshed.zip (1.46 MB, 2.37 MB unzipped)

The pollutants of concern for these Stream TMDLs are fecal coliform and total phosphorus.

Total Maximum Daily Loads (TMDL) Historic Streamsheds - Envr_mon_TMDL_streamshed_hist.zip (1.53 MB, 2.38 MB unzipped)

(Pre-2008) The pollutants of concern for these Stream TMDLs are fecal coliform and total phosphorus.

Water Purveyor Service Areas (1998 Public Community) - watpurv1998.zip (943 KB, 1439 KB unzipped)

This is a graphical representation of the 1998 Public Community Water Purveyor Service Areas. Public Community Water Purveyors are systems that pipe water for human consumption to at least 15 service connections used year-round, or one that regularly serves at least 25 year-round residents. The boundaries mapped are those of the actual water delivery or service area.

Water Quality Management Planning Areas - wqmpa.zip (19.21 MB, 30.54 MB unzipped)

This is a representation of the geographical extents of the 12 County/Areawide Water Quality Management Planning Areas (WQMPA). A County or Areawide Water Quality Management Plan (WQMP) was prepared, respectively, by a County or NJDEP pursuant to Section 5 of the Water Quality Planning Act (WQPA), N.J.S.A. 58:11A-1 et seq for each of these areas. A WQMP is used in conjunction with the Statewide WQM Plan, which together constitute the Continuing Planning Process conducted pursuant to the WQPA, the Water Pollution Control Act, N.J.S.A. 58:10A-1 et seq., and N.J.S.A. 13:1D-1 et seq., and as required by Sections 303(e) and 208 of the Federal Clean Water Act (33 U.S.C. 1251 et seq.) to make consistency determinations under the Water Quality Management (WQM) Planning rules, N.J.A.C. 7:15.

Water Quality Stations (Existing) - ewqpoi.zip (39 KB, 181 KB unzipped)

This data represents sampling points for the EWQ (Existing Water Quality) project at NJDEP. The EWQ Network was designed to provide supplemental data for water quality for the entire state.

Water Supply Planning Areas - wsplarea23.zip (399 KB, 959 KB unzipped)

This data provides a spatial delineation of the 23 Water Supply Planning Areas as depicted in the New Jersey Statewide Water Supply Plan (NJSWSP). The NJDEP's Office of Natural Resource Restoration uses the data to spatially reference ground water contaminant plumes at contaminated sites, in-order to determine if a contaminant plume is within a "Surplus" or "Deficit" water supply planning area. This information is used in ground water injury calculations.

Waterbody 2002 (National Hydrography Dataset)

This data contains information for Waterbody features delineated for NJ from 2002 color infrared (CIR) imagery with attributes extracted from the National Hydrography Dataset (NHD).

Watershed Management Areas - depwmas.zip (.43 MB, .87 MB unzipped)

The depwmas data is a simplified version of dephuc14 data. The dephuc14 is NJDEP's version of the U.S. Geological Survey (USGS) hydrologic-unit-code basins that delineates the extent of the DEP watershed management regions and areas to be used for the statewide watershed initiative. Both the depwmas and dephuc14 data comprise the Watershed Base Maps for New Jersey. Also, both were created from the USGS HUC14 by replacing the state boundary included with the USGS version with the DEP state and county border data(stco). The data was produced to address data-processing problems arising from combining the USGS huc14 with NJDEP GIS data due to the use of conflicting state boundaries.

Watersheds (Subwatersheds by name - DEPHUC14) - dephuc14.zip (2.53 MB, 5.51 MB unzipped)

The 14-digit hydrologic units (HUC14s) in New Jersey is a revision of the 2006 version of these units. This version corrects some boundaries to be consistent with a new hydrography coverage based on 1:2,400 aerial photographs (NJDEP, 2008). It also makes some changes to be more consistent with a new 12-digit hydrologic unit coverage (EPA, 2009). This editing process created 42 new HUC14s, deleted one inland HUC14 and five coastal HUC14s in the Delaware Bay, and changed over 100 boundaries. A report detailing these changes (Hoffman and Pallis, 2009) is available online . For programmatic reasons the 14-digit units are clipped to New Jersey's political boundary.

Watersheds (Subwatersheds by name - DEPHUC12) - dephuc12_boundary.zip (3.66 MB, 4.88 MB unzipped)

This data set is a digital hydrologic unit boundary layer (clipped at New Jersey political boundaries) to the Subwatershed (12-digit) 6th level for the State of New Jersey. This data set consists of geo-referenced digital data and associated attributes created in accordance with the "FGDC Proposal, Version 1.0 - Federal Standards For Delineation of Hydrologic Unit Boundaries 3/01/02" https://datagateway.nrcs.usda.gov/. Polygons are attributed with hydrologic unit codes for 4th level sub-basins, 5th level watersheds, 6th level subwatersheds, name, size, downstream hydrologic unit, type of watershed, non-contributing areas and flow modification. Arcs are attributed with the highest hydrologic unit code for each watershed, linesource and a metadata reference file

Watersheds (Watersheds by name - DEPHUC11) - dephuc11.zip (995 KB, 2441 KB unzipped)

Drainage basins are delineated from 1:24,000-scale (7.5-minute) USGS quadrangles. The delineations have been developed for general purpose use by USGS District staff over the past 20 years. Arc and polygon attributes have been included in the coverage with basin names and ranks of divides, and 14-digit hydrologic unit codes. The New Jersey state boundary as originally defined in the USGS source coverage does not match that used by the NJDEP. Therefore the coverage was edited by the NJ Geological Survey to remove the USGS state boundary and insert the NJDEP state boundary, thus resolving most potential clipping errors.

Well Program Atlas Grid 2x2m (2 minute by 2 minute) - atlas2x2m.zip (.62 MB, 1.58 MB unzipped)

The Atlas2x2m is a 2 minute by 2 minute grid dissolved from the Well Program Atlas Sheet Grid for New Jersey.

Well Program Atlas Sheet Grid - atlasgrid.zip (7.5 MB, 27.75 MB unzipped)

This grid is based on an old map series called the New Jersey Atlas Sheets and on a reference system based on them called the New Jersey Rectangular Coordinate System. The grid system developed as the ATLAS_GRID does not exist on the atlas sheets but is based on latitide and longitude grids included on the sheets.

Wind Turbine Siting Map (Large Scale) - windturbinesiting.zip (9.73 MB, 15.41 MB unzipped)

ADVISORY: This layer depicts land areas in the coastal zone where large scale wind turbines are unacceptable due to the operational impacts of turbines on birds and bats.

Upper Wetlands Boundary/Upper Wetlands Limit - uwb_uwl.zip (3.04 MB, 9.36 MB unzipped)

Upper Wetlands Boundary/Upper Wetlands Limit data layer is composed of two wetlands limit lines mapped in two separate NJDEP mapping programs.


Support for first-class place model #79

The following commentary was submitted by @ttwetmore, opening it up here for discussion, my own comments to follow:

In the GEDCOM X model places are “second-class citizens” (don’t occur in their own right as record level entities) and are not hierarchical. I believe there are places where places can be second-class citizens, that is, simply as attributes of an event or other attribute, but there are many places where an independent set of hierarchical place records could be useful. I also believe places should be hierarchical, with each place able to refer to one or more more inclusive places that they are a part of. Most modern genealogical systems seem to have some kind of place expert module built in that provides this associated structure of place information. Here is a proto specification of a possibility:

In my Google protocol notation, a structure is information that is found inside a record, and a message is a record that will be encoded, transmitted and decoded as a whole. In the DeadEnds model a place structure may be used in a number of contexts. One such context is shown here as the contents, along with a UUID value for a record id, of a place record.

Ranbo commented Sep 29, 2011

It has been surprising to me how many different people have asked that place be included as a first-class part of a record model. Part of this may be because places are so important to genealogy, and everyone recognizes the importance of some kind of source authority (or "expert system").

In a genealogical record, I would think places are properly expressed as an attribute of an event (a "second-class" citizen), because we care most about what the record says about the persons it mentions, instead of what it says about the places it mentions. However, when building a place authority, you may well care about what a gazetteer or history book says about a place, and in fact you might want to capture such information as "evidence" for your conclusions about what is true (and has been true at different points in time) about that place. On the third hand (left foot?), when doing genealogical research, you might want to see the conclusional knowledge about the place that is mentioned in a record in order to give you deeper background knowledge from which to draw stronger conclusions or guide your research.
Finally, we often have information in a record about the "place type" of various "place parts", such as when a form says "county:" and the blank is filled in with "Fulton". So we want a record model to be able to capture what the record says about place parts, in order to disambiguate when interpreting the place.

A "place source data model" seems like it would be best as an effort separate from GedcomX. On the other hand, GedcomX might benefit from having access to a "place conclusion model" (or just "place model") that can serve up things like the place name in various languages place boundaries and name changes over time hierarchical relationship to "parent" and "child" places etc.

Carpentermp commented Sep 29, 2011

My gut reaction is that there ought to be a robust "place model", but that it belongs as a separate model from Record. Places in Records might refer to Places in a place authority, or "archive of place records", but they wouldn't redundantly include place information beyond identifying the intended place. In SoRD, we had a "standardized place" which was a URI for the "place authority" and "identifier" for the place in that authority. Perhaps we should consider something along these lines for GedcomX?

Ttwetmore commented Sep 29, 2011

I agree that places are not as important as persons in a genealogical model, but why wouldn't they be modeled in the same model that holds the record object? I would think that GEDCOM X should model the universe that is important to working genealogists, and place objects play a major role in that universe. Frankly I don't find this universe to be so extensive that it needs multiple models to capture it.

As far as first and second class citizenship goes, see the model at the start of this threatd The PlaceStructure is the second class citizen -- it can appear as as attribute of any event or other attribute that requires a place in this case the place can be self-contained at the spot it appears. Though note, please, that a second class place citizen can refer to first class place citizens through its parent pointers (places can have multiple containing places if needed for historical or ecclesiastical reasons). And PlaceMessage is the first-class place citizen, complete with a UUID that gives it independent existence.

A "place authority" could be based on a network of these PlaceMessages that could be prepared by third parties. This is the way I believe all current servers and some desktop systems do it now. A GEDCOM X transmission file could either include the place records required by the data, or, since the proper way to do this would be through a UUID-based scheme, the places would not have to be transmitted as they would have a universal definition somewhere "out there" on the web.

This notation is Google protocol buffers, and can be put into JSON, Relax NG or Schema notation at will.

Stoicflame commented Oct 5, 2011

Apologies for the delay in this I'm at JavaOne, making my bandwidth limited.

Please note the proposal at #88, which allows for a to-be-defined definition (whether part of the GEDCOM X spec or not) that specifies a place as either (1) a typed literal string or (2) a separate resource or (3) both. This gives us enough flexibility to move ahead with a release of the specification without painting ourselves into a corner when/if the community determines a "place authority" or "place standard" is needed.

Ttwetmore commented Oct 5, 2011

Add the notion of "recursion", that one place can be a part of another place, and "I'm in."

Stoicflame commented Oct 6, 2011

Add the notion of "recursion", that one place can be a part of another place, and "I'm in."

That would undoubtedly be part of the definition of a first-class place model.

EssyGreen commented Feb 7, 2012

I welcome the idea of a Place entity but as far as I can see that's not what we've got since the place details are still embedded within each Fact rather than having their own identity which is then cross-referenced by each Fact.This does not allow the Place itself to be extended and/or further details to be recorded against it without replicating and fragmenting data throughout the model.

Also, please bear in mind the fact that Places are not necessarily hierarchical and it will not be feasible to come up with a universal place labelling system (beyond latitude/longitude) . For example, in the UK, records may refer to Parishes (ecclesiastical boundaries) and/or Districts/Sub-Districts (civil authority boundaries) and/or (parliamentary) Boundaries UK censuses are broken up into Enumeration Districts (walkable areas for enumerators). All of these boundaries change over time and cannot be neatly boxed inside one another in a geographical hierarchy.

It seems to me that the Record and Conclusion Models have things the wrong way round . the PlaceParts in the record model just needs to be a list of key/value pairs, (the ordering of which may or may not be hierarchical) with the labels being standardised according to the original source and/or the application. The PlacePartTypes seem to be a rather haphazard selection of labels and I'm not convinced that defining these adds anything to the model.

Conversely, the Conclusion Model uses an Original and FormalValue (both of which are open-ended) but it seems to me that this is where defined parts are useful (e.g. Latitude/Longitude) and where the Place should be a link to a Place object rather than embedded within the Fact. The Place object can then define such things as: Full name, abbreviation, postal address, Latitude/Longitude etc. It can also then have it's own embedded Facts (e.g. "Great Flood of Sheffield", 1864, "blah blah description" plus source references, illustrations etc)

Joshhansen commented Feb 14, 2012

Places are already effectively modeled by a number of existing vocabularies. The SpatialThing class from W3C's geo vocab is widely accepted for representing longitude/latitude coordinates.

GeoNames provides a widely used extension of SpatialThing called Feature, by which is defined a hierarchical feature model like people want for GedcomX. GeoNames also provides a freely available database of 10 million place features around the world. Support has recently been added for historical places as well. If GeoNames doesn't meet users' needs, there are a number of other vocabularies that might do the job.

Attempting to implement our own place vocabulary seems foolish since so many already exist. Instead of reinventing the wheel, let's have GedcomX accept any SpatialThing as a place. If people wish to represent place hierarchies, they can do so using a vocabulary like GeoNames. GedcomX could endorse such a vocabulary or even import it into the GedcomX namespace so the genealogy community can rally around a single standard. GedcomX could also introduce new feature types for representation of political entities like duchies, kingdoms, and so on.

EssyGreen commented Feb 14, 2012

Attempting to implement our own place vocabulary seems foolish since so many already exist

My original point was that the Place needed to be an entity rather than having its details embedded within each Fact. At the moment the Place in the Conclusion Model is still an embedded object and the Place in the Record Model inherits from a Field.

I believe that both should be the same (or should inherit from an abstract Place object in the common model) and that it should be a top-level Entity.

DallanQ commented Feb 14, 2012

I looked at a number of these when creating the place database for WeRelate.org, which is now available as a free download:

It includes both current and historical places, alternate names, many places list both their historical and modern jurisdictional hierarchies, and many places include coordinates.

  • Geonames: Lots of places, modern only (or mostly), most places are geographic features like lakes and rivers, but places are in a flat hierarchy -- that is, cities in England did not list the county they are in. Having a hierarchy is pretty important - how do you know which Sutton in England to match when the user says "Sutton, Bedfordshire, England"? There are a dozen different Sutton's in their database for England, and you don't have any way to determine which Sutton is in Bedfordshire, except by calculating shortest distance from each Sutton to the centroid listed for Bedfordshire - not very reliable. Because of the lack of hierarchy, I ended up not using this resource. I wasn't aware that they had included historical support, though it appears still in the very early stages. They've added an "isHistorical" flag for names that are no longer used, and are considering adding fromPeriod and toPeriod. Until they add jurisdictional hierarchies to their database, they won't have even scratched the surface of historical issues though.
  • Getty Thesaurus of Geographic Names: http://www.getty.edu/research/tools/vocabularies/tgn/ Smaller than Geonames, around 1.7M names for 992K distinct places, mostly modern, though more historical places than Geonames, most places are geographic features, places are in a hierarchy(!), data compiled from about a dozen different sources: mainly NGA/NIMA but also Rand McNally, Encyclopedia Britannica, Domesday book, generally lists places under the jurisdictional hierarchy they appeared in about 12 years ago. I got permission to include their populated places and political jurisdictions into the WeRelate place database. More information: http://www.getty.edu/research/tools/vocabularies/tgn/about.html and http://www.getty.edu/research/tools/vocabularies/tgn/faq.html
  • Alexandria Digital Library Gazetteer: http://www.alexandria.ucsb.edu/gazetteer/ContentStandard/version3.2/GCS3.2-guide.htm I obtained a license to this as well, but after reviewing it, it seemed similar to Getty so I did not use it.
  • Family History Library Catalog: The only resource I was able to find with historical places. Most (but not all) places are listed according to the jurisdictions they were in just prior to WWI. There are some duplicates: some places listed under Galicia are repeated under Poland for example. I crawled the the FHLC place database back in 2005 and included it in the WeRelate place database.
  • Wikipedia: Both current and historical places. A terrific source of information, but difficult to extract. I extracted 10's of thousands of places (certainly not all of them, but the ones that had decent templates for extraction) back in 2005 and included them in the WeRelate place database. A side-benefit of incorporating Wikipedia is that the database includes links back to the wikipedia articles, which often have helpful historical information. (Though the links aren't included in the extract on github I'll fix this shortly.)
  • Freebase.com: http://www.freebase.com/view/location updated database of places they've extracted from Wikipedia. Includes about 80,000 current and historical places. I'd love to integrate this into the WeRelate place database, though it will be a big project (see below).
  • OpenStreetMap: http://www.openstreetmap.org/ has coordinate information for modern places, and places are arranged into a hierarchy(!), I'd like to use this to fill in missing coordinates into the place database at WeRelate.org.
  • Statoids.com: http://statoids.com/ not a place database per se, but a fantastic source of information for how jurisdictions have changed over time. I used this and wikipedia and Encyclopedia Britannica when compiling the WeRelate place database (see below).

The big challenge when creating a place database is not getting the data -- as you can see, there are many sources for that. It's merging data together from multiple sources without creating duplicates. You want to say that City X in Historical Province Y from the FHLC is the same as City X' in Modern State Z in Wikipedia. Merging duplicate places is generally harder than merging duplicate people, because place names can change dramatically after wars. Even merging Getty and Wikipedia was challenging, because of the changes European countries have made to their jurisdictional hierarchies over the past 10 years due to the EU. I spent months merging Getty, FHLC, and Wikipedia together, and WeRelate users have spent the past seven years continuing to clean it up and organize it better afterward. If you're going to try to create your own current+historical place database, take the merge-time into account. Or just use the free one I posted on github.

I recently matched 7.5M places appearing in the 7K gedcoms submitted to WeRelate over the past five years to see what kinds of problems were occurring most frequently:

  • We don't have comprehensive coverage for US townships. This is on my short-list of things to add.
  • We still have duplicate places in Eastern Europe due to FHLC having duplicates that were not caught.
  • We still don't have all of the historical and modern places in Europe merged (though many have been merged).
  • We don't have all of the historical jurisdictions listed.
  • We're missing some places (though not that many).

I just posted this a couple of weeks ago, so there may still be some rough edges. I know of at least one other organization who's using it already, and I'm talking with several other organizations who are interested. I'm making it freely available so that others don't have to go through the pain that I did.


About the Jackson County GIS Department


The Jackson County GIS department was first formed in 2007. We currently consist of three full time and one part time positions. Our mission is to provide and support GIS technology in an effective, intelligible, and timely manner that meets the needs of all County departments, agencies, and cities within the County and enables them to better serve the public. We invite you to view our available maps . We regularly publish our data onto QPUBLIC and several local arcserver apps for you to view.


Set up intelligent skill finder model

To configure ML-based skill classification rulesets, you can configure intelligent skill finder models that will be used for predicting skills.

You can create and train machine-learning models that use AI to determine the necessary skills for new work items. You can create and train the model by using the data in Microsoft Dataverse. However, if you're trying to set up the model in a new organization or if skill-based routing wasn't in use, you might not have the needed skill data. In such conditions, you can use data from another application by using the Import from Excel option in the skill finder model.

Intelligent skill finder depends upon the custom AI Builder category classification model. Therefore, AI Builder should be available in the geographical region where you want to use intelligent skill finder. More information: Availability of AI Builder.

Create skill finder models

Perform the steps in this section to set up the intelligent skill finder model. You can create as many models as your business requires.

In Omnichannel admin center, select User attributes in the site map, and then select Manage beside Intelligent skill finder.

Select New, and on the Configuration tab of New skill finder model page, enter a name.

In Data criteria, enter the following to form the dataset records:

  • Attributes (Required): Select attributes in the Attributes and related list to form the training dataset. The corresponding attribute values will be merged in the sequence they are added and will be used to form the input string for the model training data.
  • Filters: Optionally, apply filters to conditionally select the relevant records.
  • Date range: Select a value to set the time period for which the records need to be loaded.

Select Save, and then select Load training data. The Training data tab appears and displays the data load status.

After the load is complete, review the load, and edit the records if you want to modify the tags.

In the Training data section, select the checkbox beside Input data to select all the records, and select Approve. You must approve a minimum of 50 records for the model to be trained.

Select Train model, and select Train model on the confirmation dialog box.

After the status changes to training completed, select the rows that you want to publish, and select Publish model. The skills model is ready for use.

Retrain the model iteratively

You should retrain your published model iteratively to improve the model with new data in Microsoft Dataverse. For example, model retraining can be done by using the records in which agents have updated the skills for records or conversations. Define the conditions as seen in the following screenshot.

Use training data imported from Excel file

If you don't have data to train your model, you can populate skills and attributes data in Excel files and upload them to the application by using the import feature of Microsoft Dataverse.

To use the data from the Excel files, you must make sure of the following:

The model name in the application should match the name in the Training record column of the Excel file.

Name the files as msdyn_ocsitrainingdata.csv and msdyn_ocsitdskill.csv.

A sample of each file is as follows.

msdyn_ocsitrainingdata.csv

Skill finder model Training record name Input data
CCSFM01-Contoso Coffee skill finder model CCSFM01-Contoso Coffee training data A10001 Hi, I work at Trey Research. One of the automatic espresso machines is becoming overheated and starts giving a burning smell after 30 minutes of usage. Please help! Yes. No.
CCSFM01-Contoso Coffee skill finder model CCSFM01-Contoso Coffee training data A10002 Hi, I work at Trey Research. One of the automatic espresso machines is becoming overheated and starts giving a burning smell after 30 minutes of usage. Please help! Yes. No., can you please connect me to an agent
CCSFM01-Contoso Coffee skill finder model CCSFM01-Contoso Coffee training data A10003 Hi, I work at Trey Research. One of the automatic espresso machines is becoming overheated and starts giving a burning smell after 30 minutes of usage. Please help! Yes. Not really, can you pls help
CCSFM01-Contoso Coffee skill finder model CCSFM01-Contoso Coffee training data A10004 Hi, I work at Trey Research. One of the automatic espresso machines is becoming overheated and starts giving a burning smell after 30 minutes of usage. Please help! Yes. Not at all, can I speak to a human
CCSFM01-Contoso Coffee skill finder model CCSFM01-Contoso Coffee training data A10005 Hi, I work at Trey Research. One of the automatic espresso machines is becoming overheated and starts giving a burning smell after 30 minutes of usage. Please help! Yes. No. Need urgent attention

msdyn_ocsitdskill.csv

Training record Characteristic mapping Characteristic
CCSFM01-Contoso Coffee training data A10001 Café A-100 Café A-100
CCSFM01-Contoso Coffee training data A10001 Heating Heating
CCSFM01-Contoso Coffee training data A10001 Electrical Electrical
CCSFM01-Contoso Coffee training data A10002 Café A-100 Café A-100
CCSFM01-Contoso Coffee training data A10002 Heating Heating

Perform the following steps to upload the data for training your model:

On the Skill finder model page, enter a name for the model, and then save the form.

Select the Training data tab, and select Import Excel.

Select the .csv files to upload in the import tool.

Review the upload settings, and select Finish after you go through the stages. The data upload starts. The time taken for the data upload depends on the number of records.

Optionally, you can select Refresh to see the updated status of the data upload.

Perform the steps 5 through 8 in Create skill finder models to approve, train, and publish your model.


Watch the video: VORTEX CM - Δημιουργία Παραγγελίας