The resistance surface that formed the basis of our coastal marten connectivity model is comprised of several data layers that represent forested and non-forested land cover, waterbodies, rivers, roads, and serpentine soils. This dataset contains the roads data used in the resistance surface. To see actual resistance values assigned to the road classes in this raster when the resistance surface is compiled, see the associated spreadsheet of resistance surface data sources and resistance values. To build the roads resistance layer, we used the OpenStreetMap dataset (OpenStreetMap contributors 2018), which is a global, collaborative project to create a free, editable map of the world’s roads. OpenStreetMap contains 33 different types of roadway in the modeled landscape that are classified based on type, size, usage, etc. (https://wiki.openstreetmap.org/wiki/Map_Features #Highway). We selected 14 of these that seemed likely to have the potential to impact coastal marten habitat and movements, modeling them as having higher resistance with increasing width and the amount and speed of vehicle traffic, as implied in the classification descriptions. We examined descriptions of the 33 roadway classifications in OpenStreetMap that occurred in our modeled landscape. Because of the origins of this global dataset, most of these road types have been given names more typically used in Great Britain than in the USA (e.g. “Motorway” instead of “Freeway”). However, good descriptions of all classifications were available in supporting documentation online (https://wiki.openstreetmap.org/wiki/Map_Features#Highway), allowing us to assess which categories were likely to impact coastal marten habitat and movements based on road width and the amount and speed of vehicle traffic implied in the classifications (we lacked data on actual traffic levels or speed limits). We assumed that freeways and major divided highways would act as strong “psychological” deterrents to martens crossing them because of the noise and disturbance from traffic and the likelihood of having to cross a wide area with little or no cover (Forman and Alexander 1998, Alexander and Waters 2000). These roads also probably pose the greatest risk of mortality from being struck by a vehicle; we assigned them a resistance value of 150. We then assigned decreasing resistance values through smaller highways and roads (“Trunk”, “Primary”, “Secondary”, and “Tertiary” roads and their associated “link roads”, which are generally very short offshoots connecting to other roads) (Fig. 4D, Table 2 of report). Many of the smallest roads (such as residential roads and logging roads) overlapped with data represented in the OGSI or ESLF layers, which could have led to “double counting” the resistance of these features and modeling them as more significant barriers than we intended them to be. Therefore, we ended up not assigning resistance values to anything smaller than “Unclassified” roads, which are usually two-lane roads connecting to small rural communities. Ultimately, 11 road classifications were assigned resistance values >0 (see Appendix 1 for full list). Rivers and roads were represented on the resistance surface as linear features a single pixel 30m wide. Because many of these features were modeled as representing significant barriers to movement by martens, we took care to minimize the occurrence of any breaks in these linear features that would encourage LCPs to pass through them. In many instances, roads and rivers are in fact wider than 30m, and in these cases the pixels were classified by the relevant surrounding land cover type (OGSI or ESLF). This is an abbreviated and incomplete description of the dataset. Please refer to the spatial metadata for a more thorough description of the methods used to produce this dataset, and a discussion of any assumptions or caveats that should be taken into consideration.

This data set contains the boundaries of the four major cities in the Northern Ecuadorian Amazon, the locations of primary communities in the colonist settlement area, and the locations of the road network, circa 2002. This area in northeastern Ecuador, know as the northern Oriente of Ecuador, borders the Andes Mountains and contains the headwaters of the Amazon River.

The road network was originally digitized from 1:50,000 scale topographic maps from 1990. The surface attributes for the majority of the roads have been updated based on later remote sensing and field observations from 1999 and 2002. There are three compressed (*.zip) files with this data set.

3.12.680 Regulation of operation of vehicles over a certain size. (a) Weight Limitations. It shall be unlawful for any vehicle having a gross weight, including load, in excess of three tons to be operated on any street within this City except those streets defined in subsections (b) and (c) as primary or secondary truck routes, except when necessary for the purpose of making pickups or deliveries of goods, wares, and merchandise from or to any building or structure located on the restricted street or for the purpose of delivering materials to be used in the actual and bona fide repair, alteration, remodeling, or construction of any building or structure upon the restricted street for which a building permit has previously been obtained. (b) Primary Truck Route. The primary truck route is to be used for all inter-City and interstate truck traffic and shall be as follows: (1) The Santa Monica Freeway. (2) Olympic Boulevard. (3) The Pacific Coast Highway. (4) Lincoln Boulevard, from Olympic Boulevard to the southerly City limits. (c) Secondary Truck Routes. The secondary truck routes are to be used by trucks transporting merchandise, materials, or equipment having an origin or destination within the City limits and are as follows: (1) Montana Avenue, from Seventh Street to Twenty-Sixth Street. (2) Santa Monica Boulevard. (3) Colorado Avenue. (4) Broadway, from Ocean Avenue to Twenty-Sixth Street. (5) Exposition Boulevard. (6) Pico Boulevard, from Ocean Avenue to the easterly City limits. (7) Ocean Park Boulevard, from Lincoln Boulevard to the easterly City limits. (8) Ocean Avenue, from Santa Monica Boulevard to Pico Boulevard. (9) Neilson Way. (10) Main Street, from Pico Boulevard to the southerly City limits. (11) Lincoln Boulevard, from Montana Avenue to Olympic Boulevard. (12) Eleventh Street, from Pico Boulevard to Santa Monica Boulevard. (13) Fourteenth Street, from Ocean Park Boulevard to Montana Avenue. (14) Seventeenth Street, from Pico Boulevard to Santa Monica Boulevard. (15) Twentieth Street, from Pico Boulevard to Montana Avenue. (16) Cloverfield Boulevard except between Pico Boulevard and Ocean Park Boulevard. (17) Twenty-Sixth Street except between Montana Avenue and the northerly City Limits. (18) Stewart Street. (d) Deviation from Established Routes. When it becomes necessary for any vehicle to deviate from the primary or secondary truck routes as permitted by subsection (a), such deviation shall be made by way of the shortest possible route or routes between the destination on the restricted street and the nearest streets described in subsections (b) and (c). (e) Exemptions for Governmental Vehicles and City Licensed Private Vehicles. This Section shall not apply to vehicles owned or operated by any Federal, State, county, or local governmental agency while such vehicles are being used in the performance of their duties. (f) Codification. This Section, insofar as it designates truck routes, is a codification of Ordinance Number 709(CCS). (Prior code 3359; amended by Ord. No. 1479CCS, adopted 5/9/89; Ord. No. 1781CCS § 1, adopted 11/29/94; Ord. No. 2587CCS § 1, adopted 9/11/18)

This dataset contains the Department of Transport and Main Roads road location details (both spatial and through distance) as well as associated traffic data.

It allows users to locate themselves with respect to road section number and through distance using the spatial coordinates on the state-controlled road network.

Through distance – the distance in kilometres measured from the gazetted start point of the road section.

Note: "Road location and traffic data" resource has been updated as of June 2025.

The 2017 Landuse captures how the landscape in NSW is being used for food production, forestry, nature conservation, infrastructure and urban development. It can be used to monitor changes in the landscape and identify impacts on biodiversity values and individual ecosystems.

The NSW 2017 Landuse mapping is dated September 2017.

This is version 1.5 of the dataset, published December 2023.

Version 1.5 of the 2017 Landuse incorporates the following updates:

• Fine scale mapping of the Central Coast, Illawarra and Shoalhaven regions
• Mapping enhancements to regional centres to improve the mapping accuracy for these centres
• NSW road network based on road centreline data from Transport NSW, with standardised buffer applied to approximate the carriage width based on the road type
• Plantation type (native hardwood and softwood) information within State Forest Estates
• Horticulture data to tertiary or commodity level present in September 2017 from Australian Tree Crop Map Dashboard developed by University of New England - Applied Agricultural Remote Sensing Centre https://www.une.edu.au/research/research-centres-institutes/applied-agricultural-remote-sensing-centre/collaborative-r-and-d-opportunities/industry-applications-and-maps
• Fixes to identified errors since published version 1.2

Previous Versions *Version 1.4 internal update (not published) * Version 1.3 internal update (not published) * Version 1.2 published 24 June 2020 - Fine scale update to Greater Sydney Metropolitan Area * Version 1 published August 2019

The 2017 Landuse is based on Aerial imagery and Satellite imagery available for NSW. These include, but not limited to; digital aerial imagery (ADS) captured by NSW Department of Customer Service (DCS), high resolution urban (Conurbation) digital aerial imagery captured on behalf of DCS, SPOT 5, 6 & 7(Airbus), Planet™, Sentinel 2 (European Space Agency) and LANDSAT (NASA) Satellite Imagery. Mapping also includes commercially available imagery from Nearmap™ and Google Earth™, along with Google Street View™.

Mapping takes into consideration ancillary datasets such as tenure such as National Parks and State forests, cadastre, roads parcels, land zoning, topographic information and Google Maps, in conjunction with visual interpretation and field validation of patterns and features on the ground.

The 2017 Landuse was captured on screen using ARC GIS (Geographical Information Software) at a scale of 1:8,000 scale (or better) and features are mapped down to 2 hectares in size. Exceptions were made for targeted Landuse classes such as horticulture, intensive animal husbandry and urban environments, which were mapped at a finer scale.

The 2017 Landuse has complete coverage of NSW. It also includes updates to the fine scale Horticulture mapping for the east coast of NSW - Newcastle to the Queensland boarder and Murray-Riverina Region. This horticultural mapping includes operations to the commodity level based on field work and high-resolution imagery interpretation.

Landuse classes assigned are based on activities that have occurred in the last 5-10 years that may be part of a rotational practice. Time-series LANDSAT information has been used in conjunction with more recent Satellite Imagery to determine whether grasslands have been disturbed or subject to ongoing land management activities over the past 30 years.

The 2017 Landuse was captured on screen using ARC GIS (Geographical Information Software) at a scale of 1:8,000 scale (or better) and features are mapped down to 2 hectares in size. Exceptions were made for targeted Landuse classes such as horticulture, intensive animal husbandry and urban environments (including Greater Sydney Metropolitan region), which were mapped at a finer scale.

The reliability scale of the dataset is 1:10,000.

Mapping has been subject to a peer review and quality assurance process.

Land use information has been captured in accordance with standards set by the Australian Collaborative Land Use Mapping Program (ACLUMP) and using the Australian Land Use and Management ALUM Classification Version 8. The ALUM classification is based upon the modified Baxter & Russell classification and presented according to the specifications contained in http://www.agriculture.gov.au/abares/aclump/land-use/alum-classification.

This product will be incorporated in the National Catchment scale land use product 2020 that will be available as a 50m raster - Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) http://www.agriculture.gov.au/abares/aclump/land-use/data-download

The Department of Planning, Industry and Environment (DPIE) will continue to complete land use mapping at approximately 5-year intervals.

The 2017 Landuse product is considered as a benchmark product that can be used for Landuse change reporting. Ongoing improvements to the 2017 Landuse product will be undertaken to correct errors or additional improvements to the mapping.