This date set contains up-to date information regarding current and future roadwork occuring on provicnally owned and maintained highways, and is available to be viewed on [Ontario 511](https://511on.ca) under the "construction and roadwork" layer. This data set can also be viewed on the Ontario 511 text report page at https://511on.ca/list/roadwork.

Data collected between 2005 to 2007, 3% of sample collected in 2005, 51% in 2006 and 46% in 2007. This dataset contains a compilation of data collected from different sources. **VOG 06/** **VOG** **08 (Value of Goods)** : The data is derived from the information collected in the 2006 Ontario Commercial Vehicle Survey. This survey is a roadside intercept survey of truck drivers, which collects information about the trip, commodity and the vehicle. The survey primarily captures intercity trucking activity and under-represents truck flows in urban areas. The value of goods provided in this table is derived from the Commercial Vehicle Survey, but factored up to represent the overall trucking activity on the network segment for 2006 and 2008. **AADTT 2006 and ****AADTT** **2008:** The data is derived from the Ministry of Transportation's (MTO) inventory of annual traffic data for the Provincial Highways. The commercial volumes are first calculated using the AADT and the Commercial Percentage values for each traffic segment. These values are then adjusted to remove variations between segments caused by fluctuations in AADT. The volume given for each direction is one-half of the total value. MTO does not maintain volume by direction. For freeway segments with core/collector configuration, the total volume is divided into four equal portions and assigned to each stream. **Hourly Truck Volumes ( WD00-23 and WN00-23): ** These fields contain estimates of average hourly volumes for a typical weekday and weekend day. The estimates are based on observed hourly distribution at more than 100 directional Commercial Vehicle Survey sites across the province, **AADTT** and other information. **RD _NAME:** Name of the road **VOG** **06:** 2006 average daily value of goods assigned to road network link by directions. **VOG** **08:** 2008 average daily value of goods assigned to road network link by directions. **AADTT** **2006:** 2006 Annual Average Daily Truck Traffic; it is the truck volume assigned to road network link by directions. **AADTT** **2008:** 2008 Annual Average Daily Truck Traffic; it is the truck volume assigned to road network link by directions. **WD** **00-23:** 2008 Weekday ( **WD** ) hourly truck volume; 00 - 23 represents starting hour of the day (e.g. 12 represents 12 P.M. - 1 P.M.). **WN** **00-23:** 2008 Weekend ( **WN** ) hourly truck volume; 00 - 23 represents starting hour of the day (e.g. 12 represents 12 P.M. - 1 P.M.). *[ WD]: Week day *[VOG]: Value of Goods *[AADTT]: Annual Average Daily Truck Traffic *[WN]: Week end *[RD]: Road *[WD]: Week day *[MTO]: Ministry of Transportation *[AADT]: Annual Average Daily Traffic

The Ontario Railway Network (ORWN) is seven data classes that use the federal GEOBASE standard for the National Railway Network (NRWN) geospatial data. The seven data classes include: * track * crossing * junctions * marker posts * structure lines * structure points * stations Although the ORWN is mainly used as base data for cartographic products, users will also get railway-associated attributes.

The Ministry of Transportation's Remote Northern Transportation Office in Thunder Bay operates 29 airports. These are located in Ontario's far north, extending from the Manitoba border on the west to Hudson's Bay on the north and to the Quebec border on the east. Except for Pickle Lake and Armstrong, these airports serve aboriginal communities, which have no all-weather road access to the rest of Ontario.

The purpose of this dataset is to identify the location of river valley connections.

Mineral Claims

Business / Person that acts as an agent for a lender in arranging consumer loans

The Ontario Digital Surface Model (Imagery-Derived) is a raster elevation product that was generated from the elevation point clouds created via pixel-correlation from aerial photography. A DSM is the highest reflective surface of features captured by the sensor. This surface is also referred to as the first reflective surface. The DSM may include: * treetops * rooftops and tops of towers * telephone poles * other natural or artificial features * ground surface if there is no vegetative ground cover This data is for geospatial tech specialists, and is used by government, municipalities, conservation authorities and the private sector for land use planning and environmental analysis.

The Ontario Point Cloud (Lidar-Derived) consists of points containing elevation and intensity information derived from returns collected by an airborne topographic lidar sensor. The point cloud is structured into non-overlapping 1 km by 1 km tiles in LAZ format. The following classification codes are applied to the data: * unclassified * ground * water * high noise * low noise This dataset is a compilation of lidar data from multiple acquisition projects, so specifications, parameters, accuracy and sensors may vary by project. This data is for geospatial tech specialists, and is used by government, municipalities, conservation authorities and the private sector for land use planning and environmental analysis. __Related data:__ Raster derivatives have been created from the point clouds. These products may meet your needs and are available for direct download. For a representation of bare earth, see the [Ontario Digital Terrain Model (Lidar-Derived)]( https://geohub.lio.gov.on.ca/maps/mnrf::ontario-digital-terrain-model-lidar-derived/about). For a model representing all surface features, see the [Ontario Digital Surface Model (Lidar-Derived)](https://geohub.lio.gov.on.ca/maps/mnrf::ontario-digital-surface-model-lidar-derived/about).

Data are contaminants in tissue of caged mussels put in the river for 3 weeks at about 25 stations located on the Canadian and US side of the river. The main objective of the study is to identify contaminant sources, or source areas requiring more detailed follow-up investigations, based on the level  of contaminants in the mussels. Compounds monitored include: * organochlorinated pesticides * Polychlorinated biphenyls * dioxins/furan * chlorinated benzenes * Polycyclic aromatic hydrocarbons * industrial organic compounds