DCGIS_APPS/PepcoPowerOutages2020 (MapServer)

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Service Description: This dataset consists of Pepco power outages District wide, by zip code, and point location as reported by Pepco’s public facing power outage mapping application. Because of the Pepco mapping application updating interval, the data contained in this service may be up to 5 minutes behind what is shown on Pepco’s map. A power outage “customer” can be an individual (a home or apartment for example) or an entity/institution (Georgetown University or the Marine Barracks located in zip code 20390). About the DC outage data: The DC outage data consists of one feature – the District of Columbia – that has power outage information appended to it from Pepco’s power outage web site. It consists of three fields: custs_out (customers without power), total_custs (total # of DC customers) and perc_out (customers without power divided by total DC customers). About the zip code outage data: The zip code boundary data used in this dataset is the ZIP Code Tabulation Areas (ZCTA) data from the U.S. Census Bureau. This data is the best approximation to what Pepco is using in their power outage map but is not a 100% match; Pepco combines some zip codes into one outage reporting area and additionally, not all zip code boundaries will exactly match. The combined zip codes are typically small, building-based or campus-based zip codes that are grouped to a more expansive zip code that they fall within. The PercentOut_DC field indicates a zip code’s contribution to the total power outages within D.C. It is derived by dividing the zip code’s current outage number by the number of Pepco customers within the District and rounded to 2 decimal points. This means that a zip code experiencing very few outages may have a value of 0 (zero) for this field, indicating an insignificant contribution to outages across the district. Summing the PercentOut_DC field will give you the total percentage of customers without power District wide. About the point outage data: As defined by Pepco, an Outage Order is: “the prediction to a single point of failure based on input from AMI and customers. The predicted device or service causing an interruption is based on our electrical model that identifies all customers that are electrically connected downstream to that device. The location on the map is the center point of the affected customers not the actual predicted device location. The customers affected is all customers that are electrically connected to the device predicted of causing that outage event.” Points that have a “clustered” value for its POINTTYPE (Point Type) field indicates that there are multiple outage orders for that same location; the number being indicated in the NUMPTSOUT (Number of Outage Orders) field. A typical scenario for this is when there are multiple transformers having service disruptions that are co-located on a utility pole but can occur when two outage points are near enough to each other. A clustered point’s ETR (Estimated Restoration) field value is the most conservative one of all the outage orders at that shared location. These points are being clipped to the DC boundary; points falling outside of DC’s boundary may be causing outages within the District but will not be displayed. Credits The data are coming from the public facing Pepco power outage map and used by the District of Columbia with permission for emergency management purposes. Use Limitations Emergency management.

Map Name: Layers

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Copyright Text: PEPCO and DC GIS

Spatial Reference: 26985 (26985)

Single Fused Map Cache: false

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Full Extent:

Units: esriMeters

Supported Image Format Types: PNG32,PNG24,PNG,JPG,DIB,TIFF,EMF,PS,PDF,GIF,SVG,SVGZ,BMP

Document Info:

Supports Dynamic Layers: false

MaxRecordCount: 1000

MaxImageHeight: 4096

MaxImageWidth: 4096

Supported Query Formats: JSON, geoJSON, PBF

Supports Query Data Elements: true

Min Scale: 0

Max Scale: 0

Supports Datum Transformation: true

Child Resources: Info

Supported Operations: Export Map Identify QueryLegends QueryDomains Find Return Updates Generate KML