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TwitterLiverpool City Council has device counters deployed throughout the city centre to understand how the city is used. Data in this dashboard is derived from Meshed nCounters and CCTV retrofitted with an algorithm developed by University of Wollongong. The algorithm has used machine learning to identify objects in the camera viewfinder and categorise them into pedestrian, vehicle or bicycle. This transforms the CCTV into visual sensors. The nCounters generate data by counting the number of Wi-Fi signals emitted by non-identifiable mobile devices within a specified proximity and performing certain filtering and processing. No individuals are identified by either method. Both methods have advantages and limitations.Advantages of the nCounter method:Can provide insightful data on crowd sizes and individualsAn individual with one device will be counted onceThe nCounter can report the average ‘dwell time’ of the deviceLimitations of the nCounter method:individuals without devices will not be counted (for example young children or people without smart phones),if someone is carrying a smart phone which is in aeroplane mode or switched off then it will not be counted, andindividuals with multiple devices will be counted by the number of devices they have. For example, one person may have two smart phones, therefore the individual will be counted more than once.The visual sensors (CCTV) count the number of bicycles, people and vehicles in the location.Advantages of the visual sensor method:Can provide insightful data on pedestrian, vehicle and bicycle numbers,Re-uses existing common technology on city streets without further visual clutter,Does not rely on individuals carrying their own devices, so useful in areas with lower technology uptakeLimitations of visual sensor method:Individuals can be counted multiple times as they exit and re-enter the camera viewfinder,The machine learning cannot differentiate between bicycles and motorbikesData collected in this dataset can be visualised in the Liverpool City Centre activity dashboard
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TwitterWard boundaries for Liverpol. Used as web look up to identify councillor details pre ward. map overlay.
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TwitterOpen Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
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Ward boundaries for Liverpool. Upon accessing this Licensed Data you will be deemed to have accepted the terms of the Open Government Licence for Public Sector Information
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TwitterOpen Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
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TwitterDataset shows boundaries of Liverpool City boundaries. held as part of Liverpool geodata holding. used on internet/intranet.
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TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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This data shows the pick-up days for waste across Liverpool City Council local government area.
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TwitterMetadata Portal Metadata Information
| Content Title | Liverpool Demolition DA in 3yrs Public |
| Content Type | Hosted Feature Layer |
| Description | The dataset shows the location and demolition information of DA in the Liverpool Local Government Area in the past 3 years. |
| Initial Publication Date | 26/10/2021 |
| Data Currency | 06/02/2024 |
| Data Update Frequency | API |
| Content Source | API |
| File Type | Web Feature Service |
| Attribution | |
| Data Theme, Classification or Relationship to other Datasets | |
| Accuracy | |
| Spatial Reference System (dataset) | WGS84 |
| Spatial Reference System (web service) | EPSG:4326 |
| WGS84 Equivalent To | GDA94 |
| Spatial Extent | |
| Content Lineage | |
| Data Classification | Unclassified |
| Data Access Policy | Open |
| Data Quality | |
| Terms and Conditions | Creative Commons |
| Standard and Specification | |
| Data Custodian | Liverpool City Council |
| Point of Contact | Liverpool City Council https:/www.liverpool.nsw.gov.au/ |
| Data Aggregator | |
| Data Distributor | |
| Additional Supporting Information | |
| TRIM Number |
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TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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A map of the high ecological value waterways and water dependent ecosystems for the Liverpool LGA was prepared by the Science Division of the Department of Planning, Industry and Environment, with input layers and support for the map provided by the former NSW Department of Primary Industries-Fisheries and Department of Industry-Crown Lands and Water. The basis for the map arises from Science Division inputs to strategic planning processes. For example, the map has been included in the Land Use and Infrastructure Implementation Plans for the Wilton (Wilton2040, page 22) and Greater Macarthur (interim plan, page 36) Priority Growth Areas. This map was specifically developed for input to Local Government Local Strategic Planning Statements to support Council’s delivery of Strategy 25.1 of the Greater Sydney Region Plan. The map shows areas where waterways and water dependent ecosystems are defined as high ecological value, based on definitions, guidelines and policies under the Environment Protection and Biodiversity Conservation Act 1999, Biodiversity Conservation Act 2016, Fisheries Management Act 1994 and Water Management Act 2000. Water dependent ecosystems are defined as wetlands, and flora and fauna that rely on water sources (including groundwater). The map represents an overlay of 39 indicators being used by the State Government to define high value, however, not all 39 indicators will be present in any one LGA (see Attachment 1 below). For example, there are 24 indicators making up the map for the Liverpool LGA. It should also be noted that the individual indicators have not been ground-truthed and it is recommended that field assessments and/or a comparison to local mapping be undertaken prior to any decisions being made. The map was created by initially placing a 1 ha (to correspond with a lot size) hexagon grid over the LGA, and attributing the grid with the area, length and/or frequency of occurrence of high value water dependent ecosystems. The purpose of the map is to identify strategic planning priorities for protecting and improving the health of high value waterways and water dependent ecosystems in the LGA. Once identified, the priorities can be used as a basis for identifying aquatic biodiversity refugia, stream rehabilitation efforts and setting management targets and/or land use planning controls that would protect or improve the health of waterways and water dependent ecosystems so they provide the essential services and functions expected of a cool blue-green corridor.
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TwitterOpen Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
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🇬🇧 영국 English Liverpool City Council dataset defining areas of special architectural interest, the character or appearance of which is desirable to preserve or enhance. Upon accessing this Licensed Data you will be deemed to have accepted the terms of the Open Government Licence for Public Sector Information
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TwitterSource: https://data.liverpool.nsw.gov.au/explore/dataset/liverpool-cbd-car-park-map/information/
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TwitterThis data shows demolition information in the Liverpool Local Government Area over the past three years.
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TwitterThis is an interactive map showing location of available parking within Liverpool City Centre. Relevant information is displayed when clicked on parking or parking meter symbol on the map.
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TwitterThis information is provided by Liverpool City Council.
Liverpool City has almost 5000 car spaces available in or near the city centre. They include free and low-cost options, short and long-stay.
Use the Go to Resource to view the data source.
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TwitterThe Liverpool Overland Flow Path Study Stage 1 (Stage 1 study) was completed in 2006 by SMEC Australia Pty Ltd (SMEC). The primary objective of this study was to identify the overland flow paths within the entire Liverpool Local Government Area (LGA), a total area of some 317 square kilometres. In this context, overland flooding is considered separate from mainstream flooding within the LGA which has been investigated in most of the major catchments in the LGA including the Georges River, Show full descriptionThe Liverpool Overland Flow Path Study Stage 1 (Stage 1 study) was completed in 2006 by SMEC Australia Pty Ltd (SMEC). The primary objective of this study was to identify the overland flow paths within the entire Liverpool Local Government Area (LGA), a total area of some 317 square kilometres. In this context, overland flooding is considered separate from mainstream flooding within the LGA which has been investigated in most of the major catchments in the LGA including the Georges River, Cabramatta Creek, Anzac Creek, Kemps Creek, South Creek, Badgerys Creek and the Nepean River. The Stage 1 study represents the first significant attempt to map major overland flow paths within the LGA and quantify associated flood risk to property. By defining the major flood behaviour for overland flooding across the LGA, sub-catchments were ranked in terms of flood risk and prioritised for further detailed assessment. This preliminary options report identifies the areas of the Liverpool study catchments most susceptible to overland flooding, and identifies potential mitigation options to alleviate or reduce the flood risk: Section 1 introduces the study. Section 2 provides background information to the study. Section 3 outlines the community consultation program undertaken. Section 4 details the modelling approach and presents design flood results. Section 5 provides a summary of the flood damages assessment. Section 6 provides a review of potential floodplain management options. Section 7 details the assessment of structural options to assess “hot-spot” areas.
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TwitterThis information is provided by Liverpool City Council.
Liverpool City has almost 5000 car spaces available in or near the city centre. They include free and low-cost options, short and long-stay.
Use the Go to Resource to view the data source.
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TwitterBOUNDARY OF AREA DESIGNATED AS A CONTROLLED SMOKE ZONE FOR LIVERPOOL CITY REGION
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TwitterLiverpool City Council (Council) has recently undertaken the Liverpool Overland Flow Study Stage 1\r (2006) which investigated overland flood risk across the entire Liverpool LGA. The study identified\r and mapped sub-catchments and overland flow paths and prioritised them in terms of flood hazard\r for future detailed study.\r \r Stage 2 of the Overland Flow Path Study, the current study, is focused on the detailed investigation\r of flooding in three of the highest priority areas identified in Stage 1. The study has been\r commissioned by Liverpool City Council with funding assistance from the NSW Department of\r Environment and Climate Change (DECC).\r The three study areas are shown in Figure 1.1 and comprise:\r \r * Area 1 - covering part or all of the suburbs of Heckenberg, Mount Pritchard, Ashcroft, Sadleir, Busby, Miller, Hinchinbrook and Green Valley;\r * Area 2 - covering part or all of the suburbs of Prestons, Lurnea and Cartwright; and\r * Area 3 - covering part or all of the suburbs of Prestons and Horningsea Park.\r \r Area 1 is situated partly within the Cabramatta Creek catchment and partly within the Green Valley\r Creek catchment. Area 2 is situated within the Maxwells Creek catchment (itself a tributary of\r Cabramatta Creek). Area 3 is situated in the Cabramatta Creek catchment also. Detailed locality\r plans for each study area are provided in Sections 5 to 7.\r
The objectives of the study are to characterise existing flooding behaviour in the catchment, identify\r significant overland flow paths and flooding locations, and to propose mitigation options to reduce\r existing flood risk.\r Specifically the study aims to:\r \r * Define overland flow behaviour within the study area including flow rates, velocities, water depths\r and extent for the 5% AEP, 1% AEP and PMF events;\r * Produce high quality mapping describing the catchment flooding;\r * Quantify probable flood damages on the basis of expected flood inundation patterns; and\r * Identify potential flood mitigation options that could be implemented to reduce the flood risk.\r \r The primary objective of the study is to examine and define the overland flooding behaviour in the\r study area. This has involved the development of computer models to simulate design flood\r conditions. The results of the modelling have enabled a preliminary assessment of flood risk and\r potential damages and the identification of potential mitigation options. It is expected that these risks\r and mitigation options would be explored in further detail in floodplain management investigations\r subsequent to this flood study.
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TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Smart Cities, Smart Liverpool, Smart Pedestrian Project Thousands of people walk through the Liverpool city centre every day and, through the Smart Pedestrian Project, the paths they take will help shape the city’s future. Liverpool City Council is counting pedestrian and vehicle movements around the city centre, collecting data from smart devices and camera-counting technology. The data is stripped of any identification and relayed and collected for analysis by researchers from the University of Wollongong. Liverpool City Council now uses this data to inform planning decisions and respond to the rising number of residents and workers making their way around the city centre every day. Liverpool City Council secured an Australian Government Smart Cities and Suburbs grant to jointly fund the project. Liverpool City Council worked with IT Integration Company Meshed and the University of Wollongong to deliver the technology. Meshed supplied the Low Power Wide Area Network and developed a Wi-Fi smart device counter. The university developed the people and vehicle-counting technology and data analysis. The technology makes use of the city’s existing CCTV cameras to capture images. No images are transmitted over the network, so there is no risk to privacy. This dataset lists the API endpoint URLs to retrieve data for the smart pedestrian project. A dashboard to view the data can be found at https://pavo.its.uow.edu.au/ Please read the API documentation as you need to provide the parameters to make any API calls. An example has been provided below that lists all the visual sensors. The API is divided in two sections: sensors, and readings (data gathered by sensors). Unless otherwise noted: * all datetimes are ISO formatted * all responses are JSON-formatted
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TwitterLiverpool City Council has device counters deployed throughout the city centre to understand how the city is used. Data in this dashboard is derived from Meshed nCounters and CCTV retrofitted with an algorithm developed by University of Wollongong. The algorithm has used machine learning to identify objects in the camera viewfinder and categorise them into pedestrian, vehicle or bicycle. This transforms the CCTV into visual sensors. The nCounters generate data by counting the number of Wi-Fi signals emitted by non-identifiable mobile devices within a specified proximity and performing certain filtering and processing. No individuals are identified by either method. Both methods have advantages and limitations.Advantages of the nCounter method:Can provide insightful data on crowd sizes and individualsAn individual with one device will be counted onceThe nCounter can report the average ‘dwell time’ of the deviceLimitations of the nCounter method:individuals without devices will not be counted (for example young children or people without smart phones),if someone is carrying a smart phone which is in aeroplane mode or switched off then it will not be counted, andindividuals with multiple devices will be counted by the number of devices they have. For example, one person may have two smart phones, therefore the individual will be counted more than once.The visual sensors (CCTV) count the number of bicycles, people and vehicles in the location.Advantages of the visual sensor method:Can provide insightful data on pedestrian, vehicle and bicycle numbers,Re-uses existing common technology on city streets without further visual clutter,Does not rely on individuals carrying their own devices, so useful in areas with lower technology uptakeLimitations of visual sensor method:Individuals can be counted multiple times as they exit and re-enter the camera viewfinder,The machine learning cannot differentiate between bicycles and motorbikesData collected in this dataset can be visualised in the Liverpool City Centre activity dashboard