Visualization of scientific results using networks has become popular in scientometric research. We provide base maps for Mendeley reader count data using the publication year 2012 and Web of Science data. Example networks are shown and explained. The reader can use our base maps to visualize other results with the VOSViewer. The proposed overlay maps are able to show the impact of publications in terms of readership data. The advantage of using our base maps is that the user does not have to produce a network based on all data (e.g. from one year), but can collect the Mendeley data for a single institution (or journals, topics) and can match them with our already produced information. Generation of such large-scale networks is still a demanding task despite the available computer power and digital data availability. Therefore, it is very useful to have base maps and create the network with the overlay technique.

The USGS National Hydrography Dataset (NHD) Downloadable Data Collection from The National Map (TNM) is a comprehensive set of digital spatial data that encodes information about naturally occurring and constructed bodies of surface water (lakes, ponds, and reservoirs), paths through which water flows (canals, ditches, streams, and rivers), and related entities such as point features (springs, wells, stream gages, and dams). The information encoded about these features includes classification and other characteristics, delineation, geographic name, position and related measures, a "reach code" through which other information can be related to the NHD, and the direction of water flow. The network of reach codes delineating water and transported material flow allows users to trace movement in upstream and downstream directions. In addition to this geographic information, the dataset contains metadata that supports the exchange of future updates and improvements to the data. The NHD supports many applications, such as making maps, geocoding observations, flow modeling, data maintenance, and stewardship. For additional information on NHD, go to http://nhd.usgs.gov/.

This data includes the Town of Warren Fluvial Erosion Hazard Overlay District as part of their zoning regulations. The data represents the area that is vulnerable to fluvial erosion from flooding events and is based on data collected during the Mad River geomorphic assessment. This overlay district was adopted in 2014.

Intended Purpose:Polygon layer of area affected by Inner Air Noise Control Overlay created for the Wellington City Council District Plan. Abbreviations/Acronyms:ePlan - "Electronic Plan" the web version of the District PlanPDP - Proposed District PlanIHP - Independent Hearings PanelWCC - Wellington City Council Refresh Rate (Data only):Static Ownership:This data is owned by WCC District Planning Team, contact District.Plan@wcc.govt.nz for questions about this layer and its appropriate use cases. Ownership specifies legal or administrative control over the content. Stewardship:This data is maintained by WCC City Insights Team, contact cityinsightsgis@wcc.govt.nz for information about the creation of this layer and its maintenance. Custodianship:This data is maintained by WCC City Insights Team, contact cityinsightsgis@wcc.govt.nz for information about the creation of this layer and its maintenance. Stewardship addresses the ongoing care, maintenance, and management of the content.Authoritative Data Sources (Data only):An overlay spatially identifies distinctive values, risks or other factors that require management. Further data changes have been made as part of the District Plan Review Process. Summary of Data Collection (Data only):The management of noise and vibration associated with transport (e.g. aircrafts, railway etc.) and entertainment occurring within Wellington City is intrinsically linked to the quality of the environment surrounding those areas. Noise ranks highly on the list of environmental pollutants. It can have an adverse effect on health and amenity values, can interfere with communication and can disturb peoples sleep and concentration. It is commonly identified as a nuisance and is the subject of frequent complaints received by council. Under the Resource Management Act 1991 (RMA), noise includes vibration. The Noise Control Overlay in the PDP was created by the WCC District Plan team following the National Planning Standards (https://environment.govt.nz/publications/national-planning-standards/). The boundaries were subsequently modified as part of the District Plan Review Process.

Dataset ReadMe

Overview

• This data was collected to study the structural properties of blockchain overlay networks. It contains several snapshots of seven different networks from the period 26 Jun 2020 to 20 July 2020.
• The networks included are (alphabetically): Bitcoin, Bitcoin Cash, Dash, Dogecoin, Ethereum, Litecoin, and ZCash.

• Studying the graph characteristics of these networks is beneficial;

  • It helps us evaluate the system's performance, robustness, and scalability by examining the network structure, node distribution, and communication patterns.
  • This analysis helps us identify bottlenecks and find ways to optimize efficiency and throughput.

  Moreover, understanding the vulnerabilities and attack possibilities unique to these networks allows us to develop proactive defense mechanisms and mitigate potential threats.

Data collection method: ask all reachable nodes continuously for their known peers. In Bitcoin's parlor, we send GETADDR messages and store all ADDR replies, drawing a connection between the sending node to all ip addresses contained in the ADDR message.

Data Description

All IP addresses have been replaced by numbers (NodeID) for ethical reasons. NodeIDs are consistent accross all files. The same NodeID corresponds to the same ip in ALL files (if present). Filenames contain the timestamp and the corresponding network. The date-time format is YYYYMMDD-HHMISS.

• File Contents: The edgelist files store information about the structure of the connectivity graph. Each file represents an edgelist of a graph at the specified time-stamp. Each line in a file corresponds the the list of known peers to a node. The NodeID of the node is the first number of each line. Example: the following line

  S N1 N2 N3 N4

  means that node S knows of nodes N1..N4; their ip addresses were included in S's ADDR responses.

To process the files in snap and networkx proper transformations have to be made. Please read the relevant documentation to find the appropriate input.

Research

This dataset has been used in the following works: - @inproceedings{aris_ssec,
author = {Paphitis, Aristodemos and Kourtellis, Nicolas and Sirivianos, Michael}, title = {Graph Analysis of Blockchain {P2P} Overlays and their Security Implications}, booktitle = {Proceedings of the 9th International Symposium on Security and Privacy in Social Networks and Big Data (SocialSec 2023)}, series = {Lecture Notes in Computer Science}, volume = {13983}, publisher = {Springer Nature}, year = {2023}, }

• @inproceedings{aris_nss,
  author = {Paphitis, Aristodemos and Kourtellis, Nicolas and Sirivianos, Michael}, title = {Resilience of Blockchain Overlay Networks}, booktitle = {Proceedings of the 17th International Conference on Network and System Security (NSS 2023)},
  series = {Lecture Notes in Computer Science}, volume = {14097}, publisher = {Springer Nature}, year = {2023}, }

License and Attribution

• You are free to share and adapt according to CC BY (4.0) https://creativecommons.org/licenses/by/4.0/

• Please cite as:

  Aristodemos Paphitis, Nicolas Kourtellis, and Michael Sirivianos. A First Look into the Structural Properties of Blockchain P2P Overlays. DOI:https://doi.org/10.6084/m9.figshare.23522919

• bibtex:

  @misc{paphitis_first_nodate,
  author = {Paphitis, Aristodemos and Kourtellis, Nicolas and Sirivianos, Michael}, title = {A First Look into the Structural Properties of Blockchain {P2P} Overlays}, howpublished = {Public dataset with figshare}, doi = {10.6084/m9.figshare.23522919}, }

Contact Information

• Aris Paphitis: am.paphitis@edu.cut.ac.cy
• https://netsysci.cut.ac.cy/aristodemos.paphitis/

MicroBooNE samples are provided for collaborative development in two different formats: HDF5, targeting the broadest audience, and artroot, targeting users that are familiar with the software infrastructure of Fermilab neutrino experiments and more in general of HEP experiments. The HDF5 files are stored on Zenodo, together with a list of artroot files accessible with xrootd.

This sample includes simulated interactions of neutrinos from the Booster Neutrino Beam (BNB), overlaid on top of cosmic ray data. The sample is inclusive, i.e. it includes all types of neutrinos and interactions, with relative abundance matching our nominal flux and cross section models. Interactions are simulated in in the whole cryostat volume.

The HDF5 files in this sample do not include the information at the wire waveform level ("NoWire" label), allowing for larger number of events to be included in the data set.

More documentation, including detailed description of content, recipes, and example usage, at https://github.com/uboone/OpenSamples.

Suggested text for acknowledgment is the following: We acknowledge the MicroBooNE Collaboration for making publicly available the data sets [data set DOIs] employed in this work. These data sets consist of simulated neutrino interactions from the Booster Neutrino Beamline overlaid on top of cosmic data collected with the MicroBooNE detector [2017 JINST 12 P02017].

In addition, we request that software products resulting from the usage of the datasets are also made publicly available.

[Superseded] This dataset is a single layer from [Superseded] City Plan 2014 – v15.00–2019 collection. Not all layers were updated in this amendment, for more information on …Show full description[Superseded] This dataset is a single layer from [Superseded] City Plan 2014 – v15.00–2019 collection. Not all layers were updated in this amendment, for more information on past Adopted City Plan amendments. This feature class is shown on the Wetlands overlay map (map reference: OM-023.3).This feature class includes the following sub-categories:(a) Wetland sub-categoryFor information about the overlay and how it is applied, please refer to the Brisbane City Plan 2014 document.Symbolisation may be downgraded due to constraints imposed by feature service capabilitiesThis dataset utilises Brisbane City Council's Open Spatial Data website to provide additional features for viewing and downloading the data.The first resource is in HTML format. The GO TO button will launch our Open Spatial Data website and this will let you preview the data and enable additional download options. The resources labelled GeoJSON, KML and SHP will give you a download of the entire dataset. The ESRI REST resource connects to metadata for the layer while the CSV resource will download attribute data in a table. For more information on the new features and other tips and tricks please read our Blog.

[Superseded] This dataset is a single layer from [Superseded] City Plan 2014 – v16.00–2019 collection. Not all layers were updated in this amendment, for more information on past Adopted City Plan amendments. This feature class is shown on the Airport environs overlay map - Light intensity (map reference: OM-001.5).This feature class includes the following sub-categories:(a) Light intensity …Show full description[Superseded] This dataset is a single layer from [Superseded] City Plan 2014 – v16.00–2019 collection. Not all layers were updated in this amendment, for more information on past Adopted City Plan amendments. This feature class is shown on the Airport environs overlay map - Light intensity (map reference: OM-001.5).This feature class includes the following sub-categories:(a) Light intensity sub-categories;(i) Zone A - 0 candela - 600m wide 1000m from runway strip sub-category;(ii) Zone B - 50 candela - 900m wide 2000m from runway strip sub-category;(iii) Zone C - 150 candela - 1200m wide 3000m from runway strip sub-category;(iv) Zone D - 450 candela - 1500m wide 4500m from runway strip sub-category;(v) within 6km - Max intensity of light sources 3 degrees above horizon sub-category.For information about the overlay and how it is applied, please refer to the Brisbane City Plan 2014 document. Additional information to assist with cross referencing the Airport environs overlay datasets is available in the City Plan 2014 — Airport Environs overlay — reference dataset on Open Data website.The Airport environs overlays contain information derived from data that is created or owned by BAC and licensed to Brisbane City Council. Its use by any person for purposes not associated with planning and development in Brisbane is not authorised.This dataset utilises Brisbane City Council's Open Spatial Data website to provide additional features for viewing and downloading the data.The first resource is in HTML format. The GO TO button will launch our Open Spatial Data website and this will let you preview the data and enable additional download options. The resources labelled GeoJSON, KML and SHP will give you a download of the entire dataset. The ESRI REST resource connects to metadata for the layer while the CSV resource will download attribute data in a table. For more information on the new features and other tips and tricks please read our Blog.

This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.

This dataset contains polygon features representing the Chesapeake Bay Preservation Areas.

Data collected and complied by Department of City Planning maintained on as needed basis by the Department of City Planning.

Any land designated by the city pursuant to Part III of the Chesapeake Bay Preservation Area Designation and Management Regulations, 9 VAC 10-20-70, and section 10.1-2107 of the Code of Virginia. A Chesapeake Bay Preservation Area shall consist of a resource protection area and a resource management area.

The Chesapeake Bay and its tributaries constitute an important and productive estuarine system, providing economic and social benefits to the citizens of the City of Norfolk and the Commonwealth of Virginia. The health of the Chesapeake Bay is vital to maintaining the city's economy and the welfare of its citizens. The intent of the city and the purposes of the Overlay District are to: (1) protect existing high quality state waters; (2) restore all other state waters to a condition or quality that will permit all reasonable public uses and will support the propagation and growth of all aquatic life, including game fish, which might reasonably be expected to inhabit them; (3) safeguard the clean waters of the Commonwealth from pollution; (4) prevent any increase in pollution; (5) reduce existing pollution; and (6) promote water resource conservation in order to provide for the health, safety, and welfare of the present and future citizens of the city.

Any and all data sets are for graphical representations only and should not be used for legal purposes. Any determination of topography or contours, or any depiction of physical improvements, property lines or boundaries is for general information only and shall not be used for the design, modification, or construction of improvement to real property or for flood plain determination.

The dataset can be available using the link:https://norfolkgisdata-orf.opendata.arcgis.com/datasets/712ae93509fb4bb6947bab945d30bd77_0/about

This study was designed to develop crime forecasting as an application area for police in support of tactical deployment of resources. Data on crime offense reports and computer aided dispatch (CAD) drug calls and shots fired calls were collected from the Pittsburgh, Pennsylvania Bureau of Police for the years 1990 through 2001. Data on crime offense reports were collected from the Rochester, New York Police Department from January 1991 through December 2001. The Rochester CAD drug calls and shots fired calls were collected from January 1993 through May 2001. A total of 1,643,828 records (769,293 crime offense and 874,535 CAD) were collected from Pittsburgh, while 538,893 records (530,050 crime offense and 8,843 CAD) were collected from Rochester. ArcView 3.3 and GDT Dynamap 2000 Street centerline maps were used to address match the data, with some of the Pittsburgh data being cleaned to fix obvious errors and increase address match percentages. A SAS program was used to eliminate duplicate CAD calls based on time and location of the calls. For the 1990 through 1999 Pittsburgh crime offense data, the address match rate was 91 percent. The match rate for the 2000 through 2001 Pittsburgh crime offense data was 72 percent. The Pittsburgh CAD data address match rate for 1990 through 1999 was 85 percent, while for 2000 through 2001 the match rate was 100 percent because the new CAD system supplied incident coordinates. The address match rates for the Rochester crime offenses data was 96 percent, and 95 percent for the CAD data. Spatial overlay in ArcView was used to add geographic area identifiers for each data point: precinct, car beat, car beat plus, and 1990 Census tract. The crimes included for both Pittsburgh and Rochester were aggravated assault, arson, burglary, criminal mischief, misconduct, family violence, gambling, larceny, liquor law violations, motor vehicle theft, murder/manslaughter, prostitution, public drunkenness, rape, robbery, simple assaults, trespassing, vandalism, weapons, CAD drugs, and CAD shots fired.

The VIKUS Viewer (overlay extension) is an adaption of VIKUS Viewer, a web-based visualization system for the dynamic, interactive visualization of metadata (originally on cultural heritage data) that allows for the exploration of thematic and temporal patterns of large collections. The extension aims at the support of multi-media data that occurs in spoken language corpora (esp. audio and video). VIKUS Viewer was designed and developed by Christopher Pietsch. The VIKUS Viewer software is based on the visualization code behind Past Visions, a collaborative effort by Katrin Glinka, Christopher Pietsch, and Marian Dörk carried out at the University of Applied Sciences Potsdam in the context of the Urban Complexity Lab during the research project VIKUS (2014-2017). Related Paper: Past Visions and Reconciling Views. The T-SNE view has been implemented for the Sphaera project with funding from Chronoi-REM {"references": ["Ferger, A.; Jettka, D. (2020). Multi-medial Corpora of Indigenous Languages from a Cultural Collections Perspective. 3rd International Congress of Computational and Corpus Linguistics (CILCC-2020), virtual event, 21.-23.10.2020. https://cilcc20.files.wordpress.com/2020/11/libro-de-resumenes-actas-iii-cilcc-2020-y-v-wopatec-2020-virtual.pdf#page=247"]}

[Superseded] This dataset is a single layer from [Superseded] City Plan 2014 – v16.00–2019 collection. Not all layers were updated in this amendment, for more information on past

           Adopted City Plan amendments.
           This feature class is shown on:(1) Airport environs overlay map - Obstacle Limitation Surfaces map (map reference: OM-001.2).- [Superseded]
           This dataset is a single layer from [Superseded] City Plan 2014 – v16.00–2019 collection. Not all layers were updated in this amendment, for more information on past

           Adopted City Plan amendments.
           This feature class is shown as the Obstacle Limitation Surfaces (OLS) sub-category: runway centreline sub-category.(2) Airport environs overlay map - Procedures for Air Navigation Services-Aircraft Operations Surfaces (map reference: OM-001.3).- [Superseded]
           This dataset is a single layer from [Superseded] City Plan 2014 – v16.00–2019 collection. Not all layers were updated in this amendment, for more information on past

           Adopted City Plan amendments.
           This feature class is shown as the Runway centreline map layer.For information about the overlay and how it is applied, please refer to the Brisbane City Plan 2014 document. Additional information to assist with cross referencing the Airport environs overlay datasets is available in the City Plan 2014 — Airport Environs overlay — reference dataset on Open Data website.The Airport environs overlays contain information derived from data that is created or owned by BAC and licensed to Brisbane City Council. Its use by any person for purposes not associated with planning and development in Brisbane is not authorised.

Background: Malaria continues to pose a major public health challenge in tropical regions. Despite significant efforts to control malaria in Tanzania, there are still residual transmission cases. Unfortunately, little is known about where these residual malaria transmission cases occur and how they spread. In Tanzania, for example, the transmission is heterogeneously distributed. In order to effectively control and prevent the spread of malaria, it is essential to understand the spatial distribution and transmission patterns of the disease. This study seeks to predict areas that are at high risk of malaria transmission so that intervention measures can be developed to accelerate malaria elimination efforts.

Methods: This study employs a geospatial-based model to predict and map out malaria risk area in Kilombero Valley. Environmental factors related to malaria transmission were considered and assigned valuable weights in the Analytic Hierarchy Process (AHP), an online system using a pairwise comparison technique. The malaria hazard map was generated by a weighted overlay of the altitude, slope, curvature, aspect, rainfall distribution, and distance to streams in Geographic Information Systems (GIS). Finally, the risk map was created by overlaying components of malaria risk including hazards, elements at risk, and vulnerability. Results: The study demonstrates that the majority of the study area falls under the moderate-risk level (61%), followed by the low-risk level (31%), while the high-malaria risk area covers a small area, which occupies only 8% of the total area. Conclusion: The findings of this study are crucial for developing spatially targeted interventions against malaria transmission in residual transmission settings. Predicted areas prone to malaria risk provide information that will inform decision-makers and policymakers for proper planning, monitoring, and deployment of interventions. Methods Data acquisition and description The study employed both primary and secondary data, which were collected from numerous sources based on the input required for the implementation of the predictive model. Data collected includes the locations of all public and private health centers that were downloaded free from the health portal of the United Republic of Tanzania, Ministry of Health, Community Development, Gender, Elderly, and Children, through the universal resource locator (URL) (http://moh.go.tz/hfrportal/). Human population data was collected from the 2012 population housing census (PHC) for the United Republic of Tanzania report. Rainfall data were obtained from two local offices; Kilombero Agricultural Training and Research Institute (KATRIN) and Kilombero Valley Teak Company (KVTC). These offices collect meteorological data for agricultural purposes. Monthly data from 2012 to 2017 provided from thirteen (13) weather stations. Road and stream network shapefiles were downloaded free from the MapCruzin website via URL (https://mapcruzin.com/free-tanzania-arcgis-maps-shapefiles.htm). With respect to the size of the study area, five neighboring scenes of the Landsat 8 OLI/TIRS images (path/row: 167/65, 167/66, 167/67, 168/66 and 168/67) were downloaded freely from the United States Geological Survey (USGS) website via URL: http://earthexplorer.usgs.gov. From July to November 2017, the images were selected and downloaded from the USGS Earth Explorer archive based on the lowest amount of cloud cover coverage as viewed from the archive before downloading. Finally, the digital elevation data with a spatial resolution of three arc-seconds (90m by 90m) using WGS 84 datum and the Geographic Coordinate System were downloaded free from the Shuttle Radar Topography Mission (SRTM) via URL (https://dds.cr.usgs.gov/srtm/version2_1/SRTM3/Africa/). Only six tiles that fall in the study area were downloaded, coded tiles as S08E035, S09E035, S10E035, S08E036, S09E036, S10E036, S08E037, S09E037 and S10E037. Preparation and Creation of Model Factor Parameters Creation of Elevation Factor All six coded tiles were imported into the GIS environment for further analysis. Data management tools, with raster/raster data set/mosaic to new raster feature, were used to join the tiles and form an elevation map layer. Using the spatial analyst tool/reclassify feature, the generated elevation map was then classified into five classes as 109–358, 359–530, 531–747, 748–1017 and >1018 m.a.s.l. and new values were assigned for each class as 1, 2, 3, 4 and 5, respectively, with regards to the relationship with mosquito distribution and malaria risk. Finally, the elevation map based on malaria risk level is levelled as very high, high, moderate, low and very low respectively. Creation of Slope Factor A slope map was created from the generated elevation map layer, using a spatial analysis tool/surface/slope feature. Also, the slope raster layer was further reclassified into five subgroups based on predefined slope classes using standard classification schemes, namely quantiles as 0–0.58, 0.59–2.90, 2.91–6.40, 6.41–14.54 and >14.54. This classification scheme divides the range of attribute values into equal-sized sub-ranges, which allow specifying the number of the intervals while the system determines where the breaks should be. The reclassified slope raster layer subgroups were ranked 1, 2, 3, 4 and 5 according to the degree of suitability for malaria incidence in the locality. To elaborate, the steeper slope values are related to lesser malaria hazards, and the gentler slopes are highly susceptible to malaria incidences. Finally, the slope map based on malaria risk level is leveled as very high, high, moderate, low and very low respectively. Creation of Curvature Factor Curvature is another topographical factor that was created from the generated elevation map using the spatial analysis tool/surface/curvature feature. The curvature raster layer was further reclassified into five subgroups based on predefined curvature class. The reclassified curvature raster layer subgroups were ranked to 1, 2, 3, 4 and 5 according to their degree of suitability for malaria occurrence. To explain, this affects the acceleration and deceleration of flow across the surface. A negative value indicates that the surface is upwardly convex, and flow will be decelerated, which is related to being highly susceptible to malaria incidences. A positive profile indicates that the surface is upwardly concave and the flow will be accelerated which is related to a lesser malaria hazard, while a value of zero indicates that the surface is linear and related to a moderate malaria hazard. Lastly, the curvature map based on malaria risk level is leveled as very high, high, moderate, low, and very low respectively.
Creation of Aspect Factor As a topographic factor associated with mosquito larval habitat formation, aspect determines the amount of sunlight an area receives. The more sunlight received the stronger the influence on temperature, which may affect mosquito larval survival. The aspect of the study area also was generated from the elevation map using spatial analyst tools/ raster /surface /aspect feature. The aspect raster layer was further reclassified into five subgroups based on predefined aspect class. The reclassified aspect raster layer subgroups were ranked as 1, 2, 3, 4 and 5 according to the degree of suitability for malaria incidence, and new values were re-assigned in order of malaria hazard rating. Finally, the aspect map based on malaria risk level is leveled as very high, high, moderate, low, and very low, respectively. Creation of Human Population Distribution Factor Human population data was used to generate a population distribution map related to malaria occurrence. Kilombero Valley has a total of 42 wards, the data was organized in Ms excel 2016 and imported into the GIS environment for the analysis, Inverse Distance Weighted (IDW) interpolation in the spatial analyst tool was applied to interpolate the population distribution map. The population distribution map was further reclassified into five subgroups based on potential to malaria risk. The reclassified map layer subgroups were ranked according to the vulnerability to malaria incidence in the locality such as areas having high population having the highest vulnerability and the less population having less vulnerable, and the new value was assigned as 1, 2, 3, 4 and 5, and then leveled as very high, high, moderate, low and very low malaria risk level, respectively. Creation of Proximity to Health Facilities Factor The distribution of health facilities has a significant impact on the malaria vulnerability of the population dwellings in the Kilombero Valley. The health facility layer was created by computing distance analysis using proximity multiple ring buffer features in spatial analyst tool/multiple ring buffer. Then the map layer was reclassified into five sub-layers such as within (0–5) km, (5.1–10) km, (10.1–20) km, (20.1–50) km and >50km. According to a WHO report, it is indicated that the human population who live nearby or easily accessible to health facilities is less vulnerable to malaria incidence than the ones who are very far from the health facilities due to the distance limitation for the health services. Later on, the new values were assigned as 1, 2, 3, 4 and 5, and then reclassified as very high, high, moderate, low and very low malaria risk levels, respectively. Creation of Proximity to Road Network Factor The distance to the road network is also a significant factor, as it can be used as an estimation of the access to present healthcare facilities in the area. Buffer zones were calculated on the path of the road to determine the effect of the road on malaria prevalence. The road shapefile of the study area was inputted into GIS environment and spatial analyst tools / multiple ring buffer feature were used to generate five buffer zones with the

Abstract

The Opportunity Atlas has collected contextual data by county and tract. Rather than providing contextual socioeconomic data of where people currently live, the data represents average socioeconomic indicators (e.g., earnings) of where people grew up.

Documentation

A core element of Population Health Science is that health outcomes can only be fully understood when they are studied within their context. Therefore, we have a copy of The Opportunity Atlas, a dataset that provides socioeconomic data by county and tract.

Several studies have shown that especially childhood neighborhoods drive adult outcomes and that residential areas lived in through adulthood have much smaller effects. The focus of the Opportunity Atlas is therefore on contextual data of where people grew up:

%3E Traditional measures of poverty and neighborhood conditions provide snapshots of income and other variables for residents in an area at a given point in time. But to study how economic opportunity varies across neighborhoods, we really need to follow people over many years and see how one’s outcomes depend upon family circumstances and where on grew up. The Opportunity Atlas is the first dataset that provides such longitudinal information at a detailed neighborhood level. Using the Atlas, you can see not just where the rich and poor currently live – which was possible in previously available data from the Census Bureau – but whether children in a given area tend to grow up to become rich of poor. This focus on mobility out of poverty across generations allows us to trace the roots of outcomes such as poverty and incarceration back to where kids grew up, potentially permitting much more effective interventions.

As such, The Opportunity Atlas data provides a rich source of data for researchers who wish to overlay health data with contextual data.

Methodology

Three sources of Census Bureau are linked to compute the data

1. The 2000 and 2010 Decennial Census short form
2. Federal income tax returns for 1989, 1994, 1995, 1998-2015
3. The 2000 Decennial Census long form and the 2005-2015 American Community Surveys (ACS).

%3C!-- --%3E

20.5 million Americans born between 1987-1983 are sampled from these data and mapped back to the Census tracts they lived in through age 23. After that step, a range of outcomes are then estimated for each of the 70,000 tracts. In order to comply with federal data disclosure standards and protect the privacy of individuals no estimates in tracts with 20 or fewer children are published and noise (small random numbers) is added to all the estimates.

For more information on the data collection and methodology, please visit:

Website

Documentation

Data availability

Some variables are available for counties only. The table below gives you an overview. Open the table in a new tab for a larger view.

https://redivis.com/fileUploads/ee6544ef-e1b1-473d-a75d-36618c91f4a5%3E" alt="data availability.png">

The protected and recreational open space datalayer contains conservation lands and outdoor recreational facilities in Massachusetts. The associated database contains relevant information about each parcel, including ownership, level of protection, public accessibility, assessor’s map and lot numbers, and related legal interests held on the land, including conservation restrictions. Conservation and outdoor recreational facilities owned by federal, state, county, municipal, and nonprofit enterprises are included in this datalayer. Not all lands in this layer are protected in perpetuity, though nearly all have at least some level of protection.

Although the initial data collection effort for this data layer has been completed, open space changes continually and this data layer is therefore considered to be under development. Additionally, due to the collaborative nature of this data collection effort, the accuracy and completeness of open space data varies across the state’s municipalities. Attributes, while comprehensive in scope, may be incomplete for many parcels.

The OpenSpace dataset includes two feature classes:

·
this layer: Protected and Recreational OpenSpace Polygons - polygons of recreational and conservation lands as described above

·
a sister layer: Protected and Recreational OpenSpace Boundaries (arcs) - attributed lines that represent boundaries of the polygons

The following types of land are included in the polygon datalayer:

·
conservation land- habitat protection with minimal recreation, such as walking trails

·
recreation land- outdoor facilities such as town parks, commons, playing fields, school fields, golf courses, bike paths, scout camps, and fish and game clubs. These may be privately or publicly owned facilities.

·
town forests

·
parkways - green buffers along roads, if they are a recognized conservation resource

·
agricultural land- land protected under an Agricultural Preservation Restriction (APR) and administered by the state Department of Agricultural Resources (DAR, formerly the Dept. of Food and Agriculture (DFA))

·
aquifer protection land - not zoning overlay districts

·
watershed protection land - not zoning overlay districts

·
cemeteries - if a recognized conservation or recreation resource

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forest land -- if designated as a Forest Legacy AreaMore information, including details for attribute codes, is available at the MassGIS metadata page for OpenSpace.

Intended Purpose:Polygon layer of area affected by Helicopter Noise Effects Advisory Overlay HNEAO created for the Wellington City Council District Plan. This is an advisory overlay.Abbreviations/Acronyms:ePlan - "Electronic Plan" the web version of the District PlanPDP - Proposed District PlanIHP - Independent Hearings PanelWCC - Wellington City Council Refresh Rate (Data only):Static Ownership:This data is owned by WCC District Planning Team, contact District.Plan@wcc.govt.nz for questions about this layer and its appropriate use cases. Ownership specifies legal or administrative control over the content. Stewardship:This data is maintained by WCC City Insights Team, contact cityinsightsgis@wcc.govt.nz for information about the creation of this layer and its maintenance. Custodianship:This data is maintained by WCC City Insights Team, contact cityinsightsgis@wcc.govt.nz for information about the creation of this layer and its maintenance. Stewardship addresses the ongoing care, maintenance, and management of the content.Authoritative Data Sources (Data only):An overlay spatially identifies distinctive values, risks or other factors that require management. Further data changes have been made as part of the District Plan Review Process. Summary of Data Collection (Data only):The management of noise and vibration associated with transport (e.g. aircrafts, railway etc.) and entertainment occurring within Wellington City is intrinsically linked to the quality of the environment surrounding those areas. Noise ranks highly on the list of environmental pollutants. It can have an adverse effect on health and amenity values, can interfere with communication and can disturb peoples sleep and concentration. It is commonly identified as a nuisance and is the subject of frequent complaints received by council. Under the Resource Management Act 1991 (RMA), noise includes vibration. The Noise Control Overlay in the PDP was created by the WCC District Plan team following the National Planning Standards (https://environment.govt.nz/publications/national-planning-standards/). The boundaries were subsequently modified as part of the District Plan Review Process.

[Superseded] This dataset is a single layer from [Superseded] City Plan 2014 – v19.00–2020 collection. Not all layers were updated in this amendment, for more …Show full description[Superseded] This dataset is a single layer from [Superseded] City Plan 2014 – v19.00–2020 collection. Not all layers were updated in this amendment, for more information on past Adopted City Plan amendments. For information about the zones and how they are applied, please refer to the Brisbane City Plan 2014 document.This dataset utilises Brisbane City Council's Open Spatial Data website to provide additional features for viewing and downloading the data.The first resource is in HTML format. The GO TO button will launch our Open Spatial Data website and this will let you preview the data and enable additional download options. The resources labelled GeoJSON, KML and SHP will give you a download of the entire dataset. The ESRI REST resource connects to metadata for the layer while the CSV resource will download attribute data in a table. For more information on the new features and other tips and tricks please read our Blog.

[Superseded]This dataset is a single layer from [Superseded] City Plan 2014 – v18.00–2020 collection. Not all layers were updated in this amendment, for more information on past Adopted City Plan …Show full description[Superseded]This dataset is a single layer from [Superseded] City Plan 2014 – v18.00–2020 collection. Not all layers were updated in this amendment, for more information on past Adopted City Plan amendments.This feature class is shown on the Airport environs overlay map - Procedures for Air Navigation Services - Aircraft operations surfaces (map reference: OM-001.3).This feature class includes the following sub-categories:(a) Procedures for Air Navigation Services – Aircraft operations surfaces (PANS-OPS) sub-categories:(i) procedures for air navigation surfaces (PANS) sub-category.For information about the overlay and how it is applied, please refer to the Brisbane City Plan 2014 document. Additional information to assist with cross referencing the Airport environs overlay datasets is available in the City Plan 2014 — Airport Environs overlay — reference dataset on Open Data website.The Airport environs overlays contain information derived from data that is created or owned by BAC and licensed to Brisbane City Council. Its use by any person for purposes not associated with planning and development in Brisbane is not authorised. This dataset utilises Brisbane City Council's Open Spatial Data website to provide additional features for viewing and downloading the data.The first resource is in HTML format. The GO TO button will launch our Open Spatial Data website and this will let you preview the data and enable additional download options. The resources labelled GeoJSON, KML and SHP will give you a download of the entire dataset. The ESRI REST resource connects to metadata for the layer while the CSV resource will download attribute data in a table. For more information on the new features and other tips and tricks please read our Blog.

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This GIS layer contains the geographical boundaries of the 2010 census blocks for Loudoun County, Virginia. The 2010 Census block boundaries were used for statistical data collection and tabulation purposes for the 2010 Decennial Census. Census blocks are the smallest geographic area for publishing data from the decennial Census.

The 2010 Census block layer has been modified from the Census Bureau's Tiger line file. Users should be aware that the Census's Tiger line data is devised from a mix of national and local GIS data sets. When the Tiger line data is overlaid with Loudoun County Government's detailed GIS layers it can be determined that the Census Bureau's Tiger line boundaries in some cases are slightly off from the actual location of the physical features, natural features, and governmental units such as town boundaries that they are designated to follow. The 2010 Loudoun Census block layer was generated by Loudoun County so that the block boundaries would overlay with the features in Loudoun County's GIS data sets that the boundary are designated to follow.