The Greek Inscriptions in Space and Time (GIST) dataset represents a comprehensive collection of ancient Greek inscriptions, enriched by temporal and spatial metadata. The dataset was created by the Social Dynamics in the Ancient Mediterranean Project (SDAM), 2019-2023, funded by the Aarhus University Forskningsfond Starting grant no. AUFF-E-2018-7-2.

The GIST dataset is mainly based on Greek inscriptions from the dataset of Searchable Greek Inscriptions [PHI](https://inscriptions.packhum.org/) and I.PHI dataset published by the Pythia Project (Sommerschield, T. et al. 2021). Furthermore, the attributes were enriched by LOD from the Trismegistos Project, Hansen and Nielsen's (2004) Inventory of Archaic and Classical Greek City-States and Hanson's (2016) Cities Database. The text of the inscriptions was lemmatised using the AGILe lemmatiser (de Graaf et al. 2022). The rights to these data are held by the respective original projects.

The GIST dataset consists of 217,863 inscriptions, enriched by 36 attributes. The individual inscriptions have been cleaned, preprocessed and enriched with additional data, such as date in a numeric format and geolocation. The origin of existing attributes is further described in columns 'dataset_source', 'attribute_source', 'created_by_script' and 'description' in the attached Metadata.csv or available via GitHub.

180,061 inscriptions have valid geospatial coordinates (the `geometry` attribute). This information is also used to determine the Roman urban context of each inscription (i.e. whether it is in the neighbourhood (i.e. within a 5000m buffer) of a large city, medium city, or small city or rural (>5000m to any type of city; see the attributes `urban_context`, `urban_context_city`, and `urban_context_pop`) and for their mapping on an ancient Greek polis (if there is any within the 5000m buffer; see the attributes `polis_context_name`, `polis_context_size`, and `polis_context_fame`).

131,677 inscriptions have a numerical date of origin expressed by means of an interval or singular year using the attributes `not_before` and `not_after`.

The scripts used to generate the dataset and their metadata are available via GitHub.

Formats

We publish the dataset in Parquet and GeoJSON file formats. A description of individual attributes is available in the Metadata.csv. Using `geopandas` library, you can load the data directly from Zenodo into your Python environment using the following command:

`GIST = gpd.read_file("https://zenodo.org/records/10127597/files/GIST_v1-0.geojson?download=1", driver="GeoJSON")`.

In R, the sfarrow and sf libraries hold tools, i.e., st_read_parquet(), read_sf(), to load a parquet and geojson, respectively, after you have downloaded the datasets locally.

Further reading:

• de Graaf, E., Stopponi, S., Bos, J., Peels-Matthey, S. & Nissim, M. (2022). AGILe: The First Lemmatizer for Ancient Greek Inscriptions. Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022), Marseille, 20-25 June 2022. pp. 5334–5344. http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.571.pdf
• Nielsen, T. H., & Hansen, M. H. (Eds.) (2004). An Inventory of Archaic and Classical Poleis. Oxford University Press. Digitised data available through https://polis.stanford.edu
• Hanson, J. W. (2016). Cities Database (OXREP databases). Version 1.0. http://oxrep.classics.ox.ac.uk/databases/cities/. DOI: https://doi.org/10.5287/bodleian:eqapevAn8
• Kaše, V. & Glomb, T. (2022). The History of Graeco-Roman Religions in the Light of Cultural Evolution: A computational text analysis of ancient Greek inscriptions (submitted). [pdf]
• Kaše, V. & Glomb, T. (2023). Affluence, Agricultural Productivity and the Rise of Moralizing Religion in the Ancient Mediterranean. Religion, Brain & Behavior 13/2, 202-206. https://doi.org/10.1080/2153599X.2022.2065350 [link]
• Sommerschield, T., Assael, Y., Shillingford, B., Bordbar, M., Pavlopoulos, J., Chatzipanagiotou, M., Androutsopoulos, I., Prag, J., & de Freitas, N. (2021). I.PHI dataset: Ancient Greek inscriptions. https://github.com/sommerschield/iphi

Notes on spatial attributes

Machine-readable spatial point geometries are provided within the GeoJSON and parquet formats, as well as 'latitude' and 'longitude' columns, which contain geospatial decimal coordinates where these are known. Other attributes that contain spatial information have been generated from other sources. These include TMgeo_name, which provides the ID of the inscription location as presented in Trismegistos. Information on associated ancient cities within a 5 km buffer of inscription location is within the polis_ and urban_context_ attributes. 'polis-' attributes contain the name, identifier, and the rank of an associated polis from the Hansen/Nielsen's Inventory of Archaic and Classical Greek City-States (Oxford 2005), specifically a digital version of the inventory created by Joshua Ober and his team, hosted by the Stanford University library (https://polis.stanford.edu). Information on Roman-period urban contexts is present in the 'urban_context' attributes. These attributes, based on Hanson's 2016 list (http://oxrep.classics.ox.ac.uk/databases/cities/), include the rank of the associated city (the largest one within 5 km distance), ancient toponym, and population estimate.

List of all spatial attributes:

• 'geometry' - contains spatial point coordinate pair, ready for use in R or Python
• 'latitude' and 'longitude' - contain angular coordinates in decimal numeric format (EPSG4326)
• 'TMgeo_name' - id of geographic location for inscription findspot from Trismegistos
• 'polis_context_name' - the textual component of the ancient polis identifier from the digital Greek polis inventory
• 'polis_context_size' - 1 to 5 ranking, 5 is largest, based on HN estimates. Range 0-5. 1= 0-25 km sq.; 2 = 25-100 km sq, 3 = 100-200 km sq; 4 = 200-500 km sq; 5 = 500 km sq or more. 0 = no evidence for size. HN Appendix 9, with additions from Hansen 2008 and from Emily Mackil (per litt).
• 'polis_context_fame' - Number of columns of text in the HN inventory (by 1/8 column), as proxy for prominence of a given place. The range is 0.12-20.87. For display, the range will be reduced to a 1-5 ranking: 0.12-.037 = 1, 0.5-0.87 = 2, 1.0-2.87 = 3, 3.0-5.87 = 4, 6.0-20.87 = 5.
• 'urban_context' - specifies the rank of a Roman city within 5 km distance of an inscription (if one exists) on the basis of population estimated by Hanson 2016. The scale is: small, medium, large.
• 'urban_context_city' - contains the name (ancient toponym) of a city within 5 km distance of an inscription (if one exists). The city dataset is based on Hanson 2016. If the inscription's findspot fell within 5 km distance of multiple Roman cities, the largest was selected.
• 'urban_context_popest' - estimated population of the associated city from Hanson 2016, 2019

Disclaimer

Please be aware that the records in this dataset are aggregated from pre-existing sources, and additional attributes are generated on the basis of third-party data (see data provenance in the 'data_source' column in the Metadata.csv). SDAM did not create the original data, vouch for its accuracy, or guarantee that it is the most recent data available from the original data provider. Many variables contain values that are, by nature, approximate and may contain some inaccuracies or missing values. The data may also contain errors introduced by the data provider(s) and/or by SDAM. The openness of our processing scripts should facilitate the fast discovery of any such errors or discrepancies. We highly recommend checking attribute accuracy with the primary source, i.e. the *editio princeps* of the inscription in question. For derived data (e.g. urban_context), please review the associated scripts to understand their limitations.

Please contact the authors in case of any questions!

Reading Area Transportation Study Technical Committee Meeting Minutes

Quarterly report for Quarter 1 of 2022 for the Alaska Geospatial Council's Transportation Technical Working Group meeting on 3/8/2022.

Reading Area Transportation Study Technical Committee Meeting Minutes

Reading Area Transportation Study Coordinating Committee Meeting Minutes

Reading Area Transportation Study Technical Committee Meeting Minutes

The Transportation Systems Plan (TSP) is the long-range plan to guide transportation investments in Portland. Originally developed in 2002 and last updated in 2007, the TSP meets state and regional planning requirements and addresses local transportation needs for cost-effective street, transit, freight, bicycle and pedestrian infrastructure improvements. The plan will provide transportation options for residents, employees, visitors, and firms doing business in Portland, making it more convenient to walk, bike, take transit -- and drive less -- while meeting their daily needs. Forecasted 20 year revenues are only ? to ½ of the amount needed to implement the City and other agency candidate projects and programs. City staff will use performance-based evaluation criteria, along with public comments, to recommend which projects to place on the higher priority 'Financially Constrained' list in the 2015 TSP. TSP Project Areas represent area (polygon) locations of proposed transportation projects over the next 20 years. Planned TSP projects are also represented as lines (Comprehensive Plan TSP Project Lines) and areas (Comprehensive Plan TSP Project Areas) in separate GIS datasets.-- Additional Information: Category: Planning - Comprehensive Plan Purpose: For mapping TSP boundaries. Update Frequency: As Needed-- Metadata Link: https://www.portlandmaps.com/metadata/index.cfm?&action=DisplayLayer&LayerID=52099

A collaborative effort between DEP and the New Jersey State Office of Innovation, the Transportation Needs Index is a data-driven analysis tool that highlights areas where high demographic needs intersect with limited transit access, helping to identify areas for potential transportation investments. These locations include those where transportation may not be meeting the needs of all residents, especially for populations traditionally underserved by transportation services or left out of transportation decisions in the past.

The New Jersey Office of Information Technology, Office of GIS (NJOGIS), in partnership with several local GIS and public safety agencies, as well as the NJ Department of Transportation, has built a comprehensive statewide NG9-1-1 database meeting and exceeding the requirements of the National Emergency Number Association (NENA) 2018 NG9-1-1 GIS Data Standard (NENA-STA-006.1-2018). The previous New Jersey statewide road segment data (Tran_road_centerline_NJ), which included the road name alias information, has been transformed into the NENA data model to create the street name alias table.The existing road centerlines were loaded into New Jersey's version of the NENA NG9-1-1 data model using Extract, Transform and Load (ETL) procedures created with Esri's Data Interoperability Extension. The data subsequently have been updated and corrected.The road centerlines no longer contain any linear referencing information. The linear referencing will only be maintained by the NJ Department of Transportation as part of the NJ Roadway Network.

IntroductionThis web map was created for a NOAA-ESLR funded research project with Old Dominion University. The purpose is to allow participants to mark down points and areas of interest within the region. The data layers, provided by a verity of organizations, offer crucial context.The edit tool is used to achieve this and allows users to explain why the spot was chosen. Data presented in this web map were compiled for the National Oceanic and Atmospheric Administration (NOAA) funded project titled, "Transportation Systems and Flood Resilience under Dynamic Sea Level Rise: Integrated Modeling to Assess Natural and Nature-Based Solutions for Roadway Flooding in Hampton Roads, Virginia." The grant number is NA22NOS4780174.Original LayersThe Inundation modeling was conducted to determine the extent of permanent flooding due to sea level rise for the years 2040, 2060, and 2080. Old Dominion University's Center for Geospatial Science, Education, and Analytics (GeoSEA) for the Commonwealth Center for Recurrent Flooding Resiliency (CCRFR) in response to a request from the Secretary of Natural Resources and Special Assistant to the Governor for Coastal Adaptation and Protection to assist with meeting the Executive Order Number Twenty-Four (2018), Increasing Virginia’s Resilience to Sea Level Rise and Natural Hazards directive set forth in Section 2 Part A requiring the development of a Coastal Resilience Master Plan. Following the recommendation of the Commonwealth Center for Recurrent Flooding Resiliency (CCRFR), NOAA 2017 Intermediate-High curve was used to model flood surfaces. Values for these flood surfaces were obtained by examining the NOAA Intermediate-high curve at tide stations throughout coastal Virginia. Concurrent with identification of SLR impacts, further analysis exposed impacts due to both "minor" (tidal nuisance) and "moderate" flooding events, occurring with SLR for the entire study region. Minor and Moderate flooding surface data were provided by NOAA for these analyses. Hydrologically disconnected areas of potential inundation are preserved to highlight areas of elevated, yet uncertain, vulnerability. Accuracy of underlying elevation data, storm water system connectivity, water tables, and other local factors will all impact flooding connectivity. All intensive study area (ISA) layers were created utilizing aerial drone imagery by GeoSEA. External LayersThe Federal Air Administration (FAA) provides real-time layers for prohibited airspace, airspace classes, and other relevent information when planning drone flights. Here you will find the appliction that hosts these layers. From ArcGIS Online, various restoration projects and local sea level rise layers are provided by NOAA and were linked directly to this webmap from a REST service. The Virginia Institute of Marine Science (VIMS) at the College of William and Mary shared a verity of coastal layers, being nearly all the inventory layers plus structures. You may find out more information here: Center for Coastal Resources Management. 2019 - 2021. Shoreline Management Model, version 5.1. Center for Coastal Resources Management, Virginia Institute of Marine Science, College of William and Mary, Gloucester Point, Virginia.The Hampton Roads Geospatial Exchange (https://www.hrgeo.org/) hub site also provided relevent data, such as a resilience tracking list that was last updated in October, 2021. The city of Norfolk, Virginia also provides resilience data on their hub site, found here: https://norfolkgisdata-orf.opendata.arcgis.com/Transportation data were provided by both the Department of Transportation (DOT) and Virginia Department of Transportation (VDOT). DOT provided line data for roads, ranging from local to interestates. VDOT provided traffic volume for major local roads.

IntroductionThis web application was created for a NOAA-ESLR funded research project with Old Dominion University. The purpose is to allow participants to mark down points and areas of interest within the region. The data layers, provided by a verity of organizations, offer crucial context.The edit tool is used to achieve this and allows users to explain why the spot was chosen. Data presented in this web map were compiled for the National Oceanic and Atmospheric Administration (NOAA) funded project titled, "Transportation Systems and Flood Resilience under Dynamic Sea Level Rise: Integrated Modeling to Assess Natural and Nature-Based Solutions for Roadway Flooding in Hampton Roads, Virginia." The grant number is NA22NOS4780174.Original LayersThe Inundation modeling was conducted to determine the extent of permanent flooding due to sea level rise for the years 2040, 2060, and 2080. Old Dominion University's Center for Geospatial Science, Education, and Analytics (GeoSEA) for the Commonwealth Center for Recurrent Flooding Resiliency (CCRFR) in response to a request from the Secretary of Natural Resources and Special Assistant to the Governor for Coastal Adaptation and Protection to assist with meeting the Executive Order Number Twenty-Four (2018), Increasing Virginia’s Resilience to Sea Level Rise and Natural Hazards directive set forth in Section 2 Part A requiring the development of a Coastal Resilience Master Plan. Following the recommendation of the Commonwealth Center for Recurrent Flooding Resiliency (CCRFR), NOAA 2017 Intermediate-High curve was used to model flood surfaces. Values for these flood surfaces were obtained by examining the NOAA Intermediate-high curve at tide stations throughout coastal Virginia. Concurrent with identification of SLR impacts, further analysis exposed impacts due to both "minor" (tidal nuisance) and "moderate" flooding events, occurring with SLR for the entire study region. Minor and Moderate flooding surface data were provided by NOAA for these analyses. Hydrologically disconnected areas of potential inundation are preserved to highlight areas of elevated, yet uncertain, vulnerability. Accuracy of underlying elevation data, storm water system connectivity, water tables, and other local factors will all impact flooding connectivity. All intensive study area (ISA) layers were created utilizing aerial drone imagery by GeoSEA. External LayersThe Federal Air Administration (FAA) provides real-time layers for prohibited airspace, airspace classes, and other relevent information when planning drone flights. Here you will find the appliction that hosts these layers. From ArcGIS Online, various restoration projects and local sea level rise layers are provided by NOAA and were linked directly to this webmap from a REST service. The Virginia Institute of Marine Science (VIMS) at the College of William and Mary shared a verity of coastal layers, being nearly all the inventory layers plus structures. You may find out more information here: Center for Coastal Resources Management. 2019 - 2021. Shoreline Management Model, version 5.1. Center for Coastal Resources Management, Virginia Institute of Marine Science, College of William and Mary, Gloucester Point, Virginia.The Hampton Roads Geospatial Exchange (https://www.hrgeo.org/) hub site also provided relevent data, such as a resilience tracking list that was last updated in October, 2021. The city of Norfolk, Virginia also provides resilience data on their hub site, found here: https://norfolkgisdata-orf.opendata.arcgis.com/Transportation data were provided by both the Department of Transportation (DOT) and Virginia Department of Transportation (VDOT). DOT provided line data for roads, ranging from local to interestates. VDOT provided traffic volume for major local roads.

This data includes bikeways of various types, whether existing or planned within the CORE MPO Non-motorized Transportation Plan. The various types of bikeways can include bike lanes, shared use paths, cycle tracks, paved shoulders, and shared lanes, among others. Definitions of different types of bikeways are provided in the Non-motorized Transportation Plan, Appendix E: Bikeway Route Notes. See web page at: http://www.thempc.org/Transportation/Non-motorTranspPlan.html. Edited as needed.Use limitations: Available for public use and download via the SAGIS Open Data website DATA DICTIONARIESThe Data Dictionary for a few of the fields are shown below. The Existing or EXIST_CD (Existing Type) fields and the Status_by_Type (Status of Plan) field are the most fundamental ones and they are explained first. The Existing field may be symbolized automatically when this feature class is added to a map. It indicates the current type of facility on each segment of the plan's bikeway network, regardless of what type is recommended in the MPO Non-motorized Transportation Plan. On the other hand, the Status_by_Type field indicates whether a given bikeway segment currently has the type of facility recommended for it in the Non-motorized Transportation Plan. For example, the record for a segment that currently has only shared lanes, but is recommended to have bike lanes, would show the code for "Shared Lane" in the Existing or EXIST_CD fields, and would show the code for "Recommended Bike Lane" in the Status_by_Type field. In short, symbolizing the Existing or EXIST_CD fields shows you current conditions on the network, while symbolizing Status_by_Type field shows you what is recommended and how far along the region is in meeting that vision (e.g. with the latter field, you could use different colors to dinstinguish types and use solid and dotted lines to dinstinguish whether the recommended type already exists or not).Existing or EXIST_CD (Existing Type) field:BL or 101 = Bike Lane CT or 102 = Cycle TrackBP or 103 = Bike Path (i.e. Shared Use Path) PS or 104 = Paved ShoulderNarrow PS or 105 = Narrow Paved ShoulderSL or 106 = Shared LaneWCL or 107 = Wide Curb Lane108 = Unopened. This describes a segment that is either physically or legally untraversable by bicycle in its current state. A few examples are: woods, swamp, canal-side maintenance drives (whether grassy or dirt), old railroad beds, freeways where bicycles are currently prohibited but where a separated facility is recommended in the future, freeways that are recommended to be replaced with slower, local streets. Status_by_Type (Status of Plan) field:0 = Existing Bike Lane1 = Existing Shared Use Path2 = Existing Paved Shoulder3 = Existing Shared Lane4 = Existing Wide Curb Lane5 = Recommended Bike Lane6 = Recommended Shared Use Path7 = Recommended Paved Shoulder8 = Recommended Wide Curb Lane9 = Existing Cycle Track10 = Recommended Cycle Track11 = Existing Narrow Paved Shoulder12 = Recommended Narrow Paved Shoulder13 = Recommended Shared LaneA few other fields are detailed below.History (Plan Year Origin) distinguishes which of the bikeway plan's segments were in the GIS data set for the CORE MPO's Bikeway Plan of 2000 and which ones were added during the udpate that became the CORE MPO Non-motorized Transportation Plan of 2014. Be aware that bikeway plans existed prior to 2000, but that prior history is not currently distinguished in this field. Also note that while most of the 2000 plan was retained in the 2014 plan, a few segments were not. Those segements are not in this data set. Segments that were retained from the 2000 plan do not necessarily have the same recommendations for facility type now that they had in 2000.2000 Plan = retained from the GIS data from the CORE MPO Chatham County Bikeway Plan (adopted in 2000)2014 Plan = added during the update which became the bikeway section of CORE MPO Non-motorized Transportation Plan (adopted in 2014)LOS_Category (Level of Service Category) shows results of a particular kind of analysis that attempts to predict the comfort of bicycling on a given roadway segment, considering factors such as volume of auto and truck traffic, traffic speed, width of lane, and pavement condition. This method is not appropriate for off-road bikeway types (such as shared use paths) and was not appllied to bikeway segments that currently exist as such. The method does not account for conditions at intersections. The method of analysis is explained in an appendix of the CORE MPO Non-motorized Transportation Plan, which includes the assumptions that were employed whenever field data was not readily available. Be aware that this Bicycle LOS is a different type of assessment from the traditional LOS assessments focusing on auto drivers, which instead measures characteristics such as delay and traffic density. In the Bicycle LOS method, numeric scores are grouped into categories as follows:A = Extremely good LOS (score less than or equal to 1.5)B = Very good LOS (score greater than 1.5 and less than or equal to 2.5)C = Moderately good LOS (score greater than 2.5 and less than or equal to 3.5)D = Moderately poor LOS (socre greater than 3.5 and less than or euqual to 4.5)E = Very poor LOS (score greater than 4.5 and less than or equal to 5.5)F = Extremely poor LOS (score greater than 5.5)