This online repository consists if the data used for the BSc thesis/project of Ivo van Middelkoop. It consists of a ArcGIS Project file (ArcGIS Pro BSc Project 2022 Ivo van Middelkoop.aprx) and an Excel worksheet file (Excel data BSc Project 2022 Ivo van Middelkoop.xlsx). The ArcGIS Project file was used to create shapefiles through a sea-level fluctuation model to make maps about paleo coastline reconstructions. The Excel worksheet file was used to analyse the output data coming from the ArcGIS Project file. The topic of this BSc project: How did the sea-level rise following the Late Pleistocene impact the connectivity over time between Sumatra and Borneo?

This repository is openly accessible to everyone. The copyright is owned by Ivo van Middelkoop and Dr. Kenneth F. Rijsdijk

This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

This zip file contains files used for the manuscript "The Aeolian Environment of the Landing Site for the ExoMars Rosalind Franklin Rover in Oxia Planum, Mars".1.The ArcGIS Pro files used to analyze the distribution, orientation, and morphologies of periodic bedrock ridges and transverse aeolian ridges.2.Excel datasets describing the dust devil work presented.Note: These .lyrx files are not backwards compatible with Arc 10.6. The files contained in this zip file are: 1. The HiRISE images used2. The 1-sigma ellipses3. The study area grid4. The ripple and PBRs analyzed5. Excel file for the density calculations of dust devils6. Excel file for the statistics associated with the dust devil tracks

Want to keep the data in your Hosted Feature Service current? Not interested in writing a lot of code?Leverage this Python Script from the command line, Windows Scheduled Task, or from within your own code to automate the replacement of data in an existing Hosted Feature Service. It can also be leveraged by your Notebook environment and automatically managed by the MNCD Tool!See the Sampler Notebook that features the OverwriteFS tool run from Online to update a Feature Service. It leverages MNCD to cache the OverwriteFS script for import to the Notebook. A great way to jump start your Feature Service update workflow! RequirementsPython v3.xArcGIS Python APIStored Connection Profile, defined by Python API 'GIS' module. Also accepts 'pro', to specify using the active ArcGIS Pro connection. Will require ArcGIS Pro and Arcpy!Pre-Existing Hosted Feature ServiceCapabilitiesOverwrite a Feature Service, refreshing the Service Item and DataBackup and reapply Service, Layer, and Item properties - New at v2.0.0Manage Service to Service or Service to Data relationships - New at v2.0.0Repair Lost Service File Item to Service Relationships, re-enabling Service Overwrite - New at v2.0.0'Swap Layer' capability for Views, allowing two Services to support a View, acting as Active and Idle role during Updates - New at v2.0.0Data Conversion capability, able to invoke following a download and before Service update - New at v2.0.0Includes 'Rss2Json' Conversion routine, able to read a RSS or GeoRSS source and generate GeoJson for Service Update - New at v2.0.0Renamed 'Rss2Json' to 'Xml2GeoJSON' for its enhanced capabilities, 'Rss2Json' remains for compatability - Revised at v2.1.0Added 'Json2GeoJSON' Conversion routine, able to read and manipulate Json or GeoJSON data for Service Updates - New at v2.1.0Can update other File item types like PDF, Word, Excel, and so on - New at v2.1.0Supports ArcGIS Python API v2.0 - New at v2.1.2RevisionsSep 29, 2021: Long awaited update to v2.0.0!Sep 30, 2021: v2.0.1, Patch to correct Outcome Status when download or Coversion resulted in no change. Also updated documentation.Oct 7, 2021: v2.0.2, workflow Patch correcting Extent update of Views when Overwriting Service, discovered following recent ArcGIS Online update. Enhancements to 'datetimeUtil' Support script.Nov 30, 2021: v2.1.0, added new 'Json2GeoJSON' Converter, enhanced 'Xml2GeoJSON' Converter, retired 'Rss2Json' Converter, added new Option Switches 'IgnoreAge' and 'UpdateTarget' for source age control and QA/QC workflows, revised Optimization logic and CRC comparison on downloads.Dec 1, 2021: v2.1.1, Only a patch to Conversion routines: Corrected handling of null Z-values in Geometries (discovered immediately following release 2.1.0), improve error trapping while processing rows, and added deprecation message to retired 'Rss2Json' conversion routine.Feb 22, 2022: v2.1.2, Patch to detect and re-apply case-insensitive field indexes. Update to allow Swapping Layers to Service without an associated file item. Added cache refresh following updates. Patch to support Python API 2.0 service 'table' property. Patches to 'Json2GeoJSON' and 'Xml2GeoJSON' converter routines.Sep 5, 2024: v2.1.4, Patch service manager refresh failure issue. Added trace report to Convert execution on exception. Set 'ignore-DataItemCheck' property to True when 'GetTarget' action initiated. Hardened Async job status check. Update 'overwriteFeatureService' to support GeoPackage type and file item type when item.name includes a period, updated retry loop to try one final overwrite after del, fixed error stop issue on failed overwrite attempts. Removed restriction on uploading files larger than 2GB. Restores missing 'itemInfo' file on service File items. Corrected false swap success when view has no layers. Lifted restriction of Overwrite/Swap Layers for OGC. Added 'serviceDescription' to service detail backup. Added 'thumbnail' to item backup/restore logic. Added 'byLayerOrder' parameter to 'swapFeatureViewLayers'. Added 'SwapByOrder' action switch. Patch added to overwriteFeatureService 'status' check. Patch for June 2024 update made to 'managers.overwrite' API script that blocks uploads > 25MB, API v2.3.0.3. Patch 'overwriteFeatureService' to correctly identify overwrite file if service has multiple Service2Data relationships.Includes documentation updates!

[Metadata] Inventory of the State of Hawaii’s affordable housing projects as of February 2023. The list includes affordable housing projects owned by private, non-profit, or governmental entities, developed with funding or support from federal, state or county resources. Data was downloaded from the HHFDC website (https://dbedt.hawaii.gov/hhfdc/affordable-housing-inventory/affordable-rental-housing-inventory/) in PDF format by Hawaii Statewide GIS Program staff, converted to Excel and geocoded in ArcGIS Pro. Projects with no addresses were not included. Data updates are posted periodically on the HHFDC website; users should check the site for the latest copy of the PDF file. For more information, please refer to metadata at https://files.hawaii.gov/dbedt/op/gis/data/Afford_Rent_Hsng_Inv_HHFDC.pdf or contact Hawaii Statewide GIS Program, Office of Planning and Sustainable Development, State of Hawaii; PO Box 2359, Honolulu, Hi. 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/gis.

This layer visualizes over 60,000 commercial flight paths. The data was obtained from openflights.org, and was last updated in June 2014. The site states, "The third-party that OpenFlights uses for route data ceased providing updates in June 2014. The current data is of historical value only. As of June 2014, the OpenFlights/Airline Route Mapper Route Database contains 67,663 routes between 3,321 airports on 548 airlines spanning the globe. Creating and maintaining this database has required and continues to require an immense amount of work. We need your support to keep this database up-to-date."To donate, visit the site and click the PayPal link.Routes were created using the XY-to-line tool in ArcGIS Pro, inspired by Kenneth Field's work, and following a modified methodology from Michael Markieta (www.spatialanalysis.ca/2011/global-connectivity-mapping-out-flight-routes).Some cleanup was required in the original data, including adding missing location data for several airports and some missing IATA codes. Before performing the point to line conversion, the key to preserving attributes in the original data is a combination of the INDEX and MATCH functions in Microsoft Excel. Example function: =INDEX(Airlines!$B$2:$B$6200,MATCH(Routes!$A2,Airlines!$D$2:Airlines!$D$6200,0))                                                

Mapa que muestra el tiempo necesario para llegar a un hospital en tramos de 15 minutos.Para el cálculo de la capa de tiempos, se ha utilizado la herramienta de Service Area, dentro del paquete de Network Analysis, en ArcGIS Pro. En aquellas zonas que están vacías es porque el tiempo de conducción hasta un hospital es superior a los 60 minutos.La capa de hospitales se encuentra en Living Atlas como Hospitales de España.El listado de Hospitales obtenida de la web del Ministerio de Sanidad, Consumo y Bienestar Social.De la citada web se ha extraído un fichero Excel con las direcciones de los hospitales. Debido a la falta de coordenadas y a que las direcciones no eran muy precisas no se ha podido geocodificar contra calle y número todas las direcciones. A este fin se ha añadido un campo que indica la calidad de la geocodificación.El fichero Excel se ha descargado el 19 de Marzo de 2020.Sistema de Referencia: WGS_1984_Web_Mercator_Auxiliary_Sphere . Fecha de publicación: 19/06/2018

Die Datensammlung "Stadtteil-Profile" enthält Strukturdaten zu den Themenbereichen Bevölkerung, Wohnen, Bürgerschaftswahlen, Sozialstruktur, Infrastruktur, und Verkehr. Berichtsjahr 2017. Die Daten ermöglichen eine Standortbeschreibung eines Stadtteils in Hamburg.Geometrien der Stadtteile aus der ALKIS Verwaltungsgrenzen.Quelle: Open Data Portal HamburgVerarbeitungsprozesse: Excel-Tabelle wurde in ArcGIS Pro importiert, mit den Stadtteilen gejoint, nach WebMercator WGS84 projiziert und als Feature Service in ArcGIS Online veröffentlicht.

This dataset provides information on Tempe's subsidized housing program. Tempe has a fixed number of Housing Choice Vouchers (HCVs) based on our HUD contract, which represents the maximum number of families that the Housing Authority could assist. Congress and HUD do not fund the program to assist all of the families we are allotted to assist. We can only assist the number of families we have the budget to assist. HUD provides an initial funding amount based on what they anticipate they will allocate to housing assistance payments. The actual amount of funding received is subject to change depending on Federal Budget priorities, Congressional approval and many other factors. Expenditures are reported monthly, as HUD requires expenses to be posted in the month they were incurred rather than the month the expense was paid. The performance measure dashboard is available at 3.05 Subsidized Housing Funding Usage.Additional InformationSource: Manually maintained data, Housing Pro and QuickbooksContact: Irma Hollamby CainContact Phone: 480-858-2264Data Source Type: ExcelPreparation Method: Monthly values are calculated by determining the month each of the expenditures was for and retroactively accruing the funding use to the appropriate period. There are multiple, multistep excel worksheets that are used to balance between the specialty Housing Software, City Financial System and the HUD mandated reporting system. Additionally, it is important to note that Funding is allocated by Congress on the Federal Fiscal Year (October - September), the City operates on a Fiscal Year (July - June) and HUD provides funding on the Housing Authority in Calendar Year (January - December) funding increments. Therefore, the City must cross balance between three funding years.Publish Frequency: AnnuallyPublish Method: ManualData Dictionary

Das Verzeichnis basiert auf den Meldungen zur amtlichen Krankenhausstatistik für das Berichtsjahr 2022. Es enthält Informationen zu Name und Adresse, Telefonnummer, E-Mail- und Internetadresse der Einrichtungen, Name und Art des Trägers sowie Anzahl der aufgestellten Betten nach Fachabteilungen gem. der Fachabteilungsgliederung der Deutschen Rentenversicherung (nur Hauptfachabteilungen).Art des Trägers 1 = öffentlicher Träger2 = freigemeinnütziger Träger3 = privater TrägerArt der Einrichtung6 = Vorsorge- oder Rehabilitationseinrichtung nach § 111 SGB V7 = sonstige Vorsorge- oder RehabilitationseinrichtungQuelle: DESTATISVerarbeitungsprozesse: Excel Tabelle wurde selektiert, in ArcGIS Pro geocodiert, Attributen angepasst nach WebMercator projiziert und als Feature Service in ArcGIS Online veröffentlicht.

No final de 2019 foram registados os primeiros casos de infeção por SARS-CoV-2 em humanos na cidade de Wuhan, na China. A 12 de março de 2020 a OMS declarou o COVID-19 como doença pandémica, pois até à data tinha atingido 117 países em mais de um continente e causado mais de 4 mil mortes. A 27 de dezembro de 2020, o número de casos confirmados acumulados desde o início da pandemia ultrapassou os 79 milhões, tendo até então morrido mais de 1 milhão e 700 mil pessoas em todo o mundo infetadas pelo SARS-CoV-2. Em Portugal Continental, à mesma data, o número de indivíduos infetados pelo vírus ultrapassou 390 mil, e o número de óbitos era superior a 6 mil.Os casos de infeção e epidemias recentes mostram que os coronavírus são uma ameaça contínua ao ser humano e à economia, por surgirem inesperadamente, disseminando-se facilmente, levando a consequências catastróficas. As ameaças à sobrevivência, subsistência e bem-estar das pessoas provocadas pelo SARS-CoV-2 ilustram como a pandemia é uma questão multidisciplinar em que a economia é uma vertente que a saúde pública tem que ter em conta, uma vez que é determinante na Saúde das Populações. Além da perda devastadora de vidas, a COVID-19 resultou num aumento do desemprego e numa crise económica global e multifacetada. Apesar de terem sido desenvolvidas vacinas para o SARS-CoV-2, estas presentemente ainda não garantem uma imunidade total à doença. Assim, uma vez que o processo de vacinação é moroso, é fundamental a identificação do padrão de evolução espaciotemporal da prevalência do SARS-CoV-2. Para além disso, o SARS-CoV-2 é suscetível a sofrer mutações que consistem em mudanças da sequência genética, originando novas variantes. As variantes podem diferir umas das outras conforme o número de mutações, denominando-se por estirpes. Os vírus mudam constantemente de dois modos: a deriva antigénica e mudança antigénica. Assim, o SARS-CoV-2 também é propenso a várias mutações que resultam na deriva antigénica, o que pode dificultar o reconhecimento imunológico. Embora as vacinas sejam uma ferramenta eficiente, nenhuma delas é 100% eficaz na prevenção da doença do COVID-19, sendo que uma percentagem da população imunizada continuará a adoecer a diferentes graus de gravidade.As análises espaciais, espaciotemporais permitem auxiliar a definição de estratégias preventivas para tomada de decisão, permitindo estimar os padrões espaciotemporais de uma epidemia. Os sistemas de apoio à decisão espacial têm se tornado cada vez mais importantes na gestão de risco na área da saúde.Para a execução da análise espacial foram recolhidos dados epidemiológicos do número de casos confirmados COVID-19 e dados de mobilidade. Os dados do número de casos confirmados de COVID-19 foram recolhidos do site da Direção Geral de Saúde (DGS). Estes dados foram tratados de modo a possibilitar a junção ao tema dos municípios como contantes na Carta administrativa Oficial de Portugal (CAOP), de forma a serem representados espacialmente. Os dados de mobilidade foram recolhidos do Instituto Nacional de Estatística (INE) para a data dos Censos 2011. Estes dados representam a origem e destino dos movimentos pendulares (casa-trabalho/escola) entre municípios. O tratamento destes dados consistiu na elaboração de uma tabela dinâmica de modo a quantificar as pessoas que se deslocam do município x para cada um dos restantes municípios. Posteriormente, fez-se a transposta da tabela dinâmica ficando assim em formato de matriz e codificaram-se os municípios de modo a obter uma matriz de origem-destino de acordo com a estrutura requerida pelo software ArcMap.Para a elaboração da análise espaciotemporal foram recolhidos dados do número de casos confirmados COVID-19 por município, de julho de 2020 a julho de 2021. Estes dados foram recolhidos do repositório Data Science for Social Good Portugal, que por sua vez tem como fonte o dashboard da DGS e da base de dados da ESRI Portugal, e são obtidos dos valores de incidência a 14 dias, representando o número de casos acumulados das duas semanas anteriores por 10 mil habitantes.A metodologia adotada foi aplicada com recurso aos seguintes softwares: Excel, do Office 365; ArcMap 10.7.1, e ArcGIS Pro da ESRI. Em primeiro lugar foi efetuada a análise espacial com recurso a dois métodos: o Getis-Ord Gi* e o Local Moran’s Index. O método Getis-Ord Gi*, também conhecido como análise de hotspots. Esta estatística é aplicada aos locais vizinhos e tem em consideração o seu ‘peso’. Este método estatístico permite detetar a presença de autocorrelações espaciais locais, ou seja, hotspots e coldspots baseados nos outputs Z-score e P-value. O método Cluster and Outlier Analysis (Anselin Local Moran’s I) permite identificar clusters locais bem como outliers espaciais locais. Este método considera que, com os pesos padronizados por linha, a soma de todos os pesos é igual ao número de observações. Uma vez que Hotspots Analysis (Getis-Ord Gi*) e Cluster and Outlier Analysis (Anselin Local Moran’s I) podem identificar diferentes padrões de clusters, optou-se por utilizar um método híbrido de autocorrelação espacial de modo a avaliar a autocorrelação espacial do número de casos e identificar padrões de clusters espaciais. Para aplicar o método híbrido, juntou-se o resultado de cada um dos métodos numa layer e criou-se uma coluna na tabela de atributos de modo a cruzar os resultados de ambos. Através do output ZScore aplicaram-se condições para criar quatro classes.Realizou-se uma análise espaciotemporal de modo a possibilitar a visualização e a análise dos dados espaciais a partir de uma serie temporal, que integra uma análise do padrão espacial e temporal e permite a visualização em 2D e a 3D. Esta foi realizada através da aplicação da estatística de Mann-Kendall, que assume como hipótese nula os dados que provêm de uma população com realizações independentes e são distribuídos de forma idêntica.Ao aplicar os métodos acima mencionados, verificou-se que a maioria dos hotspots dos casos de SARS-CoV-2 ocorrem nos municípios das Áreas Metropolitanas. No entanto, após a aplicação de medidas de restrição à mobilidade estes ocorrem em municípios do interior. No que diz respeito à análise espaciotemporal, esta identificou a maioria dos municípios como oscillating hotspots, o que corrobora a ideia apresentada anteriormente.