Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

Through the Department of the Interior-Bureau of Indian Affairs Enterprise License Agreement (DOI-BIA ELA) program, BIA employees and employees of federally-recognized Tribes may access a variety of geographic information systems (GIS) online courses and instructor-led training events throughout the year at no cost to them. These online GIS courses and instructor-led training events are hosted by the Branch of Geospatial Support (BOGS) or offered by BOGS in partnership with other organizations and federal agencies. Online courses are self-paced and available year-round, while instructor-led training events have limited capacity and require registration and attendance on specific dates. This dataset does not any training where the course was not completed by the participant or where training was cancelled or otherwise not able to be completed. Point locations depict BIA Office locations or Tribal Office Headquarters. For completed trainings where a participant _location was not provided a point locations may not be available. For more information on the Branch of Geospatial Support Geospatial training program, please visit:https://www.bia.gov/service/geospatial-training.

I’d love to begin by saying that I have not “arrived” as I believe I am still on a journey of self-discovery. I have heard people say that they find my journey quite interesting and I hope my story inspires someone out there.I had my first encounter with Geographic Information System (GIS) in the third year of my undergraduate study in Geography at the University of Ibadan, Oyo State Nigeria. I was opportune to be introduced to the essentials of GIS by one of the prominent Environmental and Urban Geographers in person of Dr O.J Taiwo. Even though the whole syllabus and teaching sounded abstract to me due to the little exposure to a practical hands-on approach to GIS software, I developed a keen interest in the theoretical learning and I ended up scoring 70% in my final course exam.

GIS files for Lab 1: Making a Map in UWSP WATR 391/591 course.

The dataset contains locations and attributes of Golf Courses, created as part of the DC Geographic Information System (DC GIS) for the D.C. Office of the Chief Technology Officer (OCTO) and participating D.C. government agencies.

Didemnum vexillum is an invasive sea squirt that is not native to UK shores. It was first detected in Europe in 1991 and has since spread to several countries (including France, Ireland and the UK). The species has been located in Wales, Scotland and England and there is concern D. vexillum may have negative impacts on biodiversity and shellfish interests.

Predicting the spread of an invasive species is crucial when assessing possible management actions. The potential impacts of the species on both biodiversity and commercial interests need to be studied and a cost-benefit approach taken to decide on the best course of management for that species.

Geographic Information System (GIS) offers a fast, efficient way to map this predicted spread. The results of this mapping can then be used to focus on areas where D. vexillum may conflict with conservation and commercial interests.

Cartography is the knowledge associated with the art, science, and technology of maps. Maps portray spatial relationships among selected phenomena of interest and increasingly are used for analysis and synthesis. Through digital cartography and web mapping, however, it is possible for almost anyone to produce a bad map in minutes. Although cartography has undergone a radical transformation through the introduction of digital technology, fundamental principles remain. Doing computer cartography well requires a broad understanding of graphicacy as a language (as well as numeracy and literacy). This course provides an introduction to the principles, concepts, software, and hardware necessary to produce good maps, especially in the context (and limitations) of geographic information systems (GIS) and the web.

You will be asked to work through a series of modules that present information relating to a specific topic. You will also complete a series of cartography projects to reinforce the material. Lastly, you will complete term projects. Please see the sequencing document for our suggestions as to the order in which to work through the material. We have also provided PDF versions of the lectures with the notes included.

Input shapefiles for the Weighted Overlay Lab of UWSP's WATR 391 GIS course.

These data were automated to provide an accurate high-resolution historical shoreline of Twelve Mile Speed Trial Course, NY suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery and/or field survey. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://inport.nmfs.noaa.gov/inport/item/39808

A report to provide complete knowledge of flood modeling with essential hands-on training. The Water & Environment Division under the Department of Civil Engineering, National Institute of Technology Warangal had organized a one week Global Initiative of Academic Networks (GIAN) Course under the supervision of Minister of Human Resource Development (MHRD), Government of India on “Geographic Information Systems (GIS) Methods for Flood Risk Management” from 25th July to 1st August, 2018.

The Feature Class CR408G_A_AB_CDA with Polygon geometry represents the location and extension of hydrographic surfaces in particular Wet area of ​​watercourse. Deriving from the database created on a 1:5,000 scale of the multiscale DBTI (Integrated Territorial Database) which envisaged the adaptation and structuring, with respect to the IntesaGis standards, of the I and II lot of the CTRN and the subsequent updating through the photogrammetric recovery of 2007 The geographical information has been organized in hierarchical groups, according to the model defined within the scope of the State-Regions agreement on geographic information systems.

Locations of golf courses in Los Angeles CountyThis dataset is maintained through the County of Los Angeles Location Management System. The Location Management System is used by the County of Los Angeles GIS Program to maintain a single, comprehensive geographic database of locations countywide. For more information on the Location Management System, visit http://egis3.lacounty.gov/lms/.

Study how the geomorphology of the Salt River channel has changed over the last 100 years and how factors such as the damming of the Salt and Verde Rivers and gravel mining operations have contributed to these changes. For more than 1,000 years there has been a city on the banks of the Salt and Gila Rivers in the vicinity of what is now Phoenix. The course of natural processes as embodied by the river have interacted with the course of human events as evidenced by the city, each exerting influence on the other. The myriad of tangled connections between the natural and social systems has inevitably altered each of them, so that understanding of one without understanding of the other is incomplete. Within the last 100 years, intensive technological development of the river resources, its space, water, materials, and biotic complements, has radically altered the natural processes and forms of the river. At the same time, the river has influenced development of the city, sometimes as a resource such as recreational space, and sometimes as a hazard such as flooding. This constantly changing fluvial system, integrating natural and artificial influences, is the foundation for the primary riparian ecosystems of the region. The research questions of this project are: (1) What has been the nature of change in the geomorphic/riparian system, and how have human and natural factors controlled the distribution and intensity of the change over the past century? (2) Why does the river have its present geomorphic/riparian configuration, and how stable is that arrangement from geomorphic, hydrologic, and geographic perspectives? and (3) How does the river respond to ongoing changes in the spatial arrangement of human activities and attending technological impacts? This project promises improved understanding of the dynamics of dryland rivers, especially how and why they change under the influence of urban development. The research also promises to provide an integrating factor in the CAP LTER effort, because the river integrates the influences of hydrologic, geomorphic, biotic, and human technological systems. The research will provide a repeatable quantitative approach to assessing the changes in the river and as it continues its millennium-long connection between natural and social systems.

description: The Geographic Names Information System (GNIS), developed by the U.S. Geological Survey in cooperation with the U.S. Board on Geographic Names (BGN), contains information about physical and cultural geographic features in the United States and associated areas, both current and historical, but not including roads and highways. The database also contains geographic names in Antarctica. The database holds the Federally recognized name of each feature and defines the location of the feature by state, county, USGS topographic map, and geographic coordinates. Other feature attributes include names or spellings other than the official name, feature designations, feature class, historical and descriptive information, and for some categories of features the geometric boundaries. The database assigns a unique feature identifier, a random number, that is a key for accessing, integrating, or reconciling GNIS data with other data sets. The GNIS is our Nation's official repository of domestic geographic feature names information. This database has been subsetted to include golf course and country club features only.; abstract: The Geographic Names Information System (GNIS), developed by the U.S. Geological Survey in cooperation with the U.S. Board on Geographic Names (BGN), contains information about physical and cultural geographic features in the United States and associated areas, both current and historical, but not including roads and highways. The database also contains geographic names in Antarctica. The database holds the Federally recognized name of each feature and defines the location of the feature by state, county, USGS topographic map, and geographic coordinates. Other feature attributes include names or spellings other than the official name, feature designations, feature class, historical and descriptive information, and for some categories of features the geometric boundaries. The database assigns a unique feature identifier, a random number, that is a key for accessing, integrating, or reconciling GNIS data with other data sets. The GNIS is our Nation's official repository of domestic geographic feature names information. This database has been subsetted to include golf course and country club features only.

A public feature layer view used to share natural spaces set aside for recreation or the protection of wildlife or natural habitats.

This dataset shows the golf courses in Allegheny County.If viewing this description on the Western Pennsylvania Regional Data Center’s open data portal (https://www.wprdc.org), this dataset is harvested on a weekly basis from Allegheny County’s GIS data portal (https://openac.alcogis.opendata.arcgis.com/). The full metadata record for this dataset can also be found on Allegheny County’s GIS portal. You can access the metadata record and other resources on the GIS portal by clicking on the “Explore” button (and choosing the “Go to resource” option) to the right of the “ArcGIS Open Dataset” text below.Category: RecreationOrganization: Allegheny CountyDepartment: Geographic Information Systems Group; Department of Information TechnologyTemporal Coverage: currentData Notes: Coordinate System: Pennsylvania State Plane South Zone 3702; U.S. Survey FootDevelopment Notes: noneOther: noneRelated Document(s): Data Dictionary (none)Frequency - Data Change: As neededFrequency - Publishing: As neededData Steward Name: Deb BeiberData Steward Email: gishelp@alleghenycounty.us

As one of the plain wetland systems in northern China, Baiyangdian Wetland plays a key role in ensuring the water resources security and good ecological environment of Xiong'an New Area. Understanding the current situation of Baiyangdian Wetland ecosystem is also of great significance for the construction of the New Area and future scientific planning. Based on the 10-meter spatial resolution sentinel-2B image provided by ESA in September 2017, combined with Google Earth high resolution satellite image (resolution 0.23m), the wetland ecosystem network distribution map and river network distribution map of in Baiyangdian basin in 2017 were drawn by artificial visual interpretation and machine automatic classification, which can provide reference for the wetland connectivity (including hydrological connectivity and landscape connectivity) in Baiyangdian basin. The spatial distribution data set of Baiyangdian Wetland includes vector data and raster data: (1) Baiyangdian basin boundary data (.shp); Baiyangdian basin river channel data (. shp); (2) Baiyangdian basin land use / cover classification data (including the classification data of Baiyangdian basin and the river 3 km buffer) (.tif); Baiyangdian basin constructed wetland and natural wetland distribution map (. shp); Baiyangdian basin slope map (. tif). The boundary of Baiyangdian basin in this dataset comes from the basic geographic information map of Baiyangdian basin provided by Zhou Wei and others. The DEM is the GDEM digital elevation data with 30m resolution. The original image data of wetland remote sensing classification comes from the sentinel-2B remote sensing image on September 20, 2017 provided by ESA. This data set uses the second, third, fourth and eighth bands of the 10m resolution in the image. The preprocessing operations such as radiometric calibration, mosaic and mosaic are carried out in SNAP and ArcGIS 10.2 software, and the supervised classification is carried out in ENVI software. The data used for river channel extraction is based on Google Earth high resolution satellite images. The research and development steps of this dataset include: preprocessing sentinel-2B image, establishing wetland classification system and selecting samples, drawing the latest wetland ecosystem network distribution map of Baiyangdian basin by support vector machine classification; based on Google Earth high-resolution satellite image (resolution 0.23m), this paper uses LocaSpaceViewer software to identify and extract river channels by manual visual interpretation. For the river channels with embankment, identify and draw along the embankment; for the river channels without embankment, distinguish according to the spectral difference between the river channels and the surrounding land use types and empirical knowledge, mark the uncertain areas, and conduct field investigation in the later stage, which can ensure that the identified river channels have been extracted. The identified river channels include the main river channel, each classified river channel, abandoned river channel, etc., and all rivers are continuous. It can effectively identify the channel and ensure the accuracy of extraction. According to the river network map of Baiyangdian basin obtained by manual visual interpretation, the total length of the river in Baiyangdian basin is about 2440 km, and the total area is 514 km2. Among them, there are 177 km2 river channels in mountainous area, with a length of 866 km, distributed in northeast-southwest direction, mostly at the junction of forest land and cultivated land; there are 337 km2 river channels in plain area, with a length of 1574 km. The Baiyangdian basin is divided into eight land use / cover types: river, flood plain, lake, marsh, ditch, cultivated land, forest land and construction land. The remote sensing monitoring results show that the wetland area of Baiyangdian basin accounted for 13.90% in 2017. Among all the wetland types, the area of marsh is the largest, followed by the area of flood plain, ditch accounts for about 1%, and the proportion of lake and river is less than 0.5%. Combined with the land use / cover classification map and the distribution of slope and elevation, it can be seen that nearly 60% of the area of forest land is distributed in 10 ° to 30 ° mountain area, and the rest of the land use / cover types are mainly distributed in 0 ° to 2 ° area. The elevation statistics show that nearly 80% of the lakes and large reservoirs are distributed in the height of 100 m to 300 m, the distribution of marsh is relatively uniform, mainly in the higher altitude area of 20 m to 300 m, the types of construction land, flood area and cultivated land are mainly concentrated in the area of 20 m to 100 m, and rivers and ditches are mainly concentrated in the area of 0 m to 100 m. Based on the classification results of land use / cover within the river, it can be found that the main land use type is wetland. Specifically, the types of marsh, flood area and lake are the most, while the types of ditch and river are less. With the increase of the buffer area, the proportion of non-wetland type gradually increased, while the proportion of wetland type gradually decreased. The main wetland types in 1-3km buffer zone on both sides of the river are marsh and flood zone. It is worth noting that nearly one third of the River belongs to cultivated land, that is, the river occupation is serious. In terms of area, about 1 / 3 rivers and 3 / 4 lakes are distributed in the river course. Most of the water bodies in the river course are controlled by human beings, but the marsh area in the river course only accounts for about 3% of the marsh area in the whole river course. In this study, 8 types of land features including river, flood plain, lake, marsh, ditch, cultivated land, forest land and construction land were selected. The total number of samples was 5199, of which 67% was used for supervised classification and 33% for accuracy verification of confusion matrix. The overall accuracy of support vector machine (SVM) classification results in Baiyangdian basin is 84.25%, and kappa coefficient is 0.82. River occupation will not only directly reduce the connectivity of wetlands in the basin, but also cause some environmental and economic problems such as water pollution. However, if the connectivity of wetlands is reduced, the ecological and environmental functions of wetlands will be destroyed, which will pose a great threat to the water security of the basin. Taking Baiyangdian basin as a whole, improving the connectivity of wetlands and enhancing the ecological and environmental functions of wetlands in the basin will help to improve the water ecological and environmental security of Xiong'an New Area and Baiyangdian basin.

Abstract

The Turkish National Employment Agency (ISKUR) provides services for individuals who register as unemployed through 109 offices in 81 provinces. The impact evaluation study was designed by researchers from the World Bank to evaluate the impact of the ISKUR vocational training programs. These programs average 336 hours over three months are available for a wide range of subjects, and are offered by both private and public providers. These training services were provided to over 250,000 registered unemployed in 2011.

The Turkey Vocational Training Program for the Unemployed Impact Evaluation 2010-2012 was the first randomized experiment of a large-scale vocational training program for the general unemployed population (not just for disadvantaged youth) in a developing country. The program was able to trace longer-term impacts up to three years post-training, by complementing a follow-up survey with administrative data from the social security agency. A sample of 5,902 applicants was randomly allocated to treatment and control groups within 130 separate courses. Excess demand among the unemployed for many of the courses offered by ISKUR provided the possibility for an over-subscription design. The evaluation was carried out in collaboration with ISKUR and under the guidance of the Ministry of Labor.

The baseline survey took place between 13 September, 2010, and 31 January, 2011. The follow-up survey was implemented approximately one year after the end of training, between December 27, 2011, and March 5, 2012. It collected data on employment outcomes, as well as individual and household well-being.

Geographic coverage

National

Analysis unit

• Vocational programs' students,
• Vocational programs' staff

Kind of data

Sample survey data [ssd]

Sampling procedure

The selection of provinces for evaluation began with a list of the 39 provinces which had at least two significantly oversubscribed training courses in 2009. These provinces were first stratified by whether they had an unemployment rate above or below the median of 10 percent in 2009. Ten provinces were then randomly selected from each strata with probability proportional to the percentage of individuals trained in 2009. Three additional provinces (Antalya, Gaziantep, and Diyarbakir) were included in the sample at the request of ISKUR because of their importance in representing varying labor market conditions across Turkey. As a result, 23 provinces were selected for inclusion in the evaluation.

Power calculations gave a target sample size of 5,700 individuals. This target was divided among the 23 provinces in proportion to the number of trainees in these provinces in the previous year. Thus Istanbul accounts for 21.8 percent of the sample, Kocaeli, Ankara and Hatay collectively 28 percent, and the remaining half of the sample is split among the other 19 provinces.

The evaluation team worked with regional ISKUR offices to determine the actual courses from within each province to be included in the evaluation. The key criteria used to decide which courses to include in the evaluation were i) the likelihood of the course being oversubscribed (which ensures the most popular types of training, for which there would be demand for further scale-up, are included); ii) inclusion of a diversity of types of training providers to enable comparison of private and public course provision; and iii) course starting and ending dates. The evaluation includes courses that started between October and December 2010 and finished by May 2011 (75 percent had finished by the end of February 2011). The timing of the evaluation was determined by the fact that it tends to be a time of year when people in Turkey are more likely to seek training through ISKUR.

This resulted in a set of 130 evaluation courses spread throughout Turkey, of which 39 were offered by private providers and the remainder were mainly government-operated. Courses were advertised and potential trainees applied to them following standard procedures. Applications were then screened to ensure they met the eligibility criteria of ISKUR and the course provider. Training providers were then asked to select a list of potential trainees that was at least 2.2 times capacity.

The ISKUR Management Information System (MIS) stratified applicants for each course by gender and whether or not they were less than 25 years old. Within these strata, the MIS randomly allocated trainees at the individual level into one of three groups: a treatment group who were selected for training, a control group who were not, and a waitlisted group who the training provider could select into the training if there were drop-outs. Since training providers are paid on the basis of number actually trained, if individuals assigned to treatment drop out of training, providers look to quickly fill in the empty spots.

The final evaluation sample consisted of 5,902 applicants, of which 3,001 were allocated to treatment and 2,901 to control groups. There were 173 individuals who applied to more than one course.

Mode of data collection

Face-to-face [f2f]

Response rate

Baseline: 90% Follow-up: 94%

Honey taken directly from 59 bee hives on the Hawaiian island of Kauaʽi was analyzed for glyphosate residue using ELISA techniques. Glyphosate residue was detected (> LOQ) in 27% of honey samples, at concentrations up to 342 ppb, with a mean = 118 ppb, S.E.M. 24 ppb. Of 15 honey samples store-purchased on Kauaʽi, glyphosate was detected in 33%, with a mean concentration of 41 ppb, S.E.M. 14. Glyphosate residue was not detected in two samples from the island of Molokai but was in one of four samples from the island of Hawaiʽi. Presence and concentration of glyphosate residues were geospatially mapped with respect to Hawaiian land divisions. Mapping showed higher occurrence of glyphosate that was over LOQ (48%) and concentrations of glyphosate (mean = 125 ppb, S.E.M. 25 ppb; N = 15) in honey from the western, predominantly agricultural, half of Kauaʽi versus the eastern half (4%, mean = 15 ppb; N = 1). Geographic Information System analysis of land use percentage was performed within a circular zone of 1 Km radius around each hive. Various land use types within each circular zone were transcribed into polygons and percent land use calculated. Only agriculture land use showed a strong positive correlation with glyphosate concentration. High glyphosate concentrations were also detected when extensive golf courses and/or highways were nearby. This suggests herbicide migration from the site of use into other areas by bees. Best management practices in use for curtailing pesticide migration are not effective and must be carefully re-assessed.

1. Basic data at river water quality monitoring points set by the Environmental Department; including station name, river basin, station coordinates, and more. 2. Station location map layer, packaged as a SHP layer in a zip file, including DBF, SHP, SHX, and other geographic data, for GIS mapping.