This is the new quarterly ArcGIS newsletter for Higher Education. It will contain all the important information for teaching and facilitating the use of ArcGIS across your university. We aim to publish newsletters in October, January, April and July. Lets get into it.

Hi, I'm Kiaran Ratcliffe a GIS Consultant within the Education Team at Esri UK. Esri is a company that creates and distributes GIS software, and my focus is on helping schools and universities access and use this software effectively. That means helping educators bring GIS into the classroom in ways that are engaging, inclusive, and relevant. We want students to leave school or university not just knowing how to use GIS, but understanding how to apply it to make a difference—socially, environmentally, and across all kinds of industries.It’s a really rewarding role. We get to support both students and teachers, and help them use modern spatial tools to explore the world, solve problems, and tell powerful stories with data.

We have 5 top tips to help you support your organisation or students to move towards a modern GIS workflow.

As of the 1st August 2024 the following changes will take effect:ArcMap (ArcGIS Desktop) will no longer be available ArcGIS Pro will only be licensable through named users This means that there will be no provision of single use (ESU) and concurrent use (EFL) licence codes for the wide scale adoption for ArcGIS Pro from the 1st August 2024.

GIS point feature class of Scottish marine protected area search feature habitats from survey undertaken by Cefas and JNCC in January 2013 at the three Fladen Grounds pMPAs. This was a Scottish Marine Protected Areas (SMPA) site identification survey. The main aims were to confirm the presence of the Priority Marine Features and MPA Search Features recommended for protection within the pMPAs and to gather groundtruth data to compare benthic assemblages between, and within/outside, the sites.

This is a web map service (WMS) for the 10-metre Land Cover Map 2023. The map presents the and surface classified into 21 UKCEH land cover classes, based upon Biodiversity Action Plan broad habitats.UKCEH’s automated land cover algorithms classify 10 m pixels across the whole of UK. Training data were automatically selected from stable land covers over the interval of 2020 to 2022. A Random Forest classifier used these to classify four composite images representing per season median surface reflectance. Seasonal images were integrated with context layers (e.g., height, aspect, slope, coastal proximity, urban proximity and so forth) to reduce confusion among classes with similar spectra.Land cover was validated by organising the 10 m pixel classification into a land parcel framework (the LCM2023 classified land parcels product). The classified land parcels were compared to known land cover producing a confusion matrix to determine overall and per class accuracy.

This web map service (WMS) is the 25m raster version of the Land Cover Map 2015 (LCM2015) for Great Britain and Northern Ireland. It shows the target habitat class with the highest percentage cover in each 25m x 25m pixel. The 21 target classes are based on the Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompass the entire range of UK habitats.The 25m raster web map service is the most detailed of the LCM2015 raster products, both thematically and spatially, and it is derived from the LCM2015 vector product. For LCM2015 per-pixel classifications were conducted, using a random forest classification algorithm. The resultant classifications were then mosaicked together, with the best classifications taking priority. This produced a per-pixel classification of the UK, which was then 'imported' into the spatial framework, recording a number of attributes, including the majority class per polygon which is the Land Cover class for each polygon.Find out more about Land Cover Map 2015 at ceh.ac.uk.LCM2015 is available for download to Catchment Based Approach (CaBA) Partnerships in the desktop GIS data package. Please contact your CaBA catchment host for further information.

Classifying trees from point cloud data is useful in applications such as high-quality 3D basemap creation, urban planning, and forestry workflows. Trees have a complex geometrical structure that is hard to capture using traditional means. Deep learning models are highly capable of learning these complex structures and giving superior results.Using the modelFollow the guide to use the model. The model can be used with the 3D Basemaps solution and ArcGIS Pro's Classify Point Cloud Using Trained Model tool. Before using this model, ensure that the supported deep learning frameworks libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.InputThe model accepts unclassified point clouds with the attributes: X, Y, Z, and Number of Returns.Note: This model is trained to work on unclassified point clouds that are in a projected coordinate system, where the units of X, Y, and Z are based on the metric system of measurement. If the dataset is in degrees or feet, it needs to be re-projected accordingly. The provided deep learning model was trained using a training dataset with the full set of points. Therefore, it is important to make the full set of points available to the neural network while predicting - allowing it to better discriminate points of 'class of interest' versus background points. It is recommended to use 'selective/target classification' and 'class preservation' functionalities during prediction to have better control over the classification.This model was trained on airborne lidar datasets and is expected to perform best with similar datasets. Classification of terrestrial point cloud datasets may work but has not been validated. For such cases, this pre-trained model may be fine-tuned to save on cost, time and compute resources while improving accuracy. When fine-tuning this model, the target training data characteristics such as class structure, maximum number of points per block, and extra attributes should match those of the data originally used for training this model (see Training data section below).OutputThe model will classify the point cloud into the following 2 classes with their meaning as defined by the American Society for Photogrammetry and Remote Sensing (ASPRS) described below: 0 Background 5 Trees / High-vegetationApplicable geographiesThis model is expected to work well in all regions globally, with an exception of mountainous regions. However, results can vary for datasets that are statistically dissimilar to training data.Model architectureThis model uses the PointCNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThe table below summarizes the accuracy of the predictions on the validation dataset. Class Precision Recall F1-score Trees / High-vegetation (5) 0.975374 0.965929 0.970628Training dataThis model is trained on a subset of UK Environment Agency's open dataset. The training data used has the following characteristics: X, Y and Z linear unit meter Z range -19.29 m to 314.23 m Number of Returns 1 to 5 Intensity 1 to 4092 Point spacing 0.6 ± 0.3 Scan angle -23 to +23 Maximum points per block 8192 Extra attributes Number of Returns Class structure [0, 5]Sample resultsHere are a few results from the model.

Esri UK is providing a digital mapping platform and expertise in biodiversity mapping for the National Education Nature Park. We are providing the Department of Education with ArcGIS Online - an extensible web-based mapping platform to provide staff and students with geospatial tools that will allow them to view, capture, store, analyse and monitor environmental and biodiversity data. We are also providing Professional Services to be delivered using an agile methodology, along with training to key stakeholders.To deploy geospatial tools to all schools, we are using the existing ArcGIS for Schools program.

Office for National Statistics' national and subnational Census 2021. Schoolchildren and full-time studentsThis dataset provides Census 2021 estimates that classify all usual residents aged 5 years and over in England and Wales. The estimates are as at Census Day, 21 March 2021. Schoolchild or full-time student indicator definition: Indicates whether a person aged 5 years and over was in full-time education on Census Day, 21 March 2021. This includes schoolchildren and adults in full-time education.Schoolchildren and students in full-time education studying away from home are treated as usually resident at their term-time address.Comparability with 2011: Broadly comparable.We have removed the category Schoolchild or full-time student for Census 2021 and replaced it with Student. In the 2011 Census people aged 4 years and over were asked to answer the question, in Census 2021 people aged 5 years and over were asked to answer the question. This data is issued at (BGC) Generalised (20m) boundary type for:Country - England and WalesRegion - EnglandUTLA - England and WalesLTLA - England and WalesWard - England and WalesMSOA - England and WalesLSOA - England and WalesOA - England and WalesIf you require the data at full resolution boundaries, or if you are interested in the range of statistical data that Esri UK make available in ArcGIS Online please enquire at content@esriuk.com.The data services available from this page are derived from the National Data Service. The NDS delivers thousands of open national statistical indicators for the UK as data-as-a-service. Data are sourced from major providers such as the Office for National Statistics, Public Health England and Police UK and made available for your area at standard geographies such as counties, districts and wards and census output areas. This premium service can be consumed as online web services or on-premise for use throughout the ArcGIS system.Read more about the NDS.

A saltmarsh habitat map derived from airborne data. The habitat map is a polygon shapefile showing site relevant habitat classes – this is a merged product of multiple sites across multiple years.

The habitat map is derived from CASI (Compact Airborne Spectrographic Imager) multispectral data, LIDAR (Light Detection and Ranging) elevation data, and other GIS products. The classification also uses ground data collected after the CASI capture.

The ground data is used to identify the characteristics of the different habitats in the CASI and LIDAR data. These characteristics are then used to classify the different habitats and fit them into one of the predefined classes.

The collection of ground data used in the classification has some limitations. It is not collected at the same time as the CASI or LIDAR; it is normally within a couple of months of CASI capture, therefore some variations between the CASI data and situation on site at the time of ground data collection are possible. A good spatial coverage of ground data around the site is recommended, although not always practically achievable. For a class to be mapped, there must have been samples collected for it on site. If the class is not seen on site or samples are not collected for a class, it cannot be mapped.

The habitat map was created using a supervised classification, which means ground data were used to train the model. The classifications had a quantitative accuracy assessment carried out on them in the form of a confusion matrix using ground data set aside and not used in training the classifier. Alongside this aerial photography captured simultaneously with the CASI data was used to check and make final improvements to the habitat map.

The Great Britain Historical Database has been assembled as part of the ongoing Great Britain Historical GIS Project. The project aims to trace the emergence of the north-south divide in Britain and to provide a synoptic view of the human geography of Britain at sub-county scales. Further information about the project is available on A Vision of Britain webpages, where users can browse the database's documentation system online.

These data were originally collected by the Censuses of Population for England and Wales, and for Scotland. They were computerised by the Great Britain Historical GIS Project and its collaborators. They form part of the Great Britain Historical Database, which contains a wide range of geographically-located statistics, selected to trace the emergence of the north-south divide in Britain and to provide a synoptic view of the human geography of Britain, generally at sub-county scales.

The first census report to tabulate social class was 1951, but this collection also includes a table from the Registrar-General's 1931 Decennial Supplement which drew on census occupational data to tabulate social class by region. In 1961 and 1971 the census used a more detailed classification of Socio-Economic Groups, from which the five Social Classes are a simplification.

This is a new edition. Data from the Census of Scotland have been added for 1951, 1961 and 1971. Wherever possible, ID numbers have been added for counties and districts which match those used in the digital boundary data created by the GBH GIS, greatly simplifying mapping.

A lookup file between unitary authorities and Department for Children Education Lifelong Learning and Skills areas in Wales as at 31st December 2024. (File Size - 16 KB)Field Names - UA24CD, UA24NM, DCELL24CD, DCELL24NMField Types - Text, Text, Text, TextField Lengths - 9, 17, 9, 20

Addy PopeHigher Education Manager - Esri UKStill think I am a glaciologistGIS consultant GIS EducationDidn't actually do any GIS as an undergrad.

My name is Addy Pope - i'm the one in the middle eating chipsticks. I am the sector manager for Higher Education at Esri UK. What does this mean? Well, I look after universities making sure that they have access to the best GIS tools for teaching and research.

It's easy to typecast a university as simply an education institution. But they can be much more than that. We already know that universities are like small cities and can use GIS to better manage their facilities, or even develop their business model.

In this asynchronous session, you will use some of the free GIS tools from the Teach With GIS website, created and maintained by the Esri UK education team. All of these tools are free to use and accessible as websites from laptops, tablets and mobile devices. We recommend that you view them on a laptop or tablet if possible, to give you plenty of screen space to see every detail. They do not require any logins or subscriptions. We want you to experience using modern, online GIS tools from the perspective of a student before you begin to create your own tools, maps, and lessons. We have chosen a range of tools that let you experience GIS as a tool to examine physical and human geography, and to compare and contrast over space and time.

How long will ArcMap (ArcGIS Desktop) be available?ArcMap will be available for the 1 st year of the new agreement (1 st August 2023 to 31 st July 2024). It is not anticipated that there will be a grace period at the end of this term. Why will ArcMap (ArcGIS Desktop) not be available beyond 31 st July 2024?