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Alternative to Generate Near Table in ArcMap 10.2

Alternative to Generate Near Table in ArcMap 10.2


I want to get the distance between each polygon in one layer (each rectangle in a fishnet grid over the area in question) and the nearest polygon in another layer, but "Generate Near Table" never finishes. Because I am limited to using school computers I can't allow the program to run continuously long enough to finish. Any ideas for a workaround or another tool I can use to gather the same information?

I do have the "nearest only" box checked. Within each grid cell are aggregated points. I need the distance from the grid cell to the nearest park (polygons in another layer file). That data will be joined to an existing table and used as a potential explanatory variable in an OLS regression analysis, in which the dependent variable is number of points within the grid cell.


Use eucledean raster distance to parks. Use grids to find statistics. To reduce time use reasonable cell size for distance raster, e.g. 50 m if this ie enough accurate for your purpose


Section Seven: Geoprocessing Specifically in ArcMap

As mentioned in the introduction for this chapter, one of the main goals of this class is to develop a foundation of geoprocessing tool comprehension. That comes only from reading about what tools can do, associating them with a category of related tools, and taking minute before running a specific tool to predict an outcome based on the input layers. Running the tool and examining the output either confirms or changes your future predictions, which in turn builds a broader geoprocessing tool comprehension. These skills are GIS software independent, as geoprocessing tools all accomplish the same tasks. The output of a clip tool run in ArcGIS will be the same as if the same data were to be run through a clip tool in QGIS, with the only real difference being the tool interface.

When comparing the tool interface for ArcGIS to another software, like QGIS, ArcGIS is actually more "user friendly" and less assuming that the user really understands concepts of GIS - defining a vector or raster, knowing what geometry types are available, and having the ability to predict outputs. As this class uses ArcGIS, the most common GIS software in use today, there are some specifics about the software that need to be covered, such as enabling extensions, filling in tool dialog boxes, the Results window, and knowing where to look to know if a tool is running, has completed successfully, completed with an error, or failed to run. Like previous chapters, the goal of this section is not to memorize how tasks are completed in the software, but instead to introduce the ideas and reasoning behind the tasks so when the tasks are presented in lab, the seem a bit familiar.

Figure 7.18: Comparing the Clip Tool Interface for QGIS to ArcMap
QGIS Clip ToolArcGIS Clip Tool
QGIS doesn't show shapefiles as a single item, but instead shows all the file and assumes the user knows to select the .shp file.ArcGIS offers shapefiles as a single file which is more user-friendly
Like the input selection box, the output box offer a large number of file types.ArcGIS only offers shapefiles to be saved inside folders and feature classes to be saved inside geodatabases (not shown).

7.7.2: Enabling Extensions and Launching Tools

ArcGIS, a proprietary software, costs money, unlike a opens source software like QGIS. And it costs a fair amount of money. With the exception of K-12 school and humanitarian non-profits, companies need to pay for not only the base software, but also for any upgrades and additional advanced toolboxes. In order to save costs, companies can elect to purchase the number of base licenses they need to match the number of employees and then just a few copies of the more advanced tools (called extensions ) to share among everyone. This sharing process, in ArcGIS, is referred to as enabling extensions. When an extension is enabled on one machine by a single technician on a shared company network, that extension cannot be used by any other technician on the same network until it is disabled by the first technician. Think about it like a public library - Instead of purchasing tons of copies of one particular best-seller, they purchase a limited number of copies, then lend them out free of charge to their registered users. While the book is checked out, no one else can read it, as it is not physically available to any other reader while in possession of the first reader. ArcGIS extensions work the same way - limited count extensions are only available to a few technicians at a time to check out (enable) and cannot be used by another technician until it is checked back in (disabled).

To enable an extension in ArcGIS, specifically:

  1. place a check mark in the box to the left of the extension name
  2. close the Extensions dialog box

If the extension is not available (not paid for or all the copies are checked out), instead of a check mark appearing in the box, a pop-up will appear stating “ The extension could not be activated. There is no license name currently available”

7.7.3: Launching Geoprocessing Tools

Within ArcGIS, geoprocessing tools are launched a couple of different ways: from the geoprocessing menu at the top of the software window, from the ArcToolbox window, and from the Search window. These three locations are available in both ArcMap and ArcCatalog, since all of the geoprocessing tools can be run in either software. In general, tools in ArcCatalog tend to run faster and with less errors, especially more complex or table-based tools. This doesn't mean tools will always fail or run slowly in ArcMap, as that isn't true, but when tools are run in ArcCatalog, there is no need for the software to draw any layers or organize a more complex layout of data and tools. In ArcCatalog, tools just run and save the data where it's been told to save instead of processing a more in-depth interface.

The Geoprocessing Menu

The ArcToolbox

The ArcToolbox is a collection of toolboxes and sub-toolboxes, organized by grouping similar tools together. For example, the Analysis Tools toolbox contains four sub-toolboxes - Extract, Overlay, Proximity, and Statistics. The Analysis toolbox "contains a powerful set of tools that perform the most fundamental GIS operations. With the tools in this toolbox, you can perform overlays, create buffers, calculate statistics, perform proximity analysis, and much more" (ArcGIS Help Menu). From there, each sub-toolbox contains a group of similar tools which perform a series of related operations. If you look inside the Proximity toolbox, you'll find a group of tools which examine how data is spatially related to other data. This toolbox explores ideas such as "What is the nearest fire hydrant to a specific office building?" (Near) "Which is the nearest fire hydrant to a whole series of homes represented by points?" (Generate Near Table) and "Where is the area which measures exactly 5 miles in infinite cardinal directions from a coffee shop represented by a point?" (Buffer).

For each individual tool found in the ArcToolbox, tools can be run in single mode, meaning the tool parameters are just filled out once and the result is (most often) a single dataset (vector, raster, or data table). Tools can also be run in batch mode meaning the tool is run multiple times in a row with individual inputs and outputs, however, it results in many output files.

While the ArcToolbox may seem overwhelming at first, after spending some time looking for tools and examining the structure, it will not only begin to make sense, but you will also start to find other “new to you” tools, resulting in “Ooo. There’s a tool for that? Neat!”

The Search Window

Searching for tools is a quick and easy way to find tools, especially if the toolbox is unknown. When the Tools option is selected in the Search window (as seen in this screenshot), the tool will only search for tools and not data or MXDs or images. As the tool name is typed into the search box, suggested are presented with the main toolbox listed in parenthesis.

Once you hit enter or click on one of the suggestions, the box populates with the best matches. The toolbox where the tool can be found is first in the list, followed by tools. In the example, the Buffer tool is found in the Analysis toolbox, thus the Analysis toolbox is listed first. Exact matches will be in bold , including in a tool name that contains the search word. Clicking on the tool name will launch the tool, clicking on the description below the tool name, listed in dark blue, will open the Help menu article for that tool, and clicking on the green toolbox path will open the ArcToolbox window, revealing where that tool lives.

7.7.4: Filling in Tool Dialog Boxes

Each geoprocessing tool is unique in the required inputs - vector or raster, specific geometry, numeric inputs, etc, but they are all the same in the fact they each take an 1. input layer (or layers), 2. require some parameters, and 3. have a line to define a name and place to save the new output layer. Some tools, such as those which are considered overlay, proximity, and extraction tools, require an “interaction” layer - the layer that defines the spatial comparisons as defined by the tool.

Saving Properly When Running the [insert name here] Tool

(Almost) every time you run a tool in ArcGIS, there will be an “Output Location” box (there are a select few tools that modify the input layer, thus do not require an output layer name and place). This box is tells ArcGIS where to save and what to call the output file of the tool after it runs. Within the data model for Introduction to GIS, there is the Results folder, a place to save the output layer. If you stick to the suggested data model and save all tool outputs to a single Results folder, you always know where the resulting layer from any tool is stored.

The Output Layer box. Depending on the tool, the box will be called slightly different things. Another note : ArcGIS will almost never have the heading "Output Shapefile", but instead will have "Output Feature Class" to reference any tool which produces a vector layer. The software is already a giant software, space wise, and to have little things like the word "shapefile" would make it even larger and slower to run.

To designate a place to save the output, click on the folder icon to the left of the Output Feature Class box (in the case of vectors) and drive to your results folder (keep opening folders until you get to your destination). Once you've found the place you'd like to save the output file, you need to give your output file a memorable and meaningful name. ArcGIS will default to the name of the tool (buffer, clip, erase, etc) appended to the original file name, which is neither memorable or meaningful. It is your job, nay, your duty , to rename the file according to your goal and task at hand, for example, “River_Buffer_50_meters”.

The Buffer tool has the first two requirements of (most) Geoprocessing tools - An input layer and an output location, using a memorable and meaningful layer name.

If a place and name is not assigned to a output dataset, the default location to save any tool is the Default Geodatabase and the default name is the name of the input layer with the name of the tool appended to the end.

Every tool initially populates the output location as the Default Geodatabase. It's necessary to change this location each and every time a geoprocessing tool is run in order to keep track of data as it's created.

Default Geodatabase

For those times you don’t designate a name and place for your output layer , your data will automatically save to the default geodatabase. The default geodatabase is found in the same place on all machines with ArcGIS installed: C:UsersusernameMy Documentsdefault.gdb. If you forget where your default geodatabase is, relaunching the tool and adding any file to the input line will automatically populate the output box with the path to your default geodatabase. It's possible to change the default geodatabase per project, creating one in your Results (or simillar) folder, then telling the MXD where to default all tool outputs. However, this change is for a single MXD, which is good if you're working on a large project, but kind of a hassle for simple, little, or quick projects. It's also possible to permnanlty chagne the location of the default geodatabase, however, the end result is no different then the one set up by ArcGIS upon install. Unless you do not have access to the Documents folder on that specific machine, it's generally accpetable to leave the default geodatabase as it is and learn the not very difficult path of "Documents > ArcGIS".

Green Circles, Yellow Exclamation Marks, and Red X’s, Oh My!

Geoprocessing tools all have some internal checks that occur when the tool is launched and again each time you populate a line in the dialog box. When the tool first launches, the required lines are marked with a green circle. This is the minimum amount of data the tool needs in order to do it’s job. As you move through each line of the dialog box, the tool continues to check your entry against the internal rules. If the entry you provided is acceptable, nothing happens (like the doctor says, “No news is good news”). If, however, the entry you’ve made doesn’t pass the test, the dialog box will put a yellow exclamation point (warning) or red x (error) next to the line’s description. Clicking on the symbol will result in a pop-up window explaining the warning or error. Warnings will (generally) allow you to continue with running the tool while errors will prevent the tool from being run and must be corrected before proceeding.


The minimum fields required for a geoprocessing tool to complete it's job are marked with a green circle.
When the internal rule checks of a tool violates an internal rule, but the tool will still run, a yellow exclamation point marks the line with the warning. Click the symbol to read the associated warning.

Alternative to Generate Near Table in ArcMap 10.2 - Geographic Information Systems

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Geographic Distribution and Regional Impacts of Oxyops vitiosa (Coleoptera: Curculionidae) and Boreioglycaspis melaleucae (Hemiptera: Psyllidae), Biological Control Agents of the Invasive Tree Melaleuca quinquenervia

K. M. Balentine, 1 P. D. Pratt, 1,* F. A. Dray, 1 M. B. Rayamajhi, 1 T. D. Center 1

1 USDA-ARS, Invasive Plant Research Laboratory, 3225 College Ave., Ft. Lauderdale, FL 33314

* Corresponding author, e-mail: [email protected]

Includes PDF & HTML, when available

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The invasive tree Melaleuca quinquenervia (Cav.) Blake is widely distributed throughout peninsular Florida and poses a significant threat to species diversity in the wetland systems of the Everglades. Mitigation of this threat includes the areawide release campaign of the biological control agents Oxyops vitiosa Pascoe and Boreioglycaspis melaleucae Moore. We summarize the results of this release effort and quantify the resulting geographic distribution of the herbivores as well as their regional impact on the target weed. A combined total of 3.3 million individual Melaleuca biological control agents have been redistributed to 407 locations and among 15 Florida counties. Surveys of the invaded a the geogO. vitiosa encompasses 71% of the Melaleuca infestation. Although released 5 yr later, the distribution of B. melaleuca is slightly greater than its predecessor, with a range including 78% of the sampled Melaleuca stands. Melaleuca stands outside both biological control agents' distributions occurred primarily in the northern extremes of the tree's range. Strong positive association between herbivore species was observed, with the same density of both species occurring in 162 stands and no evidence of interspecific competition. Soil type also influenced the incidence of biological control agents and the distribution of their impacts. The odds of encountering O. vitiosa or B. melaleucae in cells dominated by sandy soils were 2.2 and 2.9 times more likely than those predominated by organically rich soils. As a result, a greater level of damage from both herbivores was observed for stands growing on sandy versus organic-rich soils.

K. M. Balentine , P. D. Pratt , F. A. Dray , M. B. Rayamajhi , and T. D. Center "Geographic Distribution and Regional Impacts of Oxyops vitiosa (Coleoptera: Curculionidae) and Boreioglycaspis melaleucae (Hemiptera: Psyllidae), Biological Control Agents of the Invasive Tree Melaleuca quinquenervia," Environmental Entomology 38(4), 1145-1154, (1 August 2009). https://doi.org/10.1603/022.038.0422

Received: 14 January 2009 Accepted: 1 April 2009 Published: 1 August 2009

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4.6 Dynamic Brownian Bridge Movement Model (dBBMM)

With the wide-spread use of GPS technology to track animals in near real time, estimators of home range and movement have developed concurrently. Unlike the traditional point-based estimators (i.e., MCP, KDE with href/hplug-in) that only incorporate density of locations into home range estimation, newer estimators incorporate more data provided by GPS technology. While BBMM incorporates a temporal component and GPS error into estimates, dynamic Brownian Bridge Movement Models (dBBMM) incorporate temporal and behavioral characteristics of movement paths into estimation of home range (Kranstauber et al. 2012). However, estimating a movement path over the entire trajectory of data should be separated into behavorial movement patterns (i.e., resting, feeding) prior to estimating the variance of the Brownian motion (2 m). Overestimating the 2 m will cause an imprecision in estimation of the utilization distribution that dBBMM seeks to address (Kranstauber et al. 2012).

  1. Exercise 4.6 - Download and extract zip folder into your preferred location
  2. Set working directory to the extracted folder in R under File - Change dir.
  3. First we need to load the packages needed for the exercise

#TIME DIFF ONLY NECESSARY AS A MEANS TO EXCLUDE POOR DATA LATER
muleys$Date <- as.numeric(muleys$GPSFixTime)
timediff <- diff(muleys$Date)*24*60
muleys <-muleys[-1,]
muleys$timediff <-as.numeric(abs(timediff))

muleys$DT <-as.POSIXct(strptime(muleys$GPSFixTime, format='%Y.%m.%d %H:%M:%OS'))
muleys$DT

#EXCLUDE OUTLIERS AND POOR DATA FIXES

newmuleys <-subset(muleys, muleys$Long > -110.90 & muleys$Lat > 37.80)
muleys <- newmuleys
newmuleys <-subset(muleys, muleys$Long < -107)
muleys <- newmuleys

d8_dbbmm <- brownian.bridge.dyn(object=ld8, location.error=22, window.size=19, margin=7, dimSize=100,time.step=180)

dataD8 <- subset(muleys, muleys$id == "D8")
dataD8$id <- factor(dataD8$id)
d8 <- move(x=dataD8$X, y=dataD8$Y, time=as.POSIXct(dataD8$GPSFixTime,
format="%Y.%m.%d %H:%M:%S"), proj=CRS("+proj=utm +zone=12 +datum=NAD83"),
data=dataD8, animal=dataD8$id)
100
d8_dbbmm <- brownian.bridge.dyn(object=d8, location.error=22, window.size=19,
margin=7, dimSize=100,time.step=180)
plot(d8_dbbmm)
contour(d8_dbbmm, levels=c(.5,.9,.95,.99), add=TRUE)
show(d8_dbbmm)

par(mfcol=1:2)
plot(loc2, , col=3, lwd=2, pch=20, xlab="location_east",
ylab="location_north")


3. Population Exposure to Dissolved Arsenic

[34] We estimate for each geologic-geomorphic region the numbers of people who are exposed to various concentrations of groundwater arsenic by combining the above geostatistical modeling with demographic information obtained from the 1991 census conducted by the Bangladesh Bureau of Statistics (BBS) [1996] . This document reports data for 489 thanas throughout Bangladesh. Based on this data, the population of Bangladesh is estimated to be about 125 million people comprised of 51.48% males and 48.52% females, and average ages are estimated to be 23.10 yr. for males and 22.23 yr. for females. The Central Intelligence Agency (CIA) [2001] estimates the 2001 national population to be 131 million people with a population growth rate of 1.59% per year. However, 2001 population numbers for each of the 489 thanas are currently unavailable, and thus the demographic estimates based on 1991 data are used here.

[35] The BGS and DPHE survey data provides for each sample well: (1) the thana in which the well is located and (2) a GPS latitude-longitude reference that we have used to identify the geologic-geomorphic region in which the well is located. The BBS census data provides the population size of each thana. Based on this information, we use the steps below to assign each person in Bangladesh to a sample well and thus to a region. The following apply for each thana.

[36] 1. If there are sample wells in the thana (as in 433 of the 489 thanas), then we assign an equal number of people in the thana to each sample well. If every sample well in the thana lies in the same geologic-geomorphic region, then everyone is assigned to that region. And if the sample wells in the thana lie in several regions, then people are assigned to those regions based on the proportion of sample wells in each region.

[37] 2. If there are no sample wells in the thana (as in 56 of the 489 thanas), then we identify the geologic-geomorphic region in which the thana is located, and we assign an equal number of people in the thana to each sample well in the region. Thus we assume that the distribution of arsenic concentration in the unsampled thana is the same as the distribution of arsenic concentration in the geologic-geomorphic region as a whole.

[38] By steps 1 and 2, we calculate for each region the numbers of people who are exposed to the various sample arsenic concentrations in the region. Then, we estimate a finite distribution of concentration to which the national population is exposed by summing the regional distributions. We estimate that that about 46 million people are exposed to concentrations greater than 10 μg/L and about 28 million people to concentrations greater than 50 μg/L. BGS and DPHE [2001] estimate 57 million and 35 million people exposed to concentrations of 10 μg/L and 50 μg/L respectively using disjunctive kriging. Furthermore, BGS and DPHE [2001] also estimate 46 million and 28 million by multiplying the percentage of contaminated wells in a thana by the population of the thana. Since most thanas are found within a geologic-geomorphic region, our exposure estimates coincide closely with this approach.

[39] Figure 1 shows two cumulative distributions of arsenic concentration: that over the sample wells and that over the Bangladeshi population. The distribution over wells has a mean of 63 μg/L and a standard deviation of 140 μg/L while the distribution over people has a mean of 56 μg/L and a standard deviation of 123 μg/L. Thus the sample well distribution would not be accurate for calculating health effects. The distributions differ largely because of the high population density in Dhaka and to a lesser extent because of a nonuniform spacing of the sample wells.

[40] The two cumulative graphs in Figure 1 can be compared as follows. For the interval of arsenic concentrations below the detection limit (0.25–0.50 μg/L), the fraction of arsenic over people (32%) is greater than the fraction of arsenic over wells (27%). This discrepancy is due primarily to the dense population of Dhaka, located in the clay and alluvium regions (32 and 33) of the Eastern Terraces. As reported in Table 2, these regions contain 4,782 people/km 2 and 2,797 people/km 2 and contain mostly nondetection wells. For concentrations between the detection limit and 50 μg/L, the fractions of people are approximately equal to the fractions of wells, and thus the cumulative graphs are approximately parallel. For concentrations between 50 μg/L and 100 μg/L, the fractions of people are less than the fractions of wells, and thus the cumulative graph for wells rises to meet that for people. And for concentrations above 100 μg/L, the two distribution graphs are very close. Therefore, although the sample well distribution would not be accurate for calculating health effects, it would not be grossly different than that using the distribution over people since the main difference is for concentrations below 100 μg/L.

[41] Table 2 reports for each of the 34 selected regions the estimated number and percent of people who obtain drinking water from wells with arsenic concentrations above the detection limit. In sections 5.1, 5.2, we estimate regional health effects by estimating the health effects for these regional subpopulations. This approximation is suitable since the estimated health effects of exposure to arsenic concentrations below the detection limit are negligible. Note that as the number of wells in Bangladesh increases, our distributions of exposure are unchanged. This assumes that the depth distribution of wells does not change over time.

[42] The national population of people who use wells with arsenic concentrations above the detection limit consists of about 85 million people (68% of the entire population of Bangladesh). The distribution of arsenic concentration over this national subpopulation has a sample mean of 82 μg/L and sample standard deviation of 142 μg/L. This sample distribution of exposure is used in section 4.3 for the estimation of arsenicosis dose response functions.


Structure from motion (SfM)

Structure from motion (SfM) is an established and widely used method to generate 3-D models in the geosciences (Favalli et al., 2012 Westoby et al., 2012 Smith et al., 2016). It is increasingly used in geomorphology for the characterisation of topographic surfaces and analysis of spatial and temporal geomorphic changes, with an accuracy comparable to existing laser scanning and stereophotogrammetry techniques in close-range scenarios (Aguilar et al., 2009 Thoeni et al., 2014 Smith et al., 2016 Wilkinson et al., 2016). SfM photogrammetry utilises a sequence of overlapping digital images of a static subject taken from different spatial positions to produce a 3-D point cloud. Image metadata for image matching are used to estimate 3-D geometry and camera positions using a bundle adjustment algorithm (Smith et al., 2016). The workflow uses an automated scale-invariant feature transform (SIFT) image matching method (Smith et al., 2016). The advancement in new image matching algorithms has eased and automated the SfM workflow compared to stereophotogrammetry (Remondino et al., 2014 Smith et al., 2016).

Applications in geomorphology include laboratory flume experiments (Morgan et al., 2017), rockslides and landslides (Niethammer et al., 2012 Russell, 2016), eroding badlands (Smith and Vericat, 2015), fluvial morphology (Javernick et al., 2014 Dietrich, 2015 Bakker and Lane, 2016 Dietrich, 2016a, b), peatland microforms (Mercer and Westbrook, 2016), glacial process dynamics (Piermattei et al., 2016 Immerzeel et al., 2017), river restoration (Marteau et al., 2016), mapping coral reefs (Casella et al., 2016), beach surveying (Brunier et al., 2016), soil erosion (Snapir et al., 2014 Balaguer-Puig et al., 2017 Prosdocimi et al., 2017 Vinci et al., 2017 Heindel et al., 2018), volcanic terrains (James and Robson, 2012 Bretar et al., 2013 Carr et al., 2018), porosity of river bed material (Seitz et al., 2018), grain size estimation of gravel bed rivers (Pearson et al., 2017), and coastal erosion (James and Robson, 2012). In addition, SfM has also been widely used in archaeology for photogrammetric recording of small-scale rock art and artefacts and large-scale archaeological sites (Sapirstein, 2016, 2018 Sapirstein and Murray, 2017 Jalandoni et al., 2018).

The increased uptake of this method is primarily due to its relatively low cost, high portability, and ease of data processing workflow. Much of the SfM workflow is automated in a range of relatively affordable commercial software (e.g. Agisoft PhotoScan, SURE, Photomodeler), closed source-free software (e.g. VisualSfM, CMPMVS), and open-source software (e.g. Bundler, OpenMVG, OpenMVS, MicMac, SFMToolkit).

There is a considerable amount of available literature on SfM techniques and workflows. A detailed discussion of the technique is found in several available papers: e.g. Westoby et al. (2012) Fonstad et al. (2013) Thoeni et al. (2014) Micheletti et al. (2015a, b) Eltner et al. (2016) Ko and Ho (2016) Smith et al. (2016) Schonberger and Frahm (2016) Bedford (2017) Zhu et al. (2017) Ozyesil et al. (2017).

Several studies have reported high accuracy in 3-D topographic data obtained using SfM when compared to methods such as terrestrial laser scanning (TLS) or RTK-GPS surveys (Harwin and Lucieer, 2012 Favalli et al., 2012 Andrews et al., 2013 Fonstad et al., 2013 Nilosek et al., 2014 Caroti et al., 2015 Dietrich, 2015 Palmer et al., 2015 Clapuyt et al., 2016 Koppel, 2016 Piermattei et al., 2016 Panagiotidis et al., 2016 Wilkinson et al., 2016). A detailed comparison of cost–benefit, data acquisition rate, spatial coverage, operating condition, resolution, and accuracy analysis between TLS and SfM techniques is found in Smith et al. (2016) and Wilkinson et al. (2016). The recent advances in structure-from-motion approaches (SfM) have yet to be been widely applied to micro-scale landforms, such as rock breakdown features.

Here we test the use of SfM for very high-resolution (sub-millimetre) application. Our approach uses high-resolution digital photography (from consumer-grade camera) combined with SfM workflow. We evaluate errors in our DEMs using checkpoints in the field and validate our approach through a series of controlled experiments. We also assess the error propagation with distance from the control target in DEMs generated in our experiment. We find that SfM offers a robust approach for rock breakdown studies.

Our work provides an alternative and/or additional cost-effective, transportable, and fieldwork-friendly method for use in geomorphological studies that require the production of high-resolution topographic models from field sites. Below, we outline the development and test of our approach in the field and under controlled conditions. We provide a detailed guide so that others may adopt our approach in their research.

Figure 1A schematic diagram of the typical workflow for digital elevation model (DEM) production described in this study.


Particulate matter modelling techniques for epidemiological studies of open biomass fire smoke exposure: a review

Smoke exposure from landscape and coal mine fires can have severe impacts on human health. The ability of health studies to accurately identify potential associations between smoke exposure and health is dependent on the techniques utilised to quantify exposure concentrations for the population at risk. The evolution of spatial modelling techniques capable of better characterising this association has potential to provide more precise health effect estimates. We reviewed the literature to identify and assess the spatial modelling techniques available to estimate smoke PM2.5 or PM10 concentrations from open biomass or coal mine fires. Four electronic databases were searched: MEDLINE, EMBASE, Scopus and Web of Science. Studies were included if they utilised any method for modelling the spatial distribution of PM2.5 or PM10 concentrations from open biomass or coal mine fires and had applied the modelled PM to health data. Studies based on un-adjusted monitoring data, or which were not in English, were excluded. We identified 28 studies which utilised five spatial modelling techniques to assess exposure from open biomass fires: dispersion models, land use regression, satellite remote sensing, spatial interpolation and blended models. No studies of coal mine fires were identified. We found the most effective models combined multiple techniques to enhance the strengths and mitigate the weaknesses of the underlying individual techniques. “Blended” models have the potential to facilitate research in regions currently under represented in biomass or coal mine fire studies as well as enhancing the power of studies to identify associations with health outcomes.

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7. Frequency band 37-40 GHz

7.1 Current use of the spectrum

40. In Canada, fixed and mobile services are allocated in the frequency band 37-40 GHz, fixed-satellite service Footnote 16 ( space-to-Earth ) in the frequency band 37.5-40.0 GHz , space research service ( space-to-Earth ) in the frequency band 37-38 GHz , and mobile-satellite service Footnote 17 ( space-to-Earth ) in the frequency band 39.5-40 GHz all on a co-primary basis while Earth exploration-satellite service ( space-to-Earth ) is allocated on a secondary basis in the frequency band 37.5-40 GHz . In addition, the frequency band 37󈛌 GHz is available for high-density applications in the fixed service in accordance with footnote 5.547 of the ITU’s Radio Regulations . An illustration of the Canadian frequency allocations in the frequency band 37-40 GHz is shown in figure 4 below.

Figure 4 – Canadian frequency allocations in the band 37-40 GHz

Notes: Primary services are shown in all uppercase letters
Secondary services are shown with uppercase and lowercase letters

This figure shows the Canadian frequency allocations in the band 37-40 GHz . It shows that fixed and mobile services are allocated from 37-40 GHz . It shows that fixed-satellite service ( space-to-Earth ) is allocated in the frequency band 37.5-40.0 GHz , space research service (space-to-Earth) is allocated in the frequency band 37-38 GHz , and mobile-satellite ( space-to-Earth ) is allocated in the frequency band 39.5-40 GHz all on a co-primary basis. It shows that Earth exploration-satellite service ( space-to-Earth ) is allocated on a secondary basis in the frequency band 37.5-40 GHz . It shows that there is a footnote No. 5.547 for the frequency band 37-40 GHz . It also shows footnotes C51 ( 38.6-40 GHz ) and C49, C50 ( 39.5-40 GHz ).

41. In 1999, ISED designated 800 MHz of spectrum ( 38.7-39.1 GHz and 39.4-39.8 GHz ) for licensing via auction and 600 MHz of spectrum ( 38.6-38.7 GHz paired with 39.3-39.4 GHz , and 39.1-39.3 GHz paired with 39.8-40 GHz ) for point-to-point microwave systems, licensed on a grid-cell basis through a first-come first-served (FCFS) process. Footnote 18 Also, the frequency band 38.4-38.6 GHz was made available under the same FCFS licensing process for unpaired point-to-point and unpaired multipoint communication systems. The remaining 1400 MHz ( 37-38.4 GHz ) were reserved for future use by the fixed service.

42. In December 2014, ISED published the New Licensing Framework for the 24, 28 and 38 GHz Bands and the Decision on a Licence Renewal Process for the 24 and 38 GHz Bands . This framework allows for point-to-multipoint systems in the frequency ranges 38.6-38.7 GHz , 39.1󈛋.4 GHz, and 39.8󈛌 GHz and it established a new FCFS licensing process for available spectrum in the frequency bands 38.7-39.1 GHz and 39.4-39.8 GHz with site-specific licences. Existing auctioned licences were eligible for a 10 year renewal term if conditions of licence were met and annual FCFS licences with deployment were renewed. Footnote 19 Furthermore, it was recognized that site-specific licences were the most efficient and consistent approach to authorizing high frequency spectrum for backhaul and, therefore, renewed auctioned licences were not provided with a high expectation of renewal after their renewed 10 year term. Figure 5 shows how fixed services are currently using the frequency band 37-40 GHz .

Figure 5 – Current use of the frequency band 37-40 GHz by fixed service

This figure shows the current use of the frequency band 3 7-40 GHz by fixed services. It shows that the frequency range 37-38.4 GHz is reserved for future use. It also shows the frequency range used for unpaired p oint-to-point ( p-p ) and point-to-multipoint ( p-mp ) services (38.4-38.6 GHz), FCFS grid cell licences for p-p and p-mp systems ( 38.6-38-7 GHz , 3 9.1-39.4 GHz , and 39.8-40 GHz ), and Tier 3 auctioned licences and FCFS site licences for p-p and p-mp systems ( 38.7-39.1 GHz and 39.4-39.8 GHz ).

43. According to the ISED’s records, the frequency band 38.6-40 GHz is used by operators of fixed point-to-point and point-to-multipoint systems for wireless backhaul and to offer broadband wireless access to clients. There are currently 28 tier 3 auctioned licences held by four licensees. TeraGo Networks is the major licence holder with 25 licences and ABC Allen, I-Netlink Inc. and Telus each hold one licence. The Telus licence was issued in 2003 and is scheduled for a renewal decision in 2018 the other licences were renewed in 2015. These licence areas include a mixture of rural and urban areas in British Columbia, Alberta, Manitoba and Ontario.

44. There are 80 active grid cell licences held by nine licensees that have collectively deployed roughly 1900 sites. Rogers, Telus, TeraGo Networks and Freedom Mobile collectively hold 90% of these licences. Since 2014, when site licences were made available under the New Licensing Framework for the 24, 28 and 38 GHz Bands and Decision on a Licence Renewal Process for the 24 and 38 GHz Bands , ISED has issued 245 licences for 386 individual sites to seven licensees. Freedom Mobile holds 80% of these licences. Data from ISED’s licensing database shows that approximately 88% of all sites (including both grid cell and site-specific licences) are located within the greater areas of Vancouver, Calgary, Edmonton, Toronto, Ottawa and Montreal.

45. Finally, there are also two fixed stations operated on a developmental basis in the frequency band 37.6-38.6 GHz .

46. There is currently no satellite use, including fixed-satellite, space research, mobile-satellite , or Earth exploration-satellite services, in the frequency band 37.5-40 GHz. However, the fixed-satellite industry has expressed interest in this band paired with the Earth-space band around 50 GHz as the next bands to be commercially developed since the Ku and Ka bands are becoming more and more congested.

7.2 Changes to spectrum utilization policies

47. Similar to the 28 GHz frequency band, ISED is proposing to make the frequency band 37󈛌.0 GHz available for flexible use for terrestrial services.

48. The use of the fixed-satellite service applications in this band is currently limited to those that would pose minimal constraints upon the deployment of fixed service systems, such as a small number of large antennas for feeder links, as specified in footnote C51 in the CTFA. In making available the band 37-40 GHz for flexible use for terrestrial services, ISED believes that we should uphold the principle of not unduly constraining the deployment of terrestrial services throughout the band where satellite service also has an allocation. Therefore, ISED proposes to continue the limitation of the fixed-satellite service to applications which would pose minimal constraints to terrestrial services (including both fixed and mobile services) and extends the limitation to 37.5-40 GHz . However, ISED also recognizes the need for the FSS to continue having access to the band. A sharing mechanism to accommodate these services would be developed in collaboration with stakeholders, (see section 7.4).

49. In order to accommodate flexible use for terrestrial services in the band (as discussed above), footnote C51 in the CTFA would be modified as follows:

MOD C51 (CAN󈚵) The frequency band 38.637.5-40 GHz is being licensed for applications in the fixed and mobile services, which will be given priority over fixed‑satellite service systems sharing this frequency band spectrum on a co‑primary basis. Fixed-satellite service implementation in this frequency band spectrum will be limited to applications that will pose minimal constraints upon the deployment of fixed and mobile service systems, such as a small number of large antennas for feeder links.

50. ISED will continue to license the 38.4-40 GHz band under the New Licensing Framework for the 24, 28 and 38 GHz Bands and Decision on a Licence Renewal Process for the 24 and 38 GHz Bands . In the future, when alternative licensing processes have been finalized and the timing of their implementation has been determined, a moratorium on issuing new site-specific licences may be required. ISED is proposing to treat 28 GHz and 38.4-40 GHz bands differently with respect to moratoriums on issuing new licences. Unlike the 28 GHz band, which currently has no fixed service users, the 38.4-40 GHz band is currently used to deliver backhaul for mobile services and for enterprise wireless solutions. An immediate moratorium may impact existing and potential users of this band with respect to their current and future deployment plans.

Question 7-2: ISED is seeking comments on whether a moratorium on the issuance of new licences under the New Licensing Framework for the 24, 28 and 38 GHz Bands and Decision on a Licence Renewal Process for the 24 and 38 GHz Bands is required at this time.

7.3 Changes to band plan

51. For the terrestrial services, there is no existing band plan defined in the frequency band 37󈛊.4 GHz. The frequency band 38.4-38.6 GHz is divided into four blocks of 50 MHz each.

52. In the frequency band 38.6-40.0 GHz , the current Canadian band plan comprises fourteen (14) 50 MHz frequency blocks (see figure 6), with both FDD and TDD systems permitted. Footnote 20 As stated earlier, licences in this band have been granted through both auction and first-come , first-served (FCFS) processes and include a mixture of area licences (based on Tier 3 areas as well as licensee-defined areas using grid cells) and site licences therefore, the licence duration and authorized frequency blocks may differ.

Figure 6: Current Canadian band plan for 38.6-40 GHz

This figure shows the pre-2014 decision canadian band plan for 38.6-40 GHz as well as the post-2014 decision band plan. The pre-2014 decision band plan shows two consecutive sets of 14×50 MHz blocks labelled alphabetically from A to N. Each block in the first set is paired with its matching block in the second set (e.g. Block A in the first set is paired with Block A in the second set). Blocks A,B,K,L,M, and N are labelled as FCFS. Blocks C,D,E,F,G,H,I, and J are labelled as Auction. The post-2014 decision band plan shows two consecutive sets of 14×50 MHz blocks labelled alphabetically from A to N. It also shows that all of the blocks are FCFS ( site-licensed ).

53. In the U.S., as part of its further consultation on band sharing and coordination mechanisms for the frequency band 37-37.6 GHz , the band plan for this frequency band is currently under development. In particular, the FCC is considering whether or not to establish a 100 MHz minimum channel size while allowing users to aggregate these channels into a larger channel size, up to a maximum of 600 MHz, where available. Other options are also being considered. The FCC has not finalized its rules on this matter at this time. For the band 37.6-40 GHz , the FCC has adopted a new band plan comprised of 200 MHz blocks. The FCC also adopted rules that allow both FDD and TDD implementations.

54. In order to benefit from the ecosystem that develops in the U.S. and simplify coordination of fixed and mobile services along the Canada-U.S . border, ISED is proposing that Canada adopt the same band plan in the entire 37-40 GHz range as the U.S. Given the development in the U.S. with respect to a new band plan in the frequency range 37-37.6 GHz , adopting a Canadian band plan at this time would be premature and could undermine the benefits of equipment harmonization. It is therefore proposed that the development of a Canadian band plan for this frequency range be deferred to a later date. The overall proposed band plan for the frequency band 37-40 GHz is shown in figure 7 below. Similar to the 28 GHz band, this band plan would not preclude any type of duplexing scheme to be deployed.

Figure 7: Proposed Canadian 37-40 GHz frequency band plan

This figure shows the proposed Canadian 37-40 GHz frequency band plan. It shows the frequency range from 37-37.6 GHz is labelled as "to be determined". The frequency range from 37.6-40 GHz is divided into 12 200 MHz blocks with no labels.

7.4 Band sharing with other services

55. In order to facilitate the introduction of flexible use services in this frequency band, provisions will need to be developed to ensure their co-existence with existing services.

7.4.1 Coexistence between flexible use terrestrial stations and earth stations in the fixed-satellite service (space-to-Earth)

56. Currently, the coexistence of fixed terrestrial stations and FSS earth stations is addressed through coordination on a site-by-site basis, as described in section 6.5 above. It is noted, however, that there has been no deployment by the satellite service in this band yet.

57. Since FSS earth stations receive signals from satellites transmitting in this frequency band, they could be subject to interference from the emissions of new flexible use terrestrial stations. Preliminary studies provided to the FCC indicate that FSS earth stations would require a separation distance of no more than 2 km from a flexible use terrestrial station. Footnote 21 The proposed modification to Canadian footnote C51 does not allow for the ubiquitous deployment of FSS in the band. As a result, the coordination of flexible use terrestrial stations and FSS earth stations is likely to be manageable as the number of FSS earth stations will likely be limited to a small number.

58. The considerations above are very similar to those concerning the coexistence of flexible use terrestrial stations and FSS earth stations in the frequency band 27.5-28.35 GHz. Therefore, ISED proposes to adopt similar mechanisms, using a PFD or a distance threshold as a trigger for coordination, to manage the band sharing in this band.

A. ISED seeks comments on the proposal to require site-by-site coordination between proposed flexible use terrestrial stations and FSS earth stations in the frequency band 37.5󈛌 GHz when a pre-determined trigger threshold is exceeded.

B. If site-by-site coordination is proposed, what coordination trigger and value would be the most appropriate (e.g. PFD or distance threshold)?

C. ISED is also inviting proposals for specific additional technical rules on flexible use stations and FSS earth stations (e.g. site shielding) that could facilitate more efficient sharing between terrestrial and earth stations.

7.4.2 Geographic restrictions on the deployment of earth stations

59. Similar to the decisions made in the 28 GHz band, the FCC adopted new mechanisms to restrict the areas in which new FSS earth stations can be deployed. This was done to ensure that fixed-satellite services do not restrict the deployment of new UMFUS systems in core urban areas and around major infrastructure where implementation of flexible use systems would be most likely. Unlike the 28 GHz band, in the frequency band 37.5-40 GHz, it is the FSS earth station that could experience interference from the flexible use terrestrial stations. In the U.S., an FSS earth station can obtain protection from flexible use stations by obtaining an UMFUS licence, entering into an agreement with an UMFUS licensee or if the FSS earth station conforms to a set of conditions that restrict the geographic areas in which an FSS earth station can be deployed. In addition, there are provisions that would limit the number of earth stations that would be protected from harmful interference by UMFUS stations in a given licence area.

60. ISED is of the view that the FCC’s approach is not appropriate in the Canadian context. However, similar to the potential band sharing mechanisms in the 28 GHz band, ISED may consider using other methods to facilitate flexible use systems deployment in core urban areas and major infrastructure by limiting the deployment of FSS earth stations in these areas.

A. ISED is seeking comments on whether there should be restrictions on the geographic areas in which new FSS earth stations can be deployed in the frequency band 37.5󈛌 GHz.

B. If geographic restrictions on FSS earth stations are proposed, ISED is inviting detailed proposals on how they could be implemented, and what areas should be targeted?

7.4.3 Band sharing with the space research service (SRS) ( space-to-Earth ) and mobile-satellite service (MSS) ( space-to-Earth )

61. As noted above, the frequency band 37-38 GHz is allocated to the space research service (space-to-Earth) on a primary basis. Also, the band 39.5-40 GHz is allocated to the MSS, and is limited to use by the government of Canada. In the U.S. , in order to enable band sharing, the FCC created coordination zones around its three SRS earth stations where deployment by UMFUS licensees within these zones requires prior coordination. There is no existing or planned SRS or MSS operation in Canada therefore, ISED is not proposing specific restrictions on terrestrial services at this time. However, in the event that SRS or MSS begins deployment in these bands, flexible use licensees may be subject to future technical provisions in order to facilitate co-existence .

Question 7-6: It is proposed that, should SRS and/or MSS systems be deployed, flexible use licensees in the band 37.6-40 GHz may be subject to technical provisions to facilitate co-existence . Comments are sought. ISED notes that any such technical provisions would be established through a future consultation process.

7.5 Treatment of existing users

62. At mmWave frequencies, the difference between certain technical characteristics of fixed and mobile operations may be hard to distinguish. The high signal attenuation in mmWave bands will require the use of highly directive antennas for both fixed and mobile systems, and could offer the opportunity to reuse frequencies in the band at much closer distances than in lower frequency bands. On the one hand, this could enable very effective coordination between existing fixed users and future flexible use licensees. On the other hand, some of the new flexible use systems are expected to be ubiquitous in coverage, which could present coordination challenges in areas that already contain fixed systems, particularly if the two systems are operated by two different service providers. As 5G technology continues to develop, there will be more clarity on how effectively flexible use systems and existing fixed service systems will be able to co-exist. In the meantime, ISED is considering several options on the treatment of existing users as described in the following paragraphs.

63. In 2014, when the decision Footnote 22 was made to renew these licences, it was determined that site-specific licences were the most efficient and consistent approach to authorizing high frequency spectrum and therefore new licences issued through the renewal process were not provided with a high expectation of renewal after their 10-year term. The use of this spectrum is evolving to include mobile in addition to fixed use services and as such, a licensing process that does not distinguish between the two will provide more flexibility for operators to deploy and adapt their networks as they see fit. In moving from fixed licensing to flexible use, ISED is considering two options for the treatment of existing Tier 3 licences at the end of the renewed 10 year term.

64. The first option is to convert the Tier 3 fixed service licences to flexible use licences. The existing licences were issued in accordance with the current band plan, i.e., in paired blocks of 50 MHz (see figure 6) and would not align with the proposed new band plan (see figure 7). Therefore, if ISED decides to convert existing Tier 3 area fixed licences to flexible use licences, it is proposed that those licences would be aligned with the new band plan in order to maximize the amount of cleared spectrum. It is noted that flexible use licences would be expected to be much more valuable and in demand than fixed. Furthermore, technology developments and/or network re-design may provide increased efficiency which would permit continued provision of service using less spectrum. Therefore, ISED could consider issuing new licences at the end of the current term, for a lesser amount of spectrum. The new amount of spectrum could be determined by using a percentage of the current amount.

65. The second option is to issue site-specific licences for sites currently in operation at the end of the licence term. These new site-specific licences could then be treated the same way that the current site-specific licences would be treated, i.e. either with or without protection from new flexible use licensees (see section 7.5.2 below).

7.5.2 Grid cell and site-specific FCFS licences

66. Grid cell and site-specific licences are issued on an annual basis. Licensing under these approaches provides for very efficient access to spectrum in that a licence is only issued for the area or site in which the licensee intends to deploy. Furthermore, these licences could make co-ordination with future flexible use licensees relatively straight-forward as the specific location of each transmitter is known. As such, ISED is considering two options for the treatment of existing grid cell and site-specific users.

67. First, given the potential for improved coordination (both through the expected improvements in technology capability and the limited geographic areas of licences), ISED could allow these licensees to continue operating in the band and be protected from interference from new flexible use licensees. New licensees would be required to coordinate with the existing licensees by deploying around their sites or by other means determined between the licensees. This approach to treating these users would provide access to the spectrum for 5G with minimal impact on existing users. Furthermore, given the expected capabilities of technology in this band, this approach will likely be technically feasible. However, it could also severely limit deployment of 5G in major urban areas (as discussed in section 7.1, 88% of grid cell and site-specific licences are operating in the six largest urban areas).

68. A second approach would be to allow for them to continue operating on a secondary basis to flexible use licences. This approach would provide no protection for existing licensees from interference caused by new flexible use systems but would allow them to continue operating, at least until 5G systems are deployed in their specific area. It is proposed that under this option, a notification period of one year would apply.

Question 7-7: ISED is seeking comments on:

A. the options and implications for the treatment of incumbent licensees currently holding Tier 3 licences, the percentage that would apply to option 1 and supporting rationale.

B. the options and implications for the treatment of incumbent licensees currently holding FCFS licences and supporting rationale.


5 GENERAL OBSERVATIONS AND CONCLUDING REMARKS

University-business interactions are part of complex multi-layered dynamic social systems. The international body of scholarly literature identifies a wide range of (interacting) UBI determinants, among which the R&D environment, the nature of proximities between research partners, and the effectiveness of those connections. In this empirical study we applied a quantitative indicator-based mapping of UBC patterns in the United Kingdom. It taps into a rich source of comparative empirical information on the UK's research-intensive university sector, especially with regards to research co-operation patterns and cross-sectoral mobility of academic researchers. We focused our attention on a selection of 48 research-intensive universities, their joint research publications with the business sector, and the dispersion of partner firms across distance-based geographical zones in the UK and abroad.

The geographical location and spatial distribution of those firms presents a new perspective on UBC patterns, and addresses an information gap in UK government statistics or university administration data on research co-operation with the local or regional business sector. In addressing these knowledge gaps and analytical challenges, the collected data from UBRP measurement approach provides some interesting new insights into aggregate-level UBC information across the UK's largest research-intensive universities.

We focused our study on two research questions, stated in subsection 1.2: (i) is the geographical distance between the university and its industry partners a meaningful parameter of a university's UBC profile? (ii) If so, how distance-dependent are the major explanatory variables describing the way research-intensive universities are engaging with R&D-active firms? Concerning the first research question, we find that the number of UBRPs has increased across all distance zones. However, long-distance “global” UBRPs has increased at a significantly higher rate than short-distance “local” UBRPs. Several universities exhibit a “glocalizing” pattern, where UBRP growth occurs across the entire range of distances. At other “globalizing” universities the growth occurs almost entirely in the long distance zones. Focusing on the subsample of universities with significant growth rates in either glocalization or globalization, we find that the glocalization rate is higher at “catching up” universities that have low levels of local UBRPs and are located in areas with relatively low levels of business sector R&D intensity.

Regarding the second question, our macro-level findings highlight a multitude of determinants that seem to be affecting UBRP patterns, where each distance zones presents a different set of determinants. Nonetheless, four common “structural” factors emerge (see subsection 5.1), which are significant in the majority of the distance zones and are like to be major drivers of UBC activity. The business sector R&D expenditure in the region represents a very significant external factor. Not surprisingly, we find evidence of spatial concentration effects in the London metropolitan area and in other R&D-intensive areas. Two of other factors—the research volume of a university and its citation impact level—reflect research-related organizational determinants such as critical mass, economies of scale, and scientific quality. The fourth factor captures the importance of the “human factor” as a UBC and UBI determinant, with empirical evidence that local UBRPs are more likely to involve boundary-spanning academic researchers. The share of these “cross-sectoral” researchers—either “university-business mobile researchers” (UBM-Rs) and/or “university-business/multiple affiliated researchers” (UB/MA-Rs)—is consistently among the most discriminating variables to explain the propensity of universities to collaborate with firms located at close distance. Given the strong positive relationships that tend to exist between social proximity, cognitive proximity and spatial proximity (Boschma, 2005 ), this outcome suggests that these individuals are an important driving force, if not an indispensable “success factor” for create sustainable R&D-related university-business interactions within the UK. There is still insufficient understanding of how knowledge is actually shared or transferred between individuals—either within the same local geographical area or further afield.

More in general, our UBC model critically hinges on the assumption that its three key performance indicators (UBRPs, UBM-Rs, and UB/MA-Rs) are sufficiently valid proxies of general patterns and trends as regards to university-business co-operation. The model's focus on research clearly introduces an observation bias: all three key performance indicators (KPIs) are related to research publication output, more specifically successful research (otherwise the work would not be published). Moreover, publication output quantities do not reflect essential information on inputs (such as the amount of industry funding of academic research, or highly qualified graduate students moving into industry), the effectiveness of knowledge creation processes, nor how productive interactions with the business sector actually were. For example, work by Faggian and McCann ( 2009 ) shows that the quality of UK universities, via the flows of their highest quality graduates, are found to be of limited importance for regional innovation performance in the university's local region but these graduates do have significant impacts on the innovation performance in other UK regions.

Hence, these KPIs—and the UBRPs in particular—present a limited window of analysis that tends to overemphasize successful research co-operation and associated productive interactions in terms of researcher mobility, joint knowledge creation or exchange. Moreover, our UBC analysis does not include a clear-cut distinction by type of university, notably between “comprehensive” or “specialized,” in terms of their research activity profile. Although the variables “Publication output—medical fields” and “Publication output—STEM fields” partially capture this profile, a more explicit and fine-grained distinction deserves more attention in follow-up studies to ascertain possible effects of (changes in) research specialization on UBRP patterns and trends.

Given the growing importance of UBI and UBC as knowledge-intensive inputs into UK business sector R&D—witness the development of the Knowledge Exchange Framework (KEF) as a proposed new policy tool and information platform—more effort should be invested into developing new analytical methods and performance indicators for studying UBI, UBC and UBRP patterns and trends. One of the proposed activities, KEF Metrics, aims to provide “timely data that describes and compares institutional-level performance in knowledge exchange” (https://re.ukri.org/knowledge-exchange/knowledge-exchange-framework/). Between March and May 2019, 21 universities, participated in a pilot exercise to further test on how to operate KEF in England. Should KEF become operational, the three UBC performance indicators may open up new avenues for further empirical enquiry of the UK science system, especially concerning university knowledge transfer to the business section. UBI and UCB data may also be of interest in the next edition of the Research Excellence Framework (www.ref.ac.uk/about/what-is-the-ref/) either in terms of contributing to performance indicators, or as elements within impact stories that academic researchers will be required to produce. UBC-related data could also supplement university-level statistical information from the Higher Education-Business and Community Interaction survey, which may help address policy-relevant information gaps, notably on the effects and effectiveness of government policies to promote UBI within the UK.

Finally, a concluding remark regarding Brexit. Although our data only run up to 2017, the large volume of UBRPs in the most recent years provides compelling information on the size of the intersection between UK academia and their corporate partners on the continent (Tijssen & Yegros, 2017 ). According to our data, hundreds of researchers were, and probably still are, straddling and moving between UK universities and the business sector elsewhere in Europe. This connectivity space of mutual trust relationships, common understanding and shared goals spans many personal ties and associated R&D networks. It represents several decades” worth of UK investment in valuable human capital and vulnerable social capital. Leaving the EU could seriously damage the UK's UBI infrastructures if those connections are severed.


Acknowledgments

[41] The University of Melbourne thermochronology laboratory receives infrastructure support under the AuScope Program of NCRIS. S.H. received support from the National Natural Science Foundation of China (NSFC) (41072186). Y.T. received support from IPRS and MIRS scholarships at the University of Melbourne. Y.T. is grateful to Abaz Alimanovic for assistance with (U-Th)/He dating and to Zhonghua Tian and Zhaokun Yan for their assistance during fieldwork. Constructive reviews from anonymous reviewers clarified points of this work. Editorial work of James Tyburczy is gratefully appreciated.

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