The Juniper Networks Certification Program (JNCP) Junos Security affirmation track is a program that enables members to exhibit skill with Juniper Networks innovation. Effective competitors show intensive comprehension of security innovation as a rule and Junos programming for SRX Series gadgets.
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JN0-230 Exam Questions:
Questions No 1:
Which two statements are correct about functional zones? (Choose two.)
A. A functional zone uses security policies to enforce rules for transit traffic.
B. Traffic received on the management interface in the functional zone cannot transit out other interface.
C. Functional zones separate groups of users based on their function.
D. A function is used for special purpose, such as management interface
Answer: C D
Question No 2:
Which statements about NAT are correct? (Choose two.)
A. When multiple NAT rules have overlapping match conditions, the rule listed first is chosen.
B. Source NAT translates the source port and destination IP address.
C. Source NAT translates the source IP address of packet.
D. When multiple NAT rules have overlapping match conditions, the most specific rule is chosen.
Answer: A D
Question No 3:
Which security object defines a source or destination IP address that is used for an employee Workstation?
C. Address book entry
The CCDP confirmation manufactures propelled information of the system plan ideas and standards required to create multi-layer undertaking models and system segments.
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300-320 Sample Questions Answers:
Question No 1:
An engineer is designing an infrastructure to use a 40 Gigabit link as the primary uplink and a 10 Gigabit uplink as the alternate path. Which routing protocol allows for unequal cost load balancing?
Question No 2:
After an incident caused by a DDOS attack on a router, an engineer must ensure that the router is accessible and protected from future attacks without making any changes to traffic passing through the router. Which security function can be utilized to protect the router?
A. zone-based policy firewall
B. access control lists
C. class maps
D. control plane policing
Question No 3:
Recently, the WAN links between the headquarters and branch offices have been slow under peak congestion, yet multiple alternate WAN paths exist that are not always congested. What technology can allow traffic to be routed in a more informed manner to utilize transport characteristics such as delay, loss, or link load?
A. performance routing
B. static routing
C. on demand routing
D. policy based routing
Microsoft has figured out how to hurt the association between Amazon Web Services and VMware by raising costs for clients utilizing non-Microsoft mists
Microsoft has changed the way it’s charging clients who utilize its innovation on adversary mists.
This change will successfully raise costs — frequently altogether — when clients run particular kinds of Microsoft programming, for example, its database running on Windows Server, on another cloud like Amazon Web Services or Google Cloud.
“They changed the standards for everybody, even themselves,” Wes Miller, a notable expert for the similarly outstanding statistical surveying firm Directions on Microsoft, revealed to Business Insider.
While that is valid, Microsoft has another permitting program, called “Sky blue Hybrid Benefits,” that essentially balances this new change and its more expensive rates.
So the more expensive rates “will influence costs for its clients utilizing VMware in AWS and the as of late declared VMware in Google Cloud,” Miller stated, yet not for most Microsoft Azure clients.
The product permitting hare gap
To comprehend what Microsoft is doing, you initially need to comprehend a tad about the wonky, costly, and regularly draconian principles including how organizations purchase programming.
Organizations don’t really “purchase” programming.
They permit it, which means they pay to utilize the product in explicit ways: in specific areas, for example, their own server farms; for a predefined number of a specific size of servers; and for a particular time allotment, normally three years.
What Microsoft did was basically dispense with an escape clause proposed to cover programming that is utilized in an organization’s own server farm yet overseen by another person, otherwise known as old fashioned re-appropriating.
The escape clause was that the “outsourcer” assignment still connected regardless of whether the organization moved the Microsoft programming onto a cloud like Amazon’s or Google’s, the length of they utilized “devoted servers,” where the client controls the entire cloud server without offering it to other people.
Quite a bit of distributed computing doesn’t include committed servers. The first open cloud model includes sharing every one of the server farm innovation. In the language of the business, this idea is designated “multi-inhabitant.” With everybody sharing gear in a gigantic server farm, distributed computing can give clients reasonable access to practically boundless supercomputing power.
In any case, there are applications that organizations would prefer not to put onto a common framework. They might be confined by government guidelines, or the application may have persnickety execution necessities, or the organization may simply feel like the application and its information are unreasonably valuable for that.
These applications are regularly incredibly rewarding for IT sellers to supply, and there’s a land get among distributed computing merchants going on now for big business clients who still have these valuable applications in their very own server farms.
What Microsoft changed is this: Microsoft now says that with all new permit understandings marked after October 2019, explicit mists are never again secured by the re-appropriating proviso around committed servers. (Keep in mind, organizations need to ceaselessly recharge their licenses so as to legitimately continue utilizing the product that they are as of now have set up.)
Microsoft says that any client that needs to run its product on committed servers on the cloud will likewise need to purchase a unique administration called Software Assurance (SA), which incorporates “versatility rights.”
SA is similar to Microsoft’s maintenance agreement. It gives undertaking clients a bundle of additional highlights. Be that as it may, it’s expensive, more often than not adding an extra 25 to 30% to the expense of authorizing, contingent upon the items.
On the off chance that clients don’t purchase Software Assurance and “portability rights,” at that point they can’t get a boundless utilization permit for Microsoft’s product. They will return to “pay as you go” expenses, which will more likely than not cost them all the more consistently.
Microsoft names the accompanying cloud suppliers as being excluded from being marked an outsourcer: Microsoft Azure, Alibaba, Amazon (counting VMware Cloud on AWS), and Google.
In any case, once more, in spite of the fact that Microsoft has lumped itself in there, it offers another authorizing program that enables its clients to move their Microsoft applications onto Microsoft’s cloud.
“The final product is that the expenses in Azure will fundamentally remain a similar when running on committed equipment (like VMware in Azure or the new devoted hosts for Azure VMs), however will go up significantly for different mists,” Miller said.
Smackdown on the AWS-VMware advertising system
This change will especially slam a top deals technique utilized by Amazon Web Services and its nearby accomplice VMware. Those two are mutually attempting to get VMware’s clients to move to Amazon’s cloud. A significant number of VMware’s clients utilize its product to run Microsoft applications.
VMware has now enabled its product to keep running on Microsoft’s cloud, just as Google’s, Alibaba’s, and IBM’s. In any case, Microsoft and Google have gotten it going by working with a portion of VMware’s accomplices. Amazon Web Services is the main cloud where VMware is doing joint building and joint deals.
How significant was this permitting escape clause to Amazon? Enough that Amazon talked it up in its advertising materials:
Also, AWS has displayed Microsoft clients who set aside a great deal of cash on account of the escape clause when moving their Microsoft applications to AWS.
The metal at AWS is troubled about Microsoft’s choice. In spite of the fact that Amazon demands it doesn’t concentrate on what its rivals are doing, Werner Vogels, its central innovation official, sent a tweet censuring Microsoft’s authorizing change on Monday.
He considered it a sleight of hand, saying that Microsoft has now moved back two or three projects including “bring your very own permit” to the cloud.
LinkedIn said on Tuesday that it’s at long last relocating registering outstanding tasks at hand from its own gear to the Azure open cloud, entrusting its basic information with parent organization Microsoft.
The move demonstrates that LinkedIn, which Microsoft procured for $27 billion out of 2016, at last considers Azure to be dependable enough for its administrations. Microsoft has developed as the unmistakable No. 2 in cloud foundation, behind Amazon Web Services, and has been pulling a greater amount of its interior administrations over to Azure. AWS has additionally gone more to its own innovation, diminishing its utilization of database programming from Oracle.
In a blog entry on Monday, Mohak Shroff, senior VP of building at LinkedIn, said the move will occur over various years.
“The cloud holds the future for us and we are certain that Azure is the correct stage to expand on for a considerable length of time to come,” Shroff composed. A LinkedIn representative revealed to CNBC that the move will be “cost-nonpartisan” to Linkedin and will deliver efficiencies after some time.
LinkedIn has since a long time ago worked its own server farms in California, Oregon, Texas, Virginia and Singapore, among different areas. At the season of the securing, it didn’t focus on moving to Azure, however later began utilizing the Microsoft cloud benefits in a restricted limit.
Kevin Scott, Microsoft’s main innovation official and once senior VP of building and activities at LinkedIn, told CNBC in a meeting in March that LinkedIn administrators recently examined a cloud relocation and were at long last “making genuine arrangements” to break down a move.
“We would truly at LinkedIn do this yearly exercise of — we would cost out what a forklift move to a cloud would resemble each year,” said Scott, who accepted his influential position at Microsoft in 2017. “I was responsible for the designing group. The financial matters didn’t bode well yet, however it drew nearer consistently.”
Most of Microsoft’s shopper and business-concentrated cloud administrations keep running on Azure, Julia White, corporate VP at Microsoft, revealed to CNBC a year ago. That incorporates the vast majority of the Office 365 profitability applications like Microsoft Teams.
LinkedIn now has in excess of 645 million individuals, up practically half from when Microsoft reported the buy. The business informal organization speaks to about 5% of Microsoft’s income.
While AWS drives the cloud framework advertise by a wide edge, Microsoft isn’t doing too seriously, hid solidly in runner up, the main other organization with twofold digit share. Today, it reported a major ordeal with AT&T that envelops both Azure cloud foundation administrations and Office 365.
An individual with learning of the agreement pegged the consolidated arrangement at a clean $2 billion, a pleasant plume in Microsoft’s cloud top. As indicated by a Microsoft blog entry reporting the arrangement, AT&T has an objective to move the vast majority of its non-organizing remaining tasks at hand to the open cloud by 2024, and Microsoft just got itself a major cut of that pie, doubtlessly one that adversaries AWS, Google and IBM (which shut the $34 billion Red Hat arrangement a week ago) would profoundly have wanted to get.
As you would expect, Microsoft CEO Satya Nadella talked about the arrangement in grandiose terms around change and development. “Together, we will apply the intensity of Azure and Microsoft 365 to change the manner in which AT&T’s workforce teams up and to shape the fate of media and interchanges for individuals all over,” he said in an announcement in the blog entry declaration.
Keeping that in mind, they are hoping to team up on developing advancements like 5G and accept that by consolidating Azure with AT&T’s 5G organize, the two organizations can enable clients to make new sorts of utilizations and arrangements. For instance refered to in the blog entry, they could see utilizing the speed of the 5G system joined with Azure AI-controlled live voice interpretation to enable people on call for discuss momentarily with somebody who talks an alternate language.
It’s important that while this arrangement to bring Office 365 to AT&T’s 250,000 representatives is a decent success, that piece of the arrangement falls under the SaaS umbrella, so it won’t help with Microsoft’s cloud framework piece of the overall industry. All things considered, no matter how you might look at it, this is a major ordeal.
Earlier today I posted an image of EEBO-TCP as a Giant Hairball, and I’ve had some questions about how the data was put together and a few requests to see it, so here’s a brief narrative with some download links at the bottom.
Inspired by the incredible work over at the Early Modern OCR Project (eMOP) led by Laura Mandell, I thought I should share some of the inital work I’ve done parsing early modern imprints. eMOP recently released data from their project in XML form, linking parsed imprints to EEBO-TCP and ESTC data. Their files can be found here: https://github.com/Early-Modern-OCR/ImprintDB
Identifying and differentiating the printers and booksellers who produced old books is rarely a straightforward process. Publication data from title pages are notoriously irregular. Spelling variation in names, and incomplete or inaccurate attribution, is common. Names are often given in Latin and often listed only as initials. As a result, title page imprints appear in forms like this, “London: printed by T.N. for H. Heringman.” For this reason, library catalogs, which have been inherited by digital projects like Early English Books Online, typically offer only the character string of each imprint, leaving it to human readers to figure out who these people are.
Cleaning up publication metadata and making it available for search and analysis would have many important research applications for scholars working on the history of publishing, authorship, and other areas of print history. My own interests are in network analysis. Who published with whom? How did different political, religious, and literary ideas circulate in the print marketplace? Especially now that so much of the early record is available in full-text form, improving the metadata is a major task facing scholars right now.
Matthew Christy, eMOP’s co-project manager and lead developer, worked with their team to break the imprints up into attribution statements, marking out “Printed for” and “Printed by” relationships. Their work is hugely valuable. Working with Travis Mullen here at the University of South Carolina, we tackled the problem from a different angle. Our goal was to pull out the names to see if we can reconcile common entities across the catalog. If one book was attributed to “T.N.”, another to “Thomas Newcombe”, and a third to “T. Newcomb”, we wanted each to be attributed to the same person. Using a combination of algorithmic and hand-corrected methods, we figured this should be doable. The results are here: http://github.com/michaelgavin/htn
Before delving into our process, a few caveats should be kept in mind. First of all, imprints, as I mentioned above, are less than perfectly reliable. Names were often left off completely; sometimes false names were added in their place. Like eMOP’s, our technique does nothing to solve this problem. We can only parse the information available. Even in the case of false imprints, though, it makes sense to us to capture what the books actually say.
Second, we haven’t yet reconciled the names to existing name authority files, like those published by the Library of Congress or VIAF. Many of our printers and booksellers are included in linked data resources, but many aren’t. In the long term, we’d like to get them all into shape to be linked up to other resources, but we have set that ambition aside for now.
Third, because of ideological and practical motives, we looked only at books freely available from the EEBO Text Creation Partnership. On principle, I don’t really like working with proprietary data. Even among the freely available stuff through, there were practical problems. American imprints from Evans and eighteenth-century books from ECCO were far more difficult to process (for reasons that will be clear).
Lastly, as with any computer-aided process, some errors slipped through, so our data’s still far from perfect. The intial pass returned a little over 30,000 attributions, and of those about 5% were easy-to-spot errors. We tried to clean out by hand, but errors and omissions certainly remain. I am putting the initial data out now, in part, to invite collaboration from anyone who might be interested in building up or further correcting the metadata.
What did we do?
Basically, we designed a little decision-tree algorithm to read each imprint, pull out name words, and then find likely matches in the British Book Trade Index.
What makes the BBTI a great resource is that they include almost everything. If a name is on an imprint, there’s a very good chance that it’s somewhere in the BBTI. The other great thing about BBTI is that, although they don’t standarize their names, they do provide one crucial piece of data: trade dates. Unlike birth or death years, trade dates refer to a person’s professional life. The inital trade date is usually the year of the first imprint they appear on or the year they were taken on as an apprentice. This means we didn’t have to search the entire BBTI for every book, we just had to look for names in the small subset of stationers active around the time of each book.
We designed a custom set of processing rules for the imprints. Names of streets and neighborhoods were taken out, as were names of bookshops. So
“Oxon : Printed by L. Lichfield and are to be sold by A. Stephens, 1683.”
gets reduced to a vector of five words:
 “Oxon” “L” “Lichfield” “A” “Stephens”
The core process then had three steps:
Subset the BBTI to look only at entries where the initial trade date was within range of the imprint date. For each word in the imprint, search by last name, looking for matches or near matches. Then, look at the word to the left of the target word in the imprint. Select only those with the same first letter, then choose the closest match. If there are multiple matches or no matches, just skip to the next word in the imprint.
Using the example above, the algorthim searched through several possibilities.
“Oxon L” “L Lichfield” “Lichfield A” and “A Stephens”
The first and the third didn’t hit any matches. The second and the fourth returned these two:
bbtiID name TCP Role
483541 Lichfield, Leonard II 1657 A36460 Printer, Bookseller (antiquarian)
483551 Stephens, Anthony 1657 A36460 Bookseller
The result was almost always the exact name I would have chosen, if I’d looked it up by hand. The system differentiates Lichfield Jr. from Leonard Lichfield Sr. by the publication date, and the roles are just the occupation titles given by BBTI. Unlike eMOP’s, these don’t differentiate “Printed for” from “Printed by” statements, but the roles seemed generally very consistent. (It’ll be interesting now to cross reference our results with theirs.) Overall the algorithm did a good job catching spelling variation (even, often, the Latin) while also distinguishing the Jacobs and Johns from the Josephs.
There were lots of special cases that had to be handled separately. Because of “Saint Paul’s Churchyard” in all its variation, the name “Paul” was particularly difficult and had to have its own set of pre-processing rules. First-name last names like “Johnson,”” “Thomson,” or “Williams” caused lots of little problems, but they were easy to clean out in post-processing. Names like “Iohn” and “VViliam” were changed in pre-processing to “John” and “William.” There were quite a few cases like these, but not too many for the relatively small EEBO dataset. Our technique might not scale up to the entire ESTC, though. As I mentioned above, about 5% of the results were obviously false matches, and I have no doubt that a small number slipped through my attempts to catch them by hand. No effort has yet been made to measure the accuracy of the dataset as it exists,The ESTC is an order of magnitude larger, which means the initial results would need to be better. Also, because our algorithim looks for first name or first initial matches, it doesn’t work nearly as well on eighteenth-century imprints, when many printers and sellers referred to themselves as “Mr. So-and-so.” Some adjustments would need to be made.
Overall, after hand correction, the process resulted in about 29,000 stationer attributions over 22,000 EEBO-TCP entries. The total dataset, including authors and others, includes 64,887 attributions over the EEBO, Evans, and ECCO TCP documents.
For the past year or so I’ve been using text mining to study the historical meanings of words. (An early version of this work can be found here.) Lately, working with Eric Gidal, I’ve been experimenting with a geospatial approach to text analysis, looking at placenames in particular. What kind of word is “Edinburgh”? What are some of its various connotations?
Our data is drawn primarily from a collection of nineteenth-century British geography: gazetteers, statistical accounts, and topographical dictionaries. (Downloaded from the Internet Archive.) A topographical dictionary is just like it sounds: a big book with an alphabetical list of placenames and a description for each:
As you can see, these are highly structured documents, and the OCR was quite good, so it was simply a matter of parsing the plain text, capturing the name and description of each place, then matching those names to a list of places published by the U.K. government.
Here’s a map that performs a hotspot analysis on the place descriptions. It highlights the clusters of points that are most likely to have the word “Argyll” in their descriptions. Towns or cities in Argyllshire, it turns out, were often described as being “in Argyll,” so a hotspot analysis of the word returns points in a region that overlaps almost exactly with the historical boundaries of Argyllshire county. The cool thing about this map is that it discovers administrative boundaries in a bottom-up way, extrapolating those boundaries from text-based data points.
But descriptions aren’t limited to official geographies. Nineteenth-century gazetteers are rich testimonies of environmental and cultural change. So here’s a similar map, only instead of showing points with a high rate of “Argyll,” it shows places with descriptions that use the word “Caledonia.”
“Caledonia” was the ancient Latin name of north Britain. It was often invoked by Scottish nationalists who felt uneasy under English hegemony. Caledonia itself was never really a nation — more a hodgepodge of principalities and clans on the furthest outskirts of the Roman Empire. But it loomed large in the historical imagination of Scottish writers.
This map, then, actually is a map of Caledonia (sort of, at least insofar as “Caledonia” itself was a back-projection of the Scottish Enlightenment). It’s a map of places that nineteenth-century writers associated with the ancient land.
I begin with these examples just to highlight a kind of metaphysical disconnect at the heart of geospatial text analysis. On the one hand it depends on grounding analysis in “real life” data drawn from official sources. On the other hand, by exposing the many definitions and narratives that attach to official placename concepts, it draws out their multiple, often conflicting conceptualizations.
We’ve started calling this line of inquiry historical geospatial semantics.
The Mapping Scotland project
This work stems out of Eric’s book on the reception history of Ossian, a mythical Gaelic poet whose epics were “discovered” in the 1760s and 1770s by the poet and translator, James Macpherson. Although the epics themselves were largely fabricated, Macpherson borrowed from a real tradition of Ossianic Gaelic verse that he found in old manuscripts and heard recited in northern villages. Controversy over Macpherson’s work simmered for decades — Was it a mere hoax? — but Scottish writers were long among his strongest advocates. In the nineteenth century, at the same time that Scotland’s countrysides were transformed by industrialization and surveyed by government agencies with unprecedented levels of detail, Scottish historians and geographers scoured the landscapes of the Highlands and the Hebrides for evidence of their nation’s heroic past.
The study of Ossianic geography is the study of how meanings attach to places. At the most abstract, it’s about reconciling two representative systems: natural language and geographic models. In the eighteenth and nineteenth centuries, geographic models took the form of place descriptions, maps, and statistics. The work of these geographers evolved into the British Ordnance Survey, which is still the official geographic agency of the U.K. and now maintains detailed files of the British landscape, available for download and easy to use in GIS software. Perhaps strangely, because of the institutional continuity of the Ordnance Survey, the geographic imagination of the nineteenth century in fact survives in modern computerized systems, in the forms of towns, rivers, and lakes named after their mythical predecessors. Ossianic geographies permeate modern GIS systems just as they infiltrated and sparked the imaginations of Macpherson and his later defenders.
But how does language touch its physical environments? How should we characterize the relationships among meanings and the spaces of their circulation?
Our idea was to dig through some representative texts and mark up all the places mentioned, referring them back to places named in the Ordnance Survey, while also capturing their descriptions. Then, we automatically georeferenced 47 volumes of nineteenth-century gazetteers and topographical dictionaries. Across this corpus, which totals more than 7 million words, we captured 70,000 descriptions of almost 20,000 unique places. We ask: How were Scottish places described? How did Ossianic and official geographies differ, and what, if any, literary, economic, and environmental concepts informed them?
Luckily, although our questions and primary materials are somewhat novel, we’re not the first scholars to try something like this. Geospatial text analysis has a small following within both digital humanities and geography (although the two groups don’t seem to talk to one another, as far as I can tell). Matthew Wilkens has been a leading proponent of quantitative approaches to literary geography. So, too, have Ian Gregory and Andrew Hardie, whose essays on this topic cover very important technical ground. I recommend in particular their chapter from the recent collection, Deep Maps and Spatial Narratives (IUP, 2015).1
Among geographers, the subfield of “geospatial semantics” has a lot in common with work like Gregory’s and Hardie’s. It studies geographic concepts and designs formal ontologies for representing those concepts in software systems. Like other aspects of natural language, human words for places and spaces lack crisp definition. As Werner Kuhn has emphasized, “geospatial information is often based on human perception and social agreements” and for this reason will be marked by “vagueness, uncertainty, and [differing] levels of granularity.” Geospatial software systems must be sensitive to the various and conflicting “naive” geographies of human discourse. As Andrea Ballatore (et al.) have remarked, “To share geographic information across a community, it is necessary to extract concepts from the chaotic repository of implicit knowledge that lies in human minds.”2
The point here is not that human cognition is flawed, but that place is linguistically constructed. From a GIS perspective, this has practical consequences. Environmental, commercial, and government data needs to operate across very different linguistic models and so must be sensitive to the nuances of language. For the spatial humanities, this sensitivity has a very different connotation: it suggests that GIS, with its emphasis on modeling multiplicity, shares a real commensurability with postmodern geography.
Humanists looking to do this kind of work are best off starting with Gregory’s and Hardie’s work, I think. Adam Crymble has a nice post in Programming Historian if you like working in Python. However, Angela Schwering has written some excellent surveys of the field, and the work of Krzysztof Janowicz and Andrea Ballatore, to name just a few others, is pretty exciting.
Geospatial semantics: profiles & footprints
When combined with corpus linguistics, geospatial analysis sits at the nexus of two theoretical traditions. The first comes from Zellig Harris and J. R. Firth from the 1950s. The distributional hypothesis of computational semantics says that similar words will tend to appear in similar contexts. From geography, it borrows the notion of spatial autocorrelation, which proposes that places that are physically close to each other will tend to share similar properties. These ideas can be combined into one proposition (with a dash of temporality thrown in for taste):
Similar places at similar times will tend to be described using similar words.
This hypothesis proposes that geospatial text analysis should work for answering these kinds of questions:
How were the Highlands described? Which regions were most distinctly imagined as sites of Ossianic epic? Which towns were most affected by industrialization? Which regions were affiliated with ancient Scottish nations, peoples, or heroes? Which places bear the mark of myth?
When analyzing georeferenced texts, there are two basic ways to go about it. You can either choose one place and look at all the ways it has been described, or you can choose one word or topic, then map all the places where it was invoked.
We find these definitions helpful:
- The semantic profile of a place refers to the linguistic distribution of the vocabulary of the collocates of its name(s).
- The semantic footprint of a concept refers to the geographic distribution of its placename collocates.
These two modes of analysis suggest two corresponding ways to think about similarity:
- Places are semantically similar if they share similar profiles.
- Concepts are geographically similar if they share similar footprints.
This suggests a path forward for how “distant reading” might contribute to our knowledge of historical geography. If places change in culturally significant ways, those changes should be reflected in the discourse. It also suggests how GIS can contribute to intellectual history, but that will be a bit trickier. From the perspective of intellectual history, geosemantics opens a set of questions we only now can formulate. How were ideas distributed geographically? What kinds of ideas were distributed in what kinds of ways?
Industrialization & epic
Much more could be said. Right now, though, our research is still at the “Let’s see if we can get the numbers to show us what we already expect to find” phase. We want to be able to track cultural geographies of various kinds, to identify competing cultural landscapes, and to account for change over time. We start with Ossian because we know it well.
Here are a couple of maps showing the results of a topic model we ran over the topographical dictionaries. We performed a hotspot analysis, just as above, to identify spatially distributed clusters of the texts. The first one shows descriptions of fishing (“herring”, “cod”) and waterfront development (“docks”, “boats”).
This one shows results from the same model, but displaying a topic that has to do with the mining industry, with a special emphasis on coal:
The other maps show formally and culturally defined regions, like “Argyll” and “Caledonia.” By contrast, maps of fishing and mining expose functionally defined regions.
Lastly, here’s the “Caledonia” map again, but with an extra layer showing a hotspot analysis of the word “Fingal.” Fingal was the name of the warrior-king in Ossian’s epics. The “Fingal” points are blue, and the purple region shows where that word overlaps with “Caledonia.”
This purple region overlaps perfectly with the Scottish Highlands, as well as with the points most closely associated with Ossian by scholars, whose work we track separately. (But I’ll spare you yet another map.)
Notice that the area associated with “Fingal” is very distinct from the region associated with the Scottish mining industry in the map above, but “Caledonia” crosses both regions. Remember that Eric’s book was about how the Ossian controversy erupted during a moment of important cultural shift in Scotland: industrialization combined with nationalism to drive geographers and literary historians to seek out a mythic past and to locate that past quite concretely in the landscape of northern Scotland.
These maps show that dynamic quite distinctly, and it’s now actually possible to perform statistical analyses that show, for example, that Ossianic and industrializing discourses were negatively correlated geographically (that is, they tended to separate from each other) while both being positively correlated to Scottish nationalism more generally.
In its broad outlines, these results fit well with a lot of what we know about nineteenth-century Scottish literature and history. As soon as you dig down a bit, though, they raise innumerable questions.
Thanks go out to Eric for sharing his project with me, to the University of South Carolina for our initial seed grant, to Sarah Thompson, a PhD student who worked with me last summer to work through the initial designs, and to Jeannie Britton and Tony Jarrells, who have consulted along the way.
- See Gregory et al, “Spatializing and Analyzing Digital Texts: Corpora, GIS, and Places,” in Deep Maps and Spatial Narratives (IUP, 2015). This essay builds from Gregory’s and Hardie’s earlier article, “Visual GISting: Bringing Together Corpus Linguistics and Geographical Information Systems,” Literary & Linguistic Computing 26, 3 (2011): 297-314. The newer essay is more detailed and spares readers the unfortunate neologism, “visual gisting.”
- See Werner Kuhn, “Geospatial Semantics: Why, of What, and How?” Journal on Data Semantics III (2005): 1-24; and Andrea Ballatore, David C. Wilson, and Michela Bertolotto, “Computing the Semantic Similarity of Geographic Terms Using Volunteered Lexical Definitions,”” International Journal of Geographical Information Science 27, 10 (2013): 2099-2118.