Computers, Society, and Nature

It is often said that space, place, and time are three fundamental foci of geographical inquiry. GIScience shares the spatial outlook, significance of place, and temporal way of thinking, however, the latter is relegated more often than not. Yuan (****) notes that many geographic information systems lack the tools needed to conduct spatio-temporal analyses. This presents a significant research gap in the realm of GIScience.

Yuan highlights that temporal datasets are subject to a unique problems intrinsic to the factor of time. Modeling and visualization are particularly challenging. Yuan points out that time is not a singular structure – there is linear, cyclic, parallel, and branching structures of time. Additionally, time occupies multiple dimensions, corresponding to validity, transaction, user-definition, and institutions. The latter set of examples was an entirely new set of concepts for me and I would have appreciated an unpacking of these concepts. Yuan’s acknowledgement of temporal complexity was certainly required, however, the diverse features of time come across as an afterthought in this paper.

Temporal GIS is certainly an intriguing subfield of GIScience, however, there seems to be a steep theoretical learning curve. Unfortunately, this article did not make my introduction to temporal GIS any easier.

-BCBD

In “Temporal GIS and Spatio-Temporal Modeling”, Yuan describes the different issues of temporal GIS.  The main problem is that geographic information systems were built off of the tenets of cartography, and still follow the logic of static maps. Many different data models are presented in this paper, and whilst all of them have benefits, none of them can be applied universally. Researchers have been building models for their specific needs, and therefore they don’t fit most datasets. The article, as well as the field of TGIS, is convoluted and there is no clear answer. Yuan shows that none of the models answer the needs of spatiotemporal analysis completely.

Time seems like such an obvious factor to consider in any kind of analysis, yet it is still hard to incorporate into classic data models for GIS and computer science. Creating a working temporal model for geographic information systems should be a top priority for everyone involved in the field.

It seems unlikely that the best method for spatiotemporal analysis is comparing different layers manually? Temporal GIS will revolutionize the study of processes, changes, trends, flows, etc.

-IMC

The article, Temporal G.I.S. and Spatio-Temporal Modeling by May Yuan, was a challenging introduction to the field of temporal G.I.S. It was one of the readings which I enjoyed the least so far, since I felt that the author didn’t really do a great job of clearly explaining the concepts or the diagrams provided very well. Perhaps it will become clearer after tomorrows presentation. Through reading the article, it became clear that it is a major challenge to successfully implement temporal aspects into a G.I.S. This was interesting because it reminded me of the article on geovisualization which discussed Minard’s map of Napoleon’s campaign into Russia. There was a strong temporal characteristic to that map, as well as in subsequent versions. Clearly, geographers have been occupied with combining temporal and spatial data for a long time. It is also clear that the problems and issues associated with doing that have not been solved, which is fascinating (although the technical challenges associated with G.I.S.’s are presumably more complex). Time is a very important variable to take into account for any geographic process and to be able to do it successfully incorporate it into a G.I.S would provide a boost to all fields of G.I.Science which rely on temporal analyses (which is a lot).

-Benny

Crampton et al. (2013) Beyond the geotag: situating ‘big data’ and leveraging the potential of the geoweb.

Crampton et al. (2013) presents an analytical study of the intersection of big data and GIScience via the medium of geolocated tweets. The authors recognize that atheoretical analyses of the geoweb is limiting the expansion of GIScience research and identify five ways through which to remedy this shortcoming.

Using the tweets accumulated through the #lexingtonpolicescanner (also #LPS), Crampton et al. assessed the spatio-temporal phenomenon of data production. They observed spatial and temporal spikes corresponding to how the event evolved over time and through the channels of social media.

Although the bridge to geography and GIScience is established, the connection to big data is much more tenuous. Crampton et al. remark that their analysis “emphasize[d] that the informational richness of these data is often lacking” in typical big data analyses –those which focus on issues of volume, velocity, and statistical complexity (2013: 137). Perhaps this is my own naïveté when it comes to the subject of big data, however, from my understanding the lack of informational richness (due to noise), unfathomable data quantities, and overwhelming speed of dataset creation are the definitive features of big data. Big data is supposed to challenge the conventional methods of statistical analysis due to these unprecedented conditions. I doubt 12 590 tweets would constitute a ‘big’ dataset. Indeed, Crampton et al. repetitively state that their analysis may not qualify as ‘big data’. I suspect the authors were caught up in the hype of big data. If true, this very article would exemplify the phenomenon of ‘trend spam’ through which unrelated—or loosely related—subjects ride along in the wake of a popular label.

One of the more intriguing findings of this article is that certain agents, particularly popular websites, may act as catalysts in the (re)production of data. Crampton et al. note that publicity on Mashable.com sparked created a noticeable spike in the use of the #LPS hashtag. It would seem that Tobler’s first law of geography is applicable to both physical and virtual space.

-BCBD

“Beyond the geotag: situating ‘big data’ and leveraging the potential of the geoweb” describes the possibilities of advancement in the geoweb with big data analysis, by using the example of the #LexingtonPoliceScanner trending topic on Twitter.

One of the most important things I found in this article is how inaccurate the spatial data can be. As the author points out, the location of the user is usually self-reported and can be a fictional place. Moreover, even if they correctly identify their home location, it does not necessarily reflect the location of the user at the time the information was sent. However, we know that most apps record the user’s location, whether they choose to post it or not; therefore it should be possible to have more precise information.

This paper reflects the need for GIScience to move away from static points on maps. The three maps representing tweets at different times in the night could be helped by a functioning temporal GIS and geovisualization.

It is very relevant that this is one of the last papers of the semester, as it ties together many of the fields studied in GEOG 506.

-IMC

I found this article, ‘Beyond the geotag: situating ‘big data’ and leveraging the potential of the geoweb’ by Crampton et al., to be an interesting introduction to the topic of Big Data. I thought it was fascinating how they attempted to progress past the simpler research methods previously used to analyse big data and build more robust and detailed frameworks. They made it clear that, while studying big data was useful, there is a need for more nuanced avenues of research which could help advance the field of big data and the geoweb. One such idea that I found interesting was that, as the authors claimed, there is often a paradoxical lack of data, or at least value in the data, that is being analyzed. For example, only 34% of the tweets studied were geocodable by user-defined location information (Pg. 134). They also note that people can use different location data or tweet about events that are not happening in their stated location. Examining how much of an impact this has on the accuracy of studying big data would be interesting to look at.

On the topic of locational relationships, the maps provided on page 135 got me thinking about the potential for people to tweet/create content on the Internet about issues that happen a large physical distance away from them. Does this data still follow Tobler’s First Law of Geography? If a larger number of people in New York tweet about events in Ferguson than people in Ferguson, what sorts of implications does this have in terms of analyzing big data’s spatial patterns? With the power to consume information about distant events, does proximity even matter anymore? When studying big data, I would imagine that this would be an important question to answer.

-Benny

I really enjoyed “Beyond the geotag: situating ‘big data’ and leveraging the potential of the geoweb” by Crampton et al (2013). It explores the possibilities and limitations associated with the analysis of Geo-web generated Big Data. They advocate for a more comprehensive ontology of space, one that goes beyond xy coordinates to include a temporal and relational dimension, as well as a more complete understanding of geo-web generated data to include ancillary non-human produced data, as well as non user-generated data.
This article debunks the promise of the explanatory potential of Bid Data analytics and stresses the importance of choice of data and analytical techniques to provide meaningful findings. Moreover, they emphasized that Big Data created from the Geoweb represents information from a small subset of the population, undermining the external validity of findings, and potentially marginalizing ‘unplugged’ individuals.
The implications for GIScience are significant: How do we realize the potential of geospatial Big Data? What are the societal implications of Big Data analytics? How do create metadata for Big Data efficiently and comprehensively? How do you effectively geovisualize Big Data to both explore and represent data?
Geospatial Big Data gives rise to questions that build upon those generated by ‘small data’ analytics. Issues of quality are still relevant and are not supplanted by large volumes of data. GIScience will evolve rapidly and will likely intersect with other disciplines to fully realize the potential of Big Data.

Fan_G

“Temporal GIS and Spatio-Temporal Modeling” by Yuan explores the development of temporal GIS and its applicability to support spatio-temporal modeling, highlighting the challenges associated with temporal modeling of spatial information and pointing to avenues for future research.
Before reading this article, I had not fully appreciated the complexity and difficulty of creating a temporal GIS. The importance of creating a database structure that effectively anticipates the types of spatio-temporal questions to be investigated was well emphasized. While this paper focused on exploring spatio-temporal physical phenomena, similar issues are relevant in the investigation of human or behavioral phenomena, such as spatial social network analyses, migration patterns of time, changes in urbanscapes etc.
The relationships of temporal GIS with other topics in GIScience are numerous. For example, ontologies of time and space need to be properly established to construct a good temporal GIS that facilitates data analysis, and enables interoperability between datasets. Moreover, concerns over geo-spatial metadata are compounded when you consider geo-spatial temporal data. Is temporal information captured in the metadata or is incorporated in the data structure (and is such a distinction (between data and metadata) even meaningful in this context). Issues of temporal scale become important: To what level of precision is temporal data recorded, and what are the implications of temporal aggregation in accurately conveying temporal dimension of a phenomenon?
The questions that arise when exploring the temporal component of spatial phenomenon emphasize the scientific dimension of GIS and highlight the importance of studying GIS as science as instead of as a tool.

Fan_G

When institutions adopt a new technology there are bound to be issues associate with that technology’s implementation.  This paper described two instances where different institutions began using GIS as a decision-making aid.  Despite using an identical technology, the two government organizations had very different approaches to how to integrate their new tool.

The North County’s organization-wide adoption and promotion of the new tool sought to ensure all employees were fluent in the use of ArcInfo which attributed to the software’s success in this county.  Furthermore, the North County’s employees had a history of working with geospatial data making the new software an easy transition.  In the South County however, the software was only used by a very small group of computer scientists / analysts who were unfamiliar with geospatial data.

I think when adopting any new process or technology there has to be significant attention paid to how well an organization is structured to handle change.  In the North, analysts were use to changing technology regarding how geospatial data would be handled.  In the South, a rigid system overly reliant on one group of computer specialists resulted in a missed opportunity to have a staff team better trained in the handling of geographic data.

As this paper was written almost twenty years ago I would be interested in knowing how other organizations have implemented the use of GIS in more recent years.  I assume, though I could be incorrect, that as younger, more computer savvy people enter the workforce that barriers to the implementation of certain technologies will decrease.

GoOutside

In “Organizational Context, Social Interpretation, and the Implementation and Consequences of Geographic Information Systems”, Sahay & Robey (1996) analyse the implementation of Geographic Information Systems (GIS) technologies in two unidentified North American counties. The result of this implementation was closely to each county’s context, specifically the “structure” and the “capability”.

In the North County, the “unified structure facilitated the implementation of GIS as a distributed system because the departments shared a common mission” (266). In the South County, because of the decentralized agencies structure, the Office of Computer Services controlled the GIS and worked on a contractual basis for the users in other departments. Moreover, the users in the North County included many geographers who were used to working with spatial data. This was not the case for the Computer Services personnel in the South County. The structure and capability of the North County allowed its GIS implementation to flourish, and caused South County to falter.

Because people’s backgrounds affect how they use GIS — “People with data processing backgrounds emphasized technical standards, controls, formats, and product appearance in terms of menus and screen layouts. […] Users were more interested in the capabilities of the system to support their needs” (271) — and GIS implementations have different results depending on the context, it seems that standards are especially important. Is it possible that the different counties could yield such different data that they would be incompatible?

Given that this article was written 18 years ago, I wonder how GIS implementation has changed. With the average person becoming more tech-savvy and commercial GIS package being more use-friendly, are GIS implementations easier to carry out? Do the same factors still affect the implementation?

-IMC