Computers, Society, and Nature

I bring this up in many of the blog posts that I write, but it truly amazes me how much I learn about the inner workings of GIScience every week in this class. During his talk on critical GIS for GIS day, Dr. Wilson mentioned how too often critical GIS is a lecture tacked on to the end of a GIS course, which truly was my experience taking GIS courses at another university, where limited background was provided and methods and applications were favoured. This phenomenon is reflected in the content analysis of GIScience journals provided in Schuurman (2006), which explains that only 49 of 762 published between 1995 and 2004 fell under the category ‘GIS and society’. Firstly, I find the shift/difference in nomenclature from GIS and society to critical GIS interesting, because critical GIS has negative connotations to me, implying a necessarily flawed use or understanding of GIS which needs to be critiqued; whereas ‘GIS and society’ is a neutral description of the scope and intention of the study. Secondly, I don’t find this discrepancy in number surprising, as ‘GIS and society’ isn’t the main focus of GIS research by any means, but I wonder, how can we, as GIS users/researchers make the distinction between ‘GIS and society’, when GIS studies necessarily implicate society, either because of the subject matter or by means of the study implications. To me, GIS is most often just as social as it is spatial (my own project in the high Arctic being as close to an example to the contrary that I can think of-but only because it takes place in one of the most remote places on this planet)- and I think it is highly problematic to ignore these important discussions and focus on the things that get the big funding and flashy publications.

This is an old paper presenting two algorithms (i.e., Dijkstra’s algorithm and out-of-kilter algorithm) for shortest path calculation. The different is that out-of-kilter algorithm can tackle the problems with flow control. This is useful in many situations such as transportation. And as the authors note, it is still far from the reality if we apply it to support real-world decisions. However, this paper seems to be limited in algorithm and its applications even it reveals the necessity to engage researchers from other disciplines (e.g. operational science). With recent development, we may need to realize that the limitation for developing better algorithms for shortest path or other optimal solutions is not about algorithms itself. It is possibly about how we construct the network, in other words, how we create an appropriate representation of the real world using arcs and nodes. It is a more fundamental level to discuss the limits of applying network analysis techniques.

Tackling questions in geography usually needs multidisciplinary knowledges, especially in the current age where the massive high-dimensional geo-tagged data (i.e., big data with both spatial and non-spatial attributes) are generated through information and communication technologies. The complexity of real-world problems dramatically increase when such massive data involve. Therefore, the challenge is we need reduce the complexity and transform these data to networks.  The design of networks should balance between efficiency (i.e., reducing the complexity) and accuracy (i.e., not losing information). Another challenge is conducting analysis on large networks that cannot be handled by old algorithms, such as traditional Dijkstra’s algorithm. The guidance for addressing these two challenges is not just from geography or some particular disciplines. I think it can be from any disciplines according to the context of actual problems. I believe that, in near future, there is no method can identify a “real” optimal solution.

This paper mainly reviews the applications of four online mapping platforms in Haiti earthquake in 2010. It cannot be denied that the four platforms (i.e., CrisisCamp Haiti, OpenStreetMap, Ushahidi, GeoCommons) contribute to disaster relief in the earthquake. However, these technologies also bring problems remain to be discussed and further solved.

Primarily, in the beginning parts of this paper, the authors emphasize the importance of information technologies (ITs) in disaster response and then note how volunteered mapping helps. However, they focus on Haiti where the IT infrastructures are quite limited and geo-referenced data are lacked. I agree that volunteered mapping can provide efficiently and effectively provides these data for disaster rescue and tremendously facilitate the rescue. However, this may not happen in countries with good infrastructures and well-mapped. In that case, I will wonder what is the strength of volunteered mapping comparing with the traditional mapping databases and whether we need it.

Besides, since the platforms use volunteered geographic information (VGI), the fundamental problem is how to ensure the quality of these data. In term of disaster response, I think we should consider two general types of errors proposed by Goodchild (2007): a false positive (i.e., a false rumor of an incidence), or a false negative (i.e., absence of information about the existence of the incidence). The former will lead to the inefficiency in disaster rescue, and the latter can result in low effectiveness. Both could affect the human life even just one individual. Moreover, I will doubt that a place with more dense information is necessarily a place more in need. Information density can result from different reasons, but human lives are not different across areas. According to the authors, there are only 11% people can access to the Internet and one third have mobile phones. It means at two thirds people cannot send out distress calls in Ushahidi. Resources are firstly taken by people who have the access. The authors argue that we should blame the originally insufficient infrastructures. In other words, they think even without VGI the discrimination happens. This is a trick argument and defenses nothing. Of course, I agree that social inequality always exists. However, VGI is not value-neutral and it may worse the existing inequality. Criticists does not blame VGI bring the inequality but worsen the inequality, and currently there is no efficient way to solve the issues.

In conclusion, this paper provides comprehensive review of the benefits brought by volunteered mapping in disaster response in Haiti. But it is not critical enough when discussing the defects of using volunteered mapping. Through reading this paper, we can identify many questions remaining to be answered including the inherent characteristics of VGI and its applications.

Volunteered Geographic information is tool used to consolidate knowledge where it is needed by those willing to offer their data and expertise. I feel comfortable arguing that it is strictly a tool; it create no new process or analysis of information but simply refers to consolidation of knowledge to complete various projects. The project in which the information is being used could potentially be considered science depending on its nature, but VGI is a tool. In relation to the topic of privacy, VGI can either be enhanced or impeded depending on levels of privacy. in other words, if personal data is openly available for collection and use, certain tasks may be easier to complete as a result of readily available pools of knowledge. In contrast, if information is kept private, then certain tasks may lack some critical knowledge and result in inaccuracy or bias in final products.

I think the article does good job at framing VGI as a tool that facilitates transactions of knowledge and data to complete projects more efficiently than by individuals. I was skeptical of the utility/quality of the work completed but the article makes a good point that more users means more people to catch errors and mistakes throughout the process.

One particular concern I have is regarding potential failures to provide a comprehensive amount of information comparable to what could e collected on by local knowledge and expertise: Is everything doable through VGI or are there certain limitations to projects that need to be completed outside of VGI?

This paper discusses two prominent forms of network analysis: Dijkstra’s shortest path analysis, and off-kilter network analysis.
Dijkstra’s algorithm presents a very simple form of network analysis by regarding the path from point A to point B through a series of nodes and arcs, which are accounted simply by length. Indeed, the authors make a point that original network analyses were affected by computer scientists, and did not account for the inherently geographical nature of transportation and movement. Namely, it does not account for directionality or geographical coordinates. Off-kilter analysis recognizes these issues by accounting for external factors, and partitioning flows of movement depending on the maximum allocated capacity of these certain roads. This needed situation for speed and efficiency is illustrated in disaster scenarios by the authors, where precarious roads and a mass of traffic need to be accounted for quickly and dealt with efficiently.
This was written in 1992, at the cusp of a widespread informatics revolution in the home market. As it stands right now, Dijkstra’s analysis is still highly relevant, I believe that it is used in Google Maps for directions, though I have the sense that off-kilter analysis has become a viable option for many people. With traffic collection data such as Waze collecting cell-phone information, paired with basic information about current infrastructure, it has become possible for GPS services to account for the dynamic changes in traffic data for users of infrastructure.
I can’t help but feel that this is nonetheless a still rudimentary network analysis, there are still many more factors that could potentially be quantifiable and added into the algorithm. What about greenhouse gas emissions, or the level of scenery? I wonder how easily those things could be accounted for. I am still wondering about the accountability of qualifiable data as playing a part into network analysis. Perhaps in the future our GPS’ could account for personal preferences and tailor their network analysis to the individual itself? This would raise questions over privacy, perhaps. Though with growing levels of information being tracked anyway, it’s almost something to be expected. I would be interested in knowing more about the evolution of network analysis, and I am looking forward to the presentation on Monday

I found this article quite interesting, in both its recap of traditional Network analyses (i.e. Djikstra’s formula) as well as how the network features of GIS are some of GIScience’s earliest and most popular uses. I find the point on how Graph Theory is ultimately the thought holding this immense function together. On this train of thought, I was very surprised to hear that a ‘non-topological’ network existed and is still used to some degree. How a network can be formed without information linking the network to other nodes makes no sense to me, and seems to defeat the point of creating a network.

I like how the author states that Network GIS is a sub-discipline of GIScience, and goes so far as to claim it’s the only one with linear referencing, which I assume since many GIS functions rely on network analysis, ultimately anything that uses a network incorporates this (making it seem not that out of the ordinary).

Lastly, I found the use of Network Analysis in multi-disciplinary fields like microbiology and neurology very interesting, and definitely would use this as an argument that network analysis is purely a tool. As a tool it’s extremely powerful in that it’s a simple to use and understand data structure which can use many algorithms for interesting analyses.

-MercatorGator

Network analysis is very useful for showing relationships between objects/agents/people and does not require some of the more formal geographic foundations. The result is the formation and growth of informal and natural linkages to create complex systems which can model how things are connected to each other. It essentially provides an alternative to geographic datum for locating points in space through their relationships to other points. A good example are social media networks: the connections that individuals make online forms a global network of information about people and their relationships with each other.

An interesting topic highlighted in this article is the contrast between topological and non-topological data models. This distinction is interesting for me as a geography student since it seems ridiculous to exclude topology when thinking about networks. the paper makes a similar statement by explaining how these models were effectively useless as the are simply points and lines with no substantial information available for analysis. I would have appreciated a bit more explanation fro non-topological data models such as an example of how it may be used and why that might be advantageous over topological models in some uses.

The article makes one particularly large claim: Network GIS is the only sub-discipline to have redefined the spatial reference system on which locations are specified. Im not going to agree or disagree with this statement but I think the paper could’ve done a better job at supporting this argument and contrasting against potential sub-disciplines.

Goodchild does a thorough job assessing the benefits and hindrances of his three methods for quality assurance of VGI. His first two, the crowd-sourcing approach and the social approach, he evaluates in comparison to Wikipedia contribution. Goodchild failed to specify a few important details of the social approach. Ideally Wikipedia contributions are made by users who have specific knowledge of a subject. User profiles on Wikipedia list a user’s contributions/edits, as well as an optional description of the user’s background and interests (and accolades if they are a frequent or well-regarded contributor). An OSM user profile could similarly denote their [physical] area of expertise, and also register regions where the user has made the most contributions/edits, giving them more “credibility” for other related contributions.

An important aspect that Goodchild failed to mention regarding the crowd-sourcing approach is the barrier to editing OSM features. While Linus’ Law can certainly apply for geographic data, someone who sees an error in OSM would need to be a registered and knowledgeable user to fix the error. In Wikipedia, an “Edit” button is constantly visible and one need not register to make an edit. Legitimate Wikipedia contributions must also be accompanied by a citation of an outside source, an important facet that geographic information often lacks.

The geographic approach to VGI quality assurance requires a set of “rules.” Goodchild is concerned with the ability of these rules to distinguish between a real and imagined landscape, giving an example based on the characteristics of physical features such as coastlines, river systems, and settlement location. Satellite imagery has provided the basis of much of OSM’s physical geographic features. Quality assurance is more often concerned with the name and location of man-made features. A set of rules for man-made features could be more easily determined through a large-scale analysis of similarly tagged features and their relationship to their surroundings. I.e. a restaurant located in a park away from a street might be flagged as “suspicious” since its surroundings do not match the surroundings of other “restaurant” features.

This article by Zook et al. (2010) talks about VGI specifically in the context of the 2010 Earthquake in Haiti, but more broadly discusses many of the issues presented in Goodchild and Li (2012) regarding the accuracy and validity VGI. I think Zook et al. (2010) do a good job of considering many aspects of VGI, including issues in data licensing and compatibility, as well as the exclusive nature of VGI which is mostly restricted to people with the technical skills to participate in many cases, and the fact that “there will always be people and communities that are left off the map” (29). While reading that line I wondered, even though VGI is not necessarily accurate, and even though some people will be completely excluded from the VGI for a myriad of reasons (no access to internet or mobile platforms, illiteracy, distance from centres of help, etc…) is it not worth trying? There is a level of error and inaccuracy in any projected geographic information, but that does not stop us from using GISystems.

Moreover, while reading this I thought back to the Johnson, Ricker and Harrison (2017) article I shared with the class, where many of the same issues in accuracy, licensing and intention are presented. I wondered if, despite these unresolved issues, UAVs do not present an opportunity to collect objective, real-time data in instances of disaster mitigation and relief? Because UAVs were used in recent instances of disaster relief, I wonder how the discussion has shifted to include some of the particular issues that arise from their use.

I found it very interesting how Curtin (2007) points out that network analysis is the only subfield of GISciences that has redefined a spatial reference system. Linear referencing, or using the network itself as a reference, is so intuitive that I had never thought of it as an alternative method of spatial referencing. I realize that standardized spatial referencing is something that I take for granted and alternative methods may be an interesting direction for future research.

This statement can be readily debated, but in my mind, network analysis is a field within GISciences that perhaps has the most tangible impact on our daily lives, and can be applied to the most diverse types of phenomena. The authors highlight routing as one of the most fundamental operations in network analysis, and I couldn’t imagine our society functioning without it. Routing is particularly relevant in urban areas where efficient movement from point A to point B across complex road systems is essential for the transportation of people and goods.

Shortest path routing may be the most basic implementation, but I am curious to understand how other factors can be incorporated into routing algorithms to enhance efficiency. The authors indicate that “many parameters can be set in order to define more complex versions of shortest path problems”. In urban areas, for example, how are factors such as traffic, road speed limits, and road condition integrated to provide better routing options?

In reading this article, I was reminded of a previous article that we read on spatial social networks (Radil et al., 2009). Both of these articles highlight the interesting role of space in network analysis. Networks are fundamentally spatial due to their graphical basis, but they can also be used to represent explicitly spatial geographic networks.

As with many of the topics covered in class, though I have used network analysis, I never read much background on the subject, because I mostly used it as a tool in various GISystems applications. For instance, I had not ever thought of the origin of the shape file, or some of the positive/negative attributes beyond the fact that I use shape files for some things, and not for others. Once again, this shows the shortcomings of using GIS strictly as a tool, and some of the important background and concepts that are lost when used in this way.

One thing that particularly stood out in this article by Curtin (2007) was the discussion of the Travelling Salesman Problem (TSP), and how solutions are heuristic, and the problem/ abstraction from “true” solutions not properly or completely understood. To me, this links back to the what I feel I am getting out of this course, which is a deeper understanding of the background, importance, and shortcomings of various GIScience concepts which is truly lacking in other GIS courses I have taken. As Curtin (2007) mentions, network analysis is now mostly used in route mapping like MapQuest (once upon a time) and Google Maps, without most people having any background knowledge on how those routes are computed or the algorithms used. This is something that the author touches on briefly, but doesn’t explore fully, and something I feel is very important in the broadening use of GIScience in everyday life.