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@@ -996,7 +996,7 @@
     "href": "sessions/week8.html#other-preparation",
     "title": "Textual Data",
     "section": "Other Preparation",
-    "text": "Other Preparation\nCome to class prepared to discuss the following readings:\n\n\n\nCitation\nArticle\nChatGPT Summary\n\n\n\n\nMiller and Goodchild (2015)\nURL\nN/A\n\n\nDelmelle and Nilsson (2021)\nURL\nN/A\n\n\nReades et al. (in review)\nURL\nN/A\n\n\n\n\n\n\n\n\n\nConnections\n\n\n\nConceptually, this is by far the hardest week of the entire term: there is very little upon which to draw from other modules, and the processing of text with computers rarely makes it beyond simple regular expressions; however, the growth in data that is ‘accidental, open, and everywhere’ (Arribas-Bel 2014) means that a lot more of it is unstructured and contains free-text written by humans as well as numerical and coordinate data generated by sensors or transactions.\n\n\nIf you’re feeling ambitious then you can use the tutorial from the Programming Historian to look at the foundations of text processing and how we can extract important terms from a document as well as, ultimately, the foundations upon which modern Large Language Models are built.",
+    "text": "Other Preparation\n\nReadings\nCome to class prepared to discuss the following readings:\n\n\n\nCitation\nArticle\nChatGPT Summary\n\n\n\n\nMiller and Goodchild (2015)\nURL\nN/A\n\n\nDelmelle and Nilsson (2021)\nURL\nN/A\n\n\nReades et al. (in review)\nURL\nN/A\n\n\n\n\n\nStudy Guide\nReading Miller and Goodchild (2015):\n\nHow does “data-driven geography” differ from traditional geographic research?\nHow can “data-driven approaches” be incorporated into geographic research, and what are their potential benefits and limitations?\n\nReflecting on Reades et al. (in review):\n\nWhy has text become increasingly interesting to computational social scientists?\nWhat are the specific advantages of textual data for understanding cities?\nWhat are some of the key challenges and limitations of using textual data in urban research, and how can researchers address these challenges?\n\nConnecting this to Delmelle and Nilsson (2021):\n\nWhat is the framework that Delmelle and Nilsson developed for understanding the language used to advertise properties, and how does it connect to the racial and income profiles of neighborhoods?\nWhat are the implications for understanding neighborhood change and (potential) discrimination in the housing market?\n\nCollecitvely:\n\nHow do these readings connect to the broader themes of the course, and what are the implications for your own research?\n\n\n\n\n\n\n\nConnections\n\n\n\nConceptually, this is by far the hardest week of the entire term: there is very little upon which to draw from other modules, and the processing of text with computers rarely makes it beyond simple regular expressions; however, the growth in data that is ‘accidental, open, and everywhere’ (Arribas-Bel 2014) means that a lot more of it is unstructured and contains free-text written by humans as well as numerical and coordinate data generated by sensors or transactions.\n\n\nIf you’re feeling ambitious then you can use the tutorial from the Programming Historian to look at the foundations of text processing and how we can extract important terms from a document as well as, ultimately, the foundations upon which modern Large Language Models are built.",
     "crumbs": [
       "Part 2: Process",
       "8. Textual Data"
@@ -1079,7 +1079,7 @@
     "href": "sessions/week9.html#other-preparation",
     "title": "Selecting Data",
     "section": "Other Preparation",
-    "text": "Other Preparation\nCome to class prepared to discuss the following readings:\n\n\n\nCitation\nArticle\nChatGPT Summary\n\n\n\n\nElwood and Wilson (2017)\nURL\nN/A\n\n\nO’Sullivan and Manson (2015)\nURL\nN/A\n\n\nXie (2024)\nURL\nN/A\n\n\n\n\n\n\n\n\n\nConnections\n\n\n\nHere we focus on what you can now bring to the table that might help you to dinstinguish yourself from someone who did a ‘data science degree’; through what we study here (and in your other modules) you have been exposed to ways of thinking about data critically and ethically that are rarely part of an Informatics or Machine Learning degree. But as we hope you’re now conviced: these things matter. It’s not just that being critical and ethical is a good way to do your job (whatever that might end up being), it’s that being critical and ethical is a good way to do your job better. You will writing better code. You will write better assessments. You will draw better conclusions.",
+    "text": "Other Preparation\n\nReadings\nCome to class prepared to discuss the following readings:\n\n\n\nCitation\nArticle\nChatGPT Summary\n\n\n\n\nElwood and Wilson (2017)\nURL\nN/A\n\n\nO’Sullivan and Manson (2015)\nURL\nN/A\n\n\nMattern (2015)\nURL\nN/A\n\n\n\n\n\nStudy Guide\n\nHow do the concepts of “physics envy” and “geography envy” relate to the evolution of GIScience and the increasing use of urban dashboards?\nCompare and contrast the “Week 10: Ethics” approach to critical GIS with the integrated approach advocated by Elwood and Wilson (2017). What are the strengths and weaknesses of each approach?\nMattern (2015) argues that urban dashboards can obscure the complexity of cities by “bracketing out” certain variables and simplifying representations. How does this critique connect to the concerns raised by Elwood and Wilson (2017)?\nHow does the emphasis on generalization in physics-based approaches to social phenomena highlighted by @O’Sullivan and Manson (2015) challenge traditional geographical perspectives that prioritize local and particular knowledge?\nWhat are the shared concerns and potential synergies between the arguments of Elwood and Wilson (2017) and O’Sullivan and Manson (2015)?\n\n\n\n\n\n\n\nConnections\n\n\n\nHere we focus on what you can now bring to the table that might help you to dinstinguish yourself from someone who did a ‘data science degree’; through what we study here (and in your other modules) you have been exposed to ways of thinking about data critically and ethically that are rarely part of an Informatics or Machine Learning degree. But as we hope you’re now conviced: these things matter. It’s not just that being critical and ethical is a good way to do your job (whatever that might end up being), it’s that being critical and ethical is a good way to do your job better. You will writing better code. You will write better assessments. You will draw better conclusions.",
     "crumbs": [
       "Part 2: Process",
       "9. Selecting Data"
@@ -1332,7 +1332,7 @@
     "href": "sessions/week10.html#other-preparation",
     "title": "Presenting Data",
     "section": "Other Preparation",
-    "text": "Other Preparation\nYou might want to look at the following reports / profiles with a view to thinking about employability and how the skills acquired in this module can be applied beyond the end of your MSc:\n\nGeospatial Skills Report <URL>\nAAG Profile of Nicolas Saravia <URL>\nWolf et al. (2021) <URL>\n\n\n\n\n\n\n\nConnections\n\n\n\nWhile I expect most of you will be focussed on assessments, you should seriously consider returning to the three readings assigned over Reading Week as they will help you to reflect on what you’ve learned this term in this module and across the programme as a whole. The other three might be useful in terms of looking at the direction of the field, the opportunities in industry, and the kinds of work that people with (sptial) data science skills can do.",
+    "text": "Other Preparation\n\nReadings\nCome to class prepared to discuss the following readings:\n\n\n\nCitation\nArticle\nChatGPT Summary\n\n\n\n\nMattern (2015)\nURL\nN/A\n\n\n\n\n\nAdditional Resources\nYou might want to look at the following reports / profiles with a view to thinking about employability and how the skills acquired in this module can be applied beyond the end of your MSc:\n\nGeospatial Skills Report <URL>\nAAG Profile of Nicolas Saravia <URL>\nWolf et al. (2021) <URL>\n\n\n\n\n\n\n\nConnections\n\n\n\nWhile I expect most of you will be focussed on assessments, you should seriously consider returning to the three readings assigned over Reading Week as they will help you to reflect on what you’ve learned this term in this module and across the programme as a whole. The other three might be useful in terms of looking at the direction of the field, the opportunities in industry, and the kinds of work that people with (sptial) data science skills can do.",
     "crumbs": [
       "Part 2: Process",
       "10. Presenting Data"
@@ -7300,7 +7300,7 @@
     "href": "sessions/week7.html#other-preparation",
     "title": "Spatial Data",
     "section": "Other Preparation",
-    "text": "Other Preparation\nCome to class prepared to discuss the following readings:\n\n\n\nCitation\nArticle\nChatGPT Summary\n\n\n\n\nD’Ignazio and Klein (2020) Ch.6\nURL\nN/A\n\n\nLu and Henning (2013)\nURL\nN/A\n\n\nBunday (n.d.)\nURL\nN/A\n\n\nVanderPlas (2014)\nURL\nN/A\n\n\n\n\n\n\n\n\n\nConnections\n\n\n\nWe’re focussing this week on the links between the data you’re working with and the process you’re trying to study! You might (quite reasonably) assume that these line up nicely, but in the era of big data that isn’t the case. ‘Accidental’ data (Arribas-Bel 2014) such as smartcard, mobile, web traffic, etc. are only ever partial accounts of messy human reality, so we want you to think about the gap between what you have and what you want to study.",
+    "text": "Other Preparation\n\nReadings\nCome to class prepared to discuss the following readings:\n\n\n\nCitation\nArticle\nChatGPT Summary\n\n\n\n\nD’Ignazio and Klein (2020a) Ch.6\nURL\nN/A\n\n\nLu and Henning (2013)\nURL\nN/A\n\n\nBunday (n.d.)\nURL\nN/A\n\n\nVanderPlas (2014)\nURL\nN/A\n\n\n\n\n\nStudy Guide\nThinking about Bunday (n.d.):\n\nThe professors in Bundy’s article seem to be searching for a data transformation that will reveal the “true” ranking of the students. How does this relate to the concept of a “data-generating process” discussed in Lu and Henning?\nBundy’s tale suggests that any data transformation can be used to justify a particular conclusion. How does this relate to D’Ignazio and Klein (2020b, Ch.4) and warnings about the potential for bias in data analysis? Are there specific examples that resonate?\n\nReflecting on Lu and Henning (2013):\n\nLu and Henning use the example of retail cashier salaries to illustrate the limitations of traditional population-based thinking. How does their example help us to understand how the concept of a “population” is used and potentially misused?\nWhat are the implications of Lu and Henning’s argument for the use of data in policy-making, and how can we connect this back to D’Ignazio and Klein (2020b, Ch.4) as part of a larger debate around ‘the numbers’?\n\nConsidering VanderPlas (2014):\n\nIn light of the above, what can we learn from Jake’s analysis of cycling data in Seattle about exploratory data analysis?\n\n\n\n\n\n\n\nConnections\n\n\n\nWe’re focussing this week on the links between the data you’re working with and the process you’re trying to study! You might (quite reasonably) assume that these line up nicely, but in the era of big data that isn’t the case. ‘Accidental’ data (Arribas-Bel 2014) such as smartcard, mobile, web traffic, etc. are only ever partial accounts of messy human reality, so we want you to think about the gap between what you have and what you want to study.",
     "crumbs": [
       "Part 2: Process",
       "7. Spatial Data"
@@ -7311,7 +7311,7 @@
     "href": "sessions/week7.html#practical",
     "title": "Spatial Data",
     "section": "Practical",
-    "text": "Practical\nIn the practical we will continue to work with the InsideAirbnb data, here focussing on the second ‘class’ of data in the data set: geography. We will see how to use GoePandas and PySAL for (geo)visualisation and analysis.\n\n\n\n\n\n\nConnections\n\n\n\nThe practical focusses on:\n\nCreating/working with geo-data in Python.\nMaking maps with Python.\nExploring the data visually.\n\n\n\n\nTo access the practical:\n\nPreview\nDownload",
+    "text": "Practical\nIn the practical we will continue to work with the InsideAirbnb data, here focussing on the second ‘class’ of data in the data set: geography. We will see how to use GoePandas and PySAL for (geo)visualisation and analysis.\n\n\n\n\n\n\nConnections\n\n\n\nThe practical focusses on:\n\nCreating/working with geo-data in Python.\nMaking maps with Python.\nExploring the data visually.\n\n\n\nTo access the practical:\n\nPreview\nDownload",
     "crumbs": [
       "Part 2: Process",
       "7. Spatial Data"

sessions/week10.html

@@ -461,6 +461,28 @@ <h2 class="anchored" data-anchor-id="preparatory-lectures">Preparatory Lectures<
 </section>
 <section id="other-preparation" class="level2">
 <h2 class="anchored" data-anchor-id="other-preparation">Other Preparation</h2>
+<section id="readings" class="level3">
+<h3 class="anchored" data-anchor-id="readings">Readings</h3>
+<p>Come to class prepared to discuss the following readings:</p>
+<table class="caption-top table">
+<thead>
+<tr class="header">
+<th style="text-align: left;">Citation</th>
+<th style="text-align: left;">Article</th>
+<th style="text-align: left;">ChatGPT Summary</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td style="text-align: left;"><span class="citation" data-cites="mattern:2015">Mattern (<a href="#ref-mattern:2015" role="doc-biblioref">2015</a>)</span></td>
+<td style="text-align: left;"><a href="https://doi.org/10.22269/150309">URL</a></td>
+<td style="text-align: left;">N/A</td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="additional-resources" class="level3">
+<h3 class="anchored" data-anchor-id="additional-resources">Additional Resources</h3>
 <p>You might want to look at the following reports / profiles with a view to thinking about employability and how the skills acquired in this module can be applied beyond the end of your MSc:</p>
 <ul>
 <li>Geospatial Skills Report &lt;<a href="https://www.gov.uk/government/publications/demand-for-geospatial-skills-report-for-the-geospatial-commission">URL</a>&gt;</li>
@@ -481,6 +503,7 @@ <h2 class="anchored" data-anchor-id="other-preparation">Other Preparation</h2>
 </div>
 </div>
 </section>
+</section>
 <section id="practical" class="level2">
 <h2 class="anchored" data-anchor-id="practical">Practical</h2>
 <p>The practical will lead you through the fine-tuning of data visualisations in Matplotlib/Seaborn. In many ways, this should be seen as largely a recap of material encountered in previous sessions. However, you should see this as an important step in the production of outputs and analyses needed for the final project. That said, you would be better off spending time on the <em>substance</em> of the report first and only turning to the fine-tuning of the visualisations if time permits.</p>
@@ -514,6 +537,9 @@ <h2 class="anchored" data-anchor-id="practical">Practical</h2>
 </section>
 
 <div id="quarto-appendix" class="default"><section class="quarto-appendix-contents" role="doc-bibliography" id="quarto-bibliography"><h2 class="anchored quarto-appendix-heading">References</h2><div id="refs" class="references csl-bib-body hanging-indent" data-entry-spacing="0" role="list">
+<div id="ref-mattern:2015" class="csl-entry" role="listitem">
+Mattern, Shannon. 2015. <span>“<span class="nocase">Mission control: A history of the urban dashboard</span>.”</span> <em>Places Journal</em>. <a href="https://doi.org/10.22269/150309">https://doi.org/10.22269/150309</a>.
+</div>
 <div id="ref-wolf:2021" class="csl-entry" role="listitem">
 Wolf, Levi John, Sean Fox, Rich Harris, Ron Johnston, Kelvyn Jones, David Manley, Emmanouil Tranos, and Wenfei Winnie Wang. 2021. <span>“Quantitative Geography III: Future Challenges and Challenging Futures.”</span> <em>Progress in Human Geography</em> 45 (3). SAGE Publications Sage UK: London, England:596–608. <a href="https://doi.org/10.1177/0309132520924722">https://doi.org/10.1177/0309132520924722</a>.
 </div>