The Urban Story TellerThe Urban Story Teller
Léonie Hubert, Knapp, Volz
Elina
Chair of Architectural Informatics Technical University of Munich We are such an communityengaged:) The impressesarchitecturemodernistme.
Sophia
The Story Teller
Chair of Architectural Informatics Prof. Dr.-Ing. Frank Petzold Critical Modeling Ivan Bratoev, Nick Förster, Frank Petzold Léonie Hubert, Sophia Knapp, Elina Volz 067102, 067102, 03753495
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Urban
3 3423151210864 Identity Crisis of Neuperlach Research on Urban Identity Analog and Digital Sensing of Identity in Neuperlach Crowd Sourcing Urban Identities through Social Media Concept of the Tool - Making Urban Identites Visible Implementation of the Tool Three Use Cases Reflection and Outlook Table of Contents
In the context of civic participation events for the Development Plan, urban planners conducted that the actual local identity perceived by the district‘s residents strongly contradicts its public image. A reason why the positive traits and features are underrepresented in the public eye is the lack of analogue and digital platforms for representation and manifestation of identity.
Features that were defined as identitarian for Neuperlach in the Development Plan were mostly of physical and built nature such as Neuperlach‘s bridges, the Wohnring Complex, the churches and the PEP Shopping Mall. In Context of the Project „Critical Modelling 2.0“ we asked ourselves, whether there are further hidden identities and qualities of the district that can be somehow unraveled from existing data. Sourcing the subjectively perceived qualities from a larger group of people could paint a more representable picture of what Neuperlach really has to offer and give urban planners a hint on which existing potentials could be enhanced even more.
Idenitity Crisis of AsNeuperlachamodernistssettlementbuiltinthe
1 Municipality of Munich (2021): Neuperlach: Fit für die Zukunft! Integriertes Stadtteilentwicklungskonzept. Department of Urban Planning and Building Regulation Munich. ADEPT Architects. p. 35.
1970s, Neuperlach suffers – just as many other sattelite districts of Germany – from a variety of stigmata, ranging from supposed crime rates, critical social indicators such as poverty and unemployment as well as poor architecture. The predominantly negative image it carries in the public eye and media is not serving the district the justice it deserves. Therefore, improving it and strengthening the district‘s identity became one of the eight key targets of the District Development Plan published by the Municipality of Munich in 20211.
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5 examples of excerpts from the District Development Plan 2021 where the term „identity“ (Identität) is mentioned Handlungsfeld Nr. 9: Identität und Image
4 Jokar et. al (2021): Assessment of urban identity in newly built neighborhoods. IN. Geojournal. p.1.
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2. ... traits and characteristics that distinguish a place from another. According to Kevin Lynch „Identity is the extent to which a person can recognize or recall a place as being distinct from other places - as having a vivid, or unique, or at least aparticular, character of its own.“3 This means, that is is necessary to compare findings of Neuperlach to other areas of the city.
The Phenomenon of „Place-Related Identity“
1. ... physical features of a place2, which include physical elements such as landmarks, parks, rivers and roads.
Before Studying the existing Identity of Neuperlach, it was important to define the subject of study. In urban research fields identity tied to urban spaces is described with terms such as „place-related identity“ or „sense of place“. As there is no coherent scientific definition, we have come up with our own understanding for urban identity relating to theoretical approaches. For our project, urban identity is consistent of:
4. ... activities in public spaces as platforms for social interaction4, which means all activities in publicly observable spaces - not excluding the digital world - shall be taken into consideration.
2 Bernardo, Fátima, Almeida Joana and Catarina Martins (2016): Urban Idenitty and Tourism: different looks, one single place. In: iceIproceedings. p.1
3. ... collectively shared memories and symbolic meanings associated with the setting2, which implies that a crucial amount of people have to share it.
3 Lynch, Kevin (1981): A theory of good city form. Cambridge,Massachusetts and London, England, MIT Press.
7 CollectivelyLandmarks Shared Memories Differences to other Places Activities in Public Spaces (digital or real-life)
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Nevertheless, walking through the streets of Neuperlach confirmed our impressions that the urban identity of Neuperlach was hard to grasp. How are the people living here? Are they investing the public spaces ? What are their perceptions of Neuperlach?
All these questions going towards only one : are we missing a layer of reality?
Scrolling through Neuperlach Instagrams’ posts gave us some interesting perspectives. Some of the previous elements, such as the “Wohnring” or qualitative greenspaces, were present on the platform, as well as others non detected. Indeed, people apparently appreciate the view of the Alps, visible from the rooftops of Neuperlachs’ buildings…
Analogue and Digital Sensing of Identity in
Trying to go deeper, we scraped posts by searching for #neuperlach and created a word cloud from the most dominant hashtags in the posts, and extended the analysis to the other districts of Munich. The question was then, how do we go further?
gives us some information about the existing elements part of the districts’ identity. The PEP, churches and subcenters attract people and animate the spaces around. The organisation of the “Wohnring”, the Ospark and its nature seems to be relevant for people, as well as the significant pedestrian network or the predominant modernist architecture.
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TheNeuperlachurbandevelopmentplanofNeuperlach
Crowd Sourcing Urban Identities through Social Media
Perceptions of spaces can be grasp in a much more relevant way by understanding where our thoughts intersect. Thus, this complex network of subjectivity enables a partial understanding of what makes common, only if we know how to structure and deal in general with this goldmine of data. „As a visual-locative social medium, Instagram can be regarded as a participatory sensing system. Its users produce data as they navigate their everyday lives, smartphones in hand.“ From how to study the city on Instagram, John D. Boy, June 2016. Instagram was the best choice for us as people share place-based experiences on photos. Moreover, Instagram is known to be the Showcase of people’s life, assuring filtered thoughts more relevant to analyse the identity of a place.
These collective memories of a place, part of the urban identity, can also be expressed in the digital world, where the platform of social media becomes the digital mirror of public spaces.
Social media connect people together by gathering moments of life and sharing thoughts. For the first time, perceptions of places becomes visible and takes part in a gigantic architecture. Our mind is traveling in this parallel universe, jumping from content to content, combining thoughts by reactions and approvals we leave behind. The path we take will change our own connections of thoughts and influence other users. People discover and apprehend urban spaces even before setting a foot in it. Another type of space is this way created. A digital one, documenting and distorting the real one.
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To begin with, we gathered the post by searching for the #neighborhood. Almost all the neighborhoods of Munich were targeted, as we needed comparaison to understand what’s really significant in a place, and be able to talk about urban identity. Then, we visualised the networks of hashtags related to them. Some hashtags are bigger and/or have stronger bonds as they are more mentioned together in posts. We used two ways to analyse them:
- Then, to evaluate the uniqueness of these collective memories, we compared the amount of post related to each hashtags in each #neighborhood, as they are usually the same hashtags in each network created. This way, we can reach a part of Munich’s neighborhood identity.
Concept of the Tool
How do we retrieve the posts linked to located spaces from all the other ? And then, how do we distinguish the different perceptions expressed from one post to another, but also into one single post, as human thoughts are rich and intertwined. This is where the hashtag feature becomes really helpfull. People usually posts with a certain amount of hashtags to make their publication more visible on certain subjects and places. This way, each post with the same hashtag is gonna be connected and gathered aside on Instagram. These hashtags can cover a large range of memories, from #architecture, #art, #nature, #bier, to #love and so much more... Moreover, looking at hashtags help us to filter the posts impressions : users made the effort to select and right down theses hashtags in
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- First, we tried to find parts of the networks that were more intensely connected, to clear them, and be able to read quickly what makes common in each neighborhood.
Two main questions came through the analysis of Instagrams‘ posts.
Soparticular.whatdid we do exactly?
This microcosmos of hashtags post helps indeed to structure complex thoughts gathered on thesepost. thoughts can then be more analysed trough the hashtags work, by looking for patterns edges of the diagram created.
Unlesspost. we focus about hashtags. They help gather publications on certain themes, without the need for users to be theseconnected.thoughts can then be more easily analysed trough the hashtags net work, by looking for patterns trough edges of the diagram created.
# Network navigation through hashatgs #
Hashtags can be related to infinite types of mental images, from concepts to activities and spaces. How do we say this is a cluster of hashtags, as everything is related ?
13 #Altstadt #Maxvorstadt #Neuperlach #laim #trudering #architecture #architecture #architecture #architecture #architecture
Posts are never directly related, but connected trough users reactions (or follow) : likes, comments, reposts etc.
This microcosmos of hashtags on each post helps indeed to structure the complex thoughts gathered on each
multiple hashtags on a single publi cation increase it’s visibility for the user posting it. As for researchers, it helps having a general understanding of what a post is about.
When1Tool-Findingpostswewantedto
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Implementation of the
5 Kees, Christiaanse and Mirjam Zügler (Ed.)(2018): Potato Plan Collection. p.144.
start systematically downloading posts for every #[district] in Munich, we looked at the official lists of “Stadtbezirke” and “Stadtbezirksteile”. We quickly realized though, that the official names are only partly reflected in the perception of the population, so using them would lead to a distorted image. After some research we found a so-called “potato plan” of Munich5, a map that, based on urban fabric, subdivides the city into different “blobs” (the potatoes) that have loose boundaries, choosing their names so that they better reflect the common names used by the public. We slightly modified the potato plan, merging areas that consist or multiple parts (“east” and “west” for example) and dropped districts, for which we found less than 100 posts. We ended up with 67 districts for the whole city of Munich.
2 - Downloading posts Instagram officially only allows very limited access to its data, none of which is suitable for research at a larger scale. An alternative way to an official API for accessing data that is publicly available on the internet is crawling or scraping, which is a way of automating the process of scrolling from page to page and collecting data post by post. Instagram does not want this, though, and blocks accounts who try to scrape data from the page. They can easily be detected, because a scraping-script will act differently from a human scrolling through the page. That’s why scraping also isn’t a good option for obtaining data from Instagram. There are third party services though, that can be used to obtain data from different websites, including Instagram. We used a third-party service hosten on RapidAPI6, which is a marketplace where developers can offer APIs for different services, how they obtain the data they provide is not disclosed. The API gives access to everything on Instagram that a user who is logged in to the site could also access, including searching for a feed of posts by hashtag or location tag. We were able to use a tool from another student, who built a python program that automates the process of sending requests to RapidAPI7 for a given search term and structuring the returned data to be in a csv-format that contains url, caption, hashtags and some other things for every post, which saved us a lot of effort. https://rapidapi.com/arraybobo/api/instagram-scraper-2022
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7https://gitlab.lrz.de/lukaslegner/idp2
The next thing that we noticed by looking at the most frequent hashtags in every district was that many hashtags are not very informative. We looked through the most popular hashtags and decided to drop hashtags from three different categories for our analysis: - Munich related (#münchen, #munich, #muc, #089, #ig_munich, #muenchenstagram, #munichblogger, #munichlove, #munichlife… )
The last thing that we noticed from looking at the most frequent hashtags in different areas was, that in some places there are a few accounts that have so many posts, that the hashtags that they use overshadow all others, mostly advertisementrelated. For that reason, we decided to give every user one “vote” per hashtag when counting the number of mentions of a hashtag in each district.
As an initial step of analyzing the downloaded data, we counted the frequency of different hashtags in all districts. Looking at the results we noticed that many districts have ambiguous names and it was necessary to clean the data. There are multiple “Westend” in the world for example, “Riem” means “Gürtel” in Dutch, for “Laim” we got many posts related to (misspelled) “Liam” from the band “One Direction”, there is another “Neuhausen” in Switzerland to mention a few. We decided to drop all posts from those hashtags, that did not also contain a hashtag referring to Munich (#münchen, #munich, #muc …) additionally to the #[district] for that reason.
16 3 Filtering data
- “instagram”-related (#instadaily, #instagood, #instamood, #instalove #igers, #picoftheday, #nofilter, #mood, #likeforlike …) - Seasonal (#2020, #2021, #2022, … , #monday, #tuesday, … , #january, #february, … , #chistmas, #abend, #corona, #wochenende …
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4 Finding unique fingerprints
A first indicator that we looked at when comparing districts is simply the “instagrammability”, that we defined for us as the number of users mentioning its hashtag on Instagram. We found huge differences between the different districts there already, #nymphenburg being by far the most popular, probably due to tourists, followed by #schwabing, #giesing and #maxvorstadt. The districts in the outskirts of the city are far less talked about. This is interesting, because it gives an indication about how popular a place is.
Another rather simple indicator for seeing what hashtags are relevant for a district is counting the number of users mentioning each hashtag and looking at the most popular ones. 4.3 Unique hashtags In 4.2 we got some interesting results, though multiple places talk about #art or #food for example. Nevertheless, we wanted to find out what hashtags are unique to an area and used the following process for that: First, we counted the number of users mentioning every hashtag in a district: users(hashtag 1,hashtag 2...)= Theusersmentioningallhashtagsinthegivenlist
4.2 Hashtag frequency
4.1 Number of users
Nabsolute (hashtag,district)= |users(hashtag,district)| Nrelative (hashtag,district)= Nabsolute (hashtag,district) |users(district)| city =[district1,district2,...] Uniqeness(hashtag,district)= Nrelative (hashtag,district) Nrelative (hashtag,city ) Rank (hashtag,district)= Uniqueness(hashtag,district)2 ∗ Nabsolute (hashtag,district)
Then, we computed the relative amount for every hashtag in a district: In the end we computed uniqueness by comparing the relative amount of a hashtag in a district to its relative amount in all posts from all districts. In order to decide the order of hashtags on our website under the “uniqueness”tab, we also computed a rank for every hashtag, consisting of uniqueness and total amount, otherwise we would have ended up showing very unique but also very obscure and rarely mentioned hashtags, which would not have been representative for a Throughdistrict.this method we were for example able to discover event locations, public squares, parks, local sports clubs, restaurants and bars that seem to be important in a district. users(hashtag 1,hashtag 2...)= Theusersmentioningallhashtagsinthegivenlist Nabsolute (hashtag,district)= |users(hashtag,district)| Nrelative (hashtag,district)= Nabsolute (hashtag,district) |users(district)| city =[district1,district2,...] Uniqeness(hashtag,district)= Nrelative (hashtag,district) Nrelative (hashtag,city ) Rank (hashtag,district)= Uniqueness(hashtag,district)2 ∗ Nabsolute (hashtag,district) users(hashtag 1,hashtag 2...)= Theusersmentioningallhashtagsinthegivenlist Nabsolute (hashtag,district)= |users(hashtag,district)| Nrelative (hashtag,district
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)= Nabsolute (hashtag,district) |users(district)| city =[district1,district2,...] Uniqeness(hashtag,district)= Nrelative (hashtag,district) Nrelative (hashtag,city ) Rank (hashtag,district)= Uniqueness(hashtag,district)2 ∗ Nabsolute (hashtag,district) users(hashtag 1,hashtag 2 )= Theusersmentioningallhashtagsinthegivenlist Nabsolute (hashtag,district)= |users(hashtag,district)| Nrelative (hashtag,district)= Nabsolute (hashtag,district) |users(district)| city =[district1,district2,...] Uniqeness(hashtag,district)= Nrelative (hashtag,district) Nrelative (hashtag,city ) Rank (hashtag,district)= Uniqueness(hashtag,district)2 ∗ Nabsolute (hashtag,district)
19 4 Finding Stories
While we got interesting results in 4, we still wanted to go a little further than looking at individual hashtags. The first reason for that was that an individual hashtag sometimes doesn’t tell us much. For example, many places use #nature, but it is more interesting to see what other hashtags that hashtag is combined with, in order to find what type of nature people appreciate in which location exactly, for example “#nature #ostpark #lake #ducks“ tells more of a story than just #nature or #ducks by itself. The second reason is that a district might not have only one “story” that is shared by everyone, but can consist of multiple different ones, and we wanted to discover those.
In graph theory a community is defined as a group of nodes, where connections between nodes within are stronger than connections between nodes of different communities. If hashtags are mentioned together frequently, they will form a community in the graph, which is why we wanted to discover those. In order to detect these communities we used the fast greedy modularity optimization algorithm8. Previous research showed that it worked for the analysis of large graphs created from hashtags used on social media9.
It wasn’t a trivial task to create graphs from our data. It was important to both encode the total number of mentions of a hashtag, as it’s an indicator of its overall importance, and the strength of its relationship with other hashtags into the graph.
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If hashtags A and B are mentioned together 5% of the time they are mentioned, the edge between them should be less important than the edge between hashtags C and D, that are mentioned together 90% of the time they are mentioned for Weexample.came up with the following method of creating graphs that results in undirected graphs with weighted edges:
9A. Ruiz-Frau, A. Ospina-Alvarez, S. Villasante, P. Pita, I. Maya-Jariego, S. de Juan, Using graph theory and social media data to assess cultural ecosystem services in coastal areas: Method development and application, Ecosystem Services, Volume 45, 2020, 101176
8Aaron Clauset, M. E. J. Newman, and Cristopher Moore (2004): Finding community structure in very large networks. Phys. Rev. E, 70:066111.
2. Give every user a “weight per edge”, calculated as one divided by the total number possible pairs of hashtags mentioned by this user
4. Calculate the weight of every possible edge between 2 nodes by summing up the weight per edge for every user mentioning both hashtags.
This method gives every user one “vote” in total, that is distributed equally amongst all hashtags that he or she used. This means that a user who uses many hashtags has less influence on those than a user who only uses a few. This method was a choice and we could have done this differently, but this is how we got the “best” (as explained in 4) results from different approaches we tried. The algorithm that we used resulted in non-overlapping sets of nodes, which is not ideal, as it does not account for a hashtag being part of multiple “stories” (for example a hashtag like #nature could be mentioned together with a park in the east of a district and with a lake in the south of a district) but in total we seemed to get good results with our chosen method and things like that would be hard to detect.
3. Create a node for every hashtag
We used an implementation from the python library networkx of our selected algorithm10. It is important to point out that there is one correct answer for communities within a graph.
10https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.community.modularity_max.greedy_modularity_communities.html
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1. Group hashtags by user, resulting in a list of user-ids with an array of hashtags for each
As an approximation of how many users are involved in a community of hashtags, we summed up the values of all edges within a community in the end. With the method mentioned above, summing up all edge weights in the created graph equals the number of users, so this seemed like an acceptable approximation.
The algorithm takes a resolution as an input, which decides whether the algorithm should favor smaller or larger communities. We tried a few different values for that and picked the one where the size of communities we found looked most reasonable to us.
An important aspect of the website is that we embed Instagram-posts that are relevant for the current selected district, hashtags and/or story. Instagram is after all a platform based around photos, and showing those photos is very important for the understanding of the meaning of a hashtag or a set of hashtags.
The center of our website is a map containing all districts from the potato map mentioned in 1.The map becomes a heatmap that shows the amount of posts found in each district through a darker color when hashtags are selected. If no hashtag is selected, the more “instagrammable” districts, meaning the ones with more users talking about, have a darker color.
After our analysis in the previous steps we ended up with a lot of tables and folders for the 67 districts that we analyzed that might contain interesting information, though not in an accessible way. This is why we decided to build an interactive web interface to be found at: urbanstoryteller.web.app We built the application using React.js [ https://reactjs.org/ ], a javascript library for building user interfaces, and D3.js [ https://d3js.org/ ], a library that allows the creation of data-driven vector graphics.
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Visualizing Data
Finally, when a district is selected, its most common and most unique hashtags, as well as stories can be seen in the “explore districts” tab.
In the “Explore hashtags”-tab, there is a graph where one can visually explore the absolute and relative amount of the selected hashtags in each district.
1. Architectural and Urban Design
Where can the tool be applied and which target groups is it aimed at? Generally, it is aimed at anyone with the interest to grasp a subjectively perceived layer of the city on a district level. Through the functions of the hashtag heat map and the differentiation of hashtags according to uniqueness, one is able to compare the findings between different districts and thus understand them in the context of the whole city. We defined three uses cases with corresponding professional fields, that could benefit from the tool. How they navigate within the tool and which helpful findings they could come up with, will be explained in the further examples.
Use Cases
traits of districts/cities/villages to help build a recogizable brand target group: marketing agencies, municipalities
Exploration of the „genius loci“ of a project site target professionals: architects, urban designers, project developers
patterns of human behaviour within the city target group: sociologists, urbanists, anthropologists, geographers 3. Place IdentifyingBrandingunique
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2. Urban StudyingResearchdistribution
Urban developpers and architects are not spared the need to understand what‘s in stake on their projects‘ sites. We can even say that the quality of a project depends on its capacity to answer the challenges of a place, play with the constraints, and propose a new way of apprehending the existing in general. Inspiration, to avoid the too made error of generic projects endlessly reproduced, that do not take into account the physical and sociological specificities of a site.
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The Architect
Checking the location on googlemaps, or looking at the surroundings in details with google streetmap help to catch what’s physically there. But what are people expecting here? How do they perceive the surroundings ? These questions are never easy to answer. Grasping the genius loci of a place , it‘s social dynamics, and generally the desires and needs of inhabitants takes time that makers of the city usually don‘t have. This is where the Urban Story Teller of Munich becomes helpfull. As for the other usecases, a new layer of reality is directly accessible, concrete and quite easy to read. In this example, an architect just received a call from another agency who wants to compete with him for a significant project in Trudering. As the program is unfinished, he will have to propose some additional space and uses. Is there some hidden uses of the space they could consider for their project to fit in nicely ? Interested by a precise location, he directly go into the tab « explore district », and look at the most significant hashtags through the 3 different hierarchy : most frequent, most unique, and most intensely connected (or stories). He will discover three main elements about Trudering: the significant social life, culture, and presence of nature...
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By looking at the most unique hashtags connected with #trudering, he discovers the presence of a special event in the neighborhood, with the #truderingerfestwoche on top of the list. Truderings‘ social life seems to be active , also with the #truderingwirtshaus and #bier (spotted in the most frequent hashtags). Also, the well rated #truderinger can say a lot about identity, if people or feeling like they belong in a way to the neighborhood. He decides to click on the hashtag he is the most interested about (orange), and go into the next tab...
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Going further into the stories tab, he can directly see in which storie is the hashtag he selected : the #party story. But there is actually lots of others interesting stories in Trudering. The color opacity, showing the uniqueness, helps him to choose which story is the most relevant to look at. Art is well represented here, due to the presence of a #kulturzentrum, as well as the presence of nature and specially a forest that seems to matter for people...
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He decides to click on the #party story to see concretely what is the Trudering Fest Woche about. The stories tab helps him to understand quickly what are the main subjects about a place, while keeping the biases in mind. He can always click again on the story to see all the stories of the neighborhood again, or go back into another tab to see better the hierarchy between the hashtags. Finally, to communicate and remember these findings, he simply choose to save these visualisations for the upcoming presentations.
In the given example on the right, a geographer studies the access to green spaces in Munich. By filtering a whole collection of hashtags in relation to green spaces such as #green, #woods, #park, #tree, #nature, etc. she creates a heat map that clearly shows the underserved areas, where less posts were found. As assumed, most of them are found in the inner city and have a high density. Surpringsingly, also in the North of Munich people tend to post less about activities in green spaces.
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Scientist such as Geographers, Urbanists, Athropologists and Sociologists study patterns of human behaviour within the city. They are f. ex. interested in how certain settlement structures influence the life within them or how the distance to the city center affects the functionality of urban areas. They also try to learn where and why problems concentrate in certrain areas or how ethnic groups are distributed within the city. Our tool presents a key to a new layer of the city that has not been made accessible before. Through searching for concrete hashtags and discovering their distribution in heat maps human activities can be tracked on a district level. By switching from the absolute amount of posts with a certain hashtags, to its relative amount it is not just possible to identify concentrations but also the relevance of a theme for the area. Furthermore, by filtering several hashtags at the same time, much more complex topics can be described, searched and depicted.
The Urban Researcher
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The urban researcher searches for several hashtags in relation to green spaces. When observing the relative heatmap, she discovers that some „Gründerzeit“ quarters such as Westend and Ludwigvorstadt as well as some northern districts such as „Am Hart“ and „Harthof“, have less posts within those categories.
In the following example we will show how a potential marketer, working for the municipality of Munich, tries to find unique traits of Neuperlach by using the tool. While clicking through the interface he discovers the high importance of the rapculture for the district, which might have otherwise stayed underrepresented in conventional civic participation processes.
Cities and Villages are in constant competition for companies, new residents, tourists and students. Thus, building a recognizable „place brand“ has become an important task for municipalities. When certain areas suffer from a negative image, place brand development can also be financially supported by the state (f.ex. with the German Aid Program Städtebauförderung).
The Branding Agency
The first step to building an authentic place brand is to identify the key qualities that distinguish the place from others2. Also, the brand ideally represents the existing identities and shall not be artificially created from a top-down perspective. This is why civic participation with residents is often used in these brand building processes11. Nevertheless, many place brands fail to create a unique name and often sound generic.
The tool we created can help municipalties and branding agencies to identify unique and appreciated qualities through sourcing posts of thousands, if not millions of users, that post content in relation to the place. Thus, a much bigger group of people can be reached in comparison to a civic participation process alone. Also our tool offers non-negotiable statistics, which numerically proof the importance (by amount of posts) or uniqueness (by comparing its relevance in other districts) of certain topics.
11Fasselt, Jan und Ralf Zimmer-Hegmann (2014): Ein neues Image für benachteiligte Quartiere: Neighbourhood Branding als wirksamer Ansatz? In: O. Schnur (Hrsg.): Quartiersforschung. p. 267-291.
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By clicking on the chosen district the markteter discovers the different common stories, consistent of hashtags often mentioned together. Some of them, such as the clusters called #npl and #pep contain especially many unique hashtags that invite the user to browse for further discoveries ...
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The marketer clicked on the #npl-story-cluster and discovered that it features many unique hashtags around the topic of music such as #hiphop, #deutschrap, #ghetto and #rap.
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Because the marketer got curious about the supposed rap-culture, he compares the hashtag to the rest of the city. Thats where he discovers that Neuperlach is the ultimate hot spot for it. As a result he proposes the district officials to promote the music-scene in Neuperlach.
While we got some helpful results during our research, we need to keep in mind that data from instagram is heavily biased. The majority of the users of the platform are young and tech-savvy, so many people are not represented. Also, there is a “positivity-bias” inherent to the platform, meaning that people tend to talk about the things that are appreciated about a place, but probably won’t talk about its problems and deficiencies.
Reflection and Outlook
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Also, we analyzed an unequal amount of posts for every district, the reasons being partly that some districts are perceived as more “instagrammable” than others. Which we were also interested in, as it tells us something about their popularity. Nevertheless, we probably lost interesting data through our filtering process (described in the technical chapter) which is related to some names of districts like “Altstadt” or “Westend” being ambiguous. For our analysis we decided to look at the city as a set of distinct districts. This only partly reflects how the city is perceived though. While some districts might have clear boundaries, the boundaries of others might not even be noticed at all by a visitor. While some of the stories that we found might be unique to a specific district, others might stretch over multiple districts or over the whole city, which we did not take into consideration.
If provided with a set of hashtags, that could either be districts from a different city, or different villages in a rural area, or even different cities in a country for example, our tool could very quickly be adapted to any other desired place. One would only need to download the related posts and provide a map of the areas corresponding to the selected hashtags, then the processing of the data could be run automatically. This has great potential for all the described use cases.
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Our tool could be improved by collecting data from other social media platforms like flickr or twitter additionally to Instagram.
For further research the tool shall be validated by combining the analysis of Instagram posts with field research, meaning conducting interviews with inhabitants of a place to see if our results align with their perceptions. This would mitigate the fact that data from Instagram is biased, as different social groups could be included intentionally.
to source precise sets of knowledge and methodological approaches from architecture, urban planning and computer science. Thus we were able to stay on track regarding the technical challenges and distributed the needed work load in the most sufficient way. Nevertheless, decisions within the project - whether they were graphical or technical - were always taken in consultation with the whole group.
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The most helpful tool to make the most of an interdisciplinary approach was collecting, sharing and reorganizing graphically described knowledge and ideas for the prototype within the digital platform Miro. Working with this type of „digital diary“, we were able to get back to previous ideas and merge them with new approaches developed in the process. And of course we benefited as well from the great inspirations and tips given by our project mentors.
WorkingCollaborationasaninterdisciplinaryteamwewereable
A project idea, that all three of us shared from the beginning was the desire to unravel a subjective layer of the city, that cannot yet be found in the tools available. With the urban story teller we succeded to discover new knowledge on our project area and hope to have raised more curiosity for studying the city through social media by browsing and exploring the city within our tool.
„Work in Progress“ - Extract from Miro Board
38 ContactLéonieHubertMatrikel0671024thM.A.Architectureleonie.hubert35@gmail.com+33695031303 Sophia +49sophiaknapp@tum.de6MatrikelKnapp067102thSem.B.Sc.Informatics17650078544 Elina 2MatrikelVolz03753495ndSem.M.Sc.Urban Studies +49elinavolz@yahoo.de17630487329
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I really enjoy the alp views from my 16th floor.
