Measuring Amsterdam: a participatory mapping tool for citizen empowerment

Measuring Amsterdam: A participatory mapping tool for citizen empowerment

Maarten Groen Citizen Data Lab

Amsterdam University of Applied Science Amsterdam, Netherlands

m.n.groen@hva.nl

Wouter Meys Citizen Data Lab

Amsterdam University of Applied Science Amsterdam, Netherlands

w.t.meys@hva.nl A participatory tool, called Measuring Amsterdam

,

is developed that empowers citizens to share information about their neighbourhood via their smartphone. Together with the citizens, variables are defined that are relevant to their living environment. We aim to engage the citizens in the entire process by first defining their needs and priorities and secondly letting the citizens collectively collect the data. By publishing this data online under the open data license people can see how the data is stored, processed and distributed making the system transparent. Furthermore, giving the data back as open data allows citizens to both organize themselves in citizen initiatives, where they can handle the local issues themselves, or present the data and visualisations as up-to-date information about their neighborhood to government officials and other local stakeholders and decision makers. The tool automatically creates a visualisation that presents in real-time what the data looks like by plotting it onto a geographical map, allowing citizens to explore their data in an interactive way. With the Measuring Amsterdam tool, two pilot studies have been done within two different Amsterdam neightbourhoods. These pilot studies showed that we could gather a large amount of measurements within a short period of time. However we encountered some issues with regards to the motivation of using such a tool over a longer period of time and the validity of the data.

Keywords: Citizen Empowerment, Participatory Mapping, Open Data, Neighbourhood, Visualisation, Smartphone

I. I NTRODUCTION

While we are standing at the start of the Internet of Things (IoT) revolution, more and more aspects of our daily lives in the public space are being monitored and measured via autonomous sensor systems. These systems are able to do relatively simple repetitive tasks such as measuring traffic density or air quality.

Although the IoT technology is improving at a rapid rate, the technology still has trouble with measuring more complicated contexts. Most of the usage of these technologies are aimed to add to the Smart City and try

i www.measuringamsterdam.nl

improve or streamline the living environments. But we seem to forget to ask input from one of the best

‘sensors’ available with regards to complicated contexts: people [1]. People hold valuable information about the current situation and problems in their own neighbourhood or city [3,4].

Citizens within urban environments can and will be more actively involved with their living environment, which will result in a change in the way we will use these urban environments. The participatory society tries to stimulate people in being actively involved in improving or maintaining the livability of their living environment [2]. There have been various ways in which decision makers and researchers try to retrieve this valuable information from the citizens. Besides the traditional ways such as interviews or surveys, more web based standards have been developed that allow citizens to, on a voluntary basis, help give input about their neighbourhood [5]. Many of these projects are started and designed from a top-down perspective, where the citizens are only asked to participate in performing the measurements while being ignored in the rest of the process [6].

While there are also initiatives such as the Smart citizens kit [7] or the Air Quality Egg that enable citizens to generate their own data via small sensor nodes, not many tools have be created that allow citizens to quickly and easily measure their surroundings themselves. With this in mind, we created the Measuring Amsterdam tool which is developed to empower citizens to share information about their neighborhood, via their smartphone. This paper will focus on this tool and two pilot studies that have been performed using the tool.

II. B ACKGROUND

Calling on the power of the crowd to participate in gathering data is not a new approach. Crowdsourcing tools has shown to be a very effective way to collect data [8]. There are certain fields in the crowdsourcing domain that focus specifically on gathering, geo- located, data on by using participatory mapping tools.

Multiple projects have been done in the fields of

Voluntary Geographic Information (VGI) [1] and Public

participatory GIS (PPGIS) [9] that try to receive

citizens’ input for urban planning [10], scientific studies [5] or problem notifications in a city environment. What many projects in these fields have in common is that 1) the participants are not involved in deciding what will be measured and 2) the retrieved data is only for professionals to act on, instead of also allowing the citizens to act on the data.

These issues feed into the problems and difficulties with activating and motivating a large variety of citizens to be actively involved in giving input about their neighbourhood via such participatory tools[11]. There is no concrete solution to this issue, but there are various aspects that can help with this problem. By giving the citizens a sense of ownership of the data they collect, next to the sense ownership and responsibility they have with the surroundings they live in, they can be more motivated to participate [12,13]. The sense of ownership can also be increased by incorporating the needs and priorities of the citizens from the start [15].

Furthermore, studies have shown that giving direct feedback, by for example creating visualisations of the data, can help with motivating users [14].

To make the process of what happens with any collected data as transparent as possible the data should be, when possible, published as open data. It has a positive influence on the trust that people have in a system [16]. In addition, open data can trigger citizen empowerment [17]. Since all data is free for anyone to use, it allows people to analyse, visualise, combine it with other data, draw conclusions and take action themselves.

III. T HE TOOL AND PILOT STUDIES

The tool, called Measuring Amsterdam, is developed to enable citizens to measure a predefined set of variables that are created in a collaboration between citizens and professionals. The tool consists of three parts, the input webform included in a web app, the data storage and distribution platform and the visualisation tool. The webform is created from the collaboratively defined variables. As shown in figure 1, measurements can be entered by participants by selecting values from dropdown menus or entering text in text fields. The web app works on any smartphone or tablet with a modern browser. By using the GPS location of the device, or by dragging a marker on a map, the location of the measurements can be attached. The data is stored in a NoSQL database and is being made available in the open standard format GeoJSON. Since all data has a geo-location attached we are able to visualise the data on maps in real-time.

Two pilot studies were performed. The first pilot study focused on testing the functional implementation of the tool. Based on known existing problems in one of the busiest streets in Amsterdam and input from researches in public spaces, variables were defined.

Examples of variables included: interaction with mobile technology usage in public spaces, interaction between people and CCTV camera annotation. During an

organized event a mix of 30, students, professionals and citizens performed measurements and observations within Amsterdam. In groups of two or three the participants were assigned to a specific area, so that there would be the least amount of overlap between measurements. The data was directly visualized

so that during the event participants could see all the measurements in real-time. Because all variables were predefined, we knew exactly what type of data to expect. This allowed us to, within the hour, create infographics based on the just collected data. These infographics were used as discussion starter about the measurements results.

During the second pilot, variables that applied to health were defined that indicated the health state of a neighborhood from the citizens’ perspective. The variables were the results from a local project to improve health, where citizens of a neighbourhood indicated what they perceive as a healthy neighbourhood. Variables were categorized into four groups that contained variables as shown in table 1.

Fig. 1. Screenshot of the webapp

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TABLE I. T

S

Catagory Variable

Children playing outside Children running outside Children playing a ball game Children at a playground

People smoking Smoking men

Smoking woman Smoking group of men Smoking group of woman People snacking People eating a sweet snack

People eating a savory snack People drinking a soda

Trash Full garbage bin

Dog poo

Garbage on the street

The second pilot took place during a festival to promote healthy living in a deprived neighborhood in Amsterdam called Slotermeer. Together with the citizens of this neighbourhood, we tested the participatory mapping tool and method. Citizens that were present at this locally organized festival were asked to participate and did not sign-up beforehand.

They were able to use the web-app via their own smartphone or walk together with a researcher if the participant did not own a smartphone. In the second case, the researcher would insert the measurements based on the input from the participant. As in the first pilot, all results were directly visualised and reported back to the participants. This was done by creating an interactive visualization showing all measurements on a map

. The web-app was improved by visualising the different variables using pictures, to also enable the citizens with low literacy skills to participate.

Both pilot studies worked with the same system. In which users had to go to web form (figure 1) which they could use to input their measurements. The measurements were stored on a database and were published in a GeoJSON format. With the collected data a visualisation was created that allowed users to browse the measured data in an interactive way.

iii www.measuringamsterdam.nl/overview_health

IV. R ESULT & D ISCUSSION

During the first pilot 1050 measurements were performed in a one hour period. Interesting discoveries were made regarding usability of the application. It showed that the application was at times confusing, and that the GPS tracking was not always accurate. In some cases we found errors with as much as 100 meters of deviation. Since we knew that the GPS results would not always be accurate in a city with many tall building interfering [18] we implemented a function that allowed participants to move a marker on the map to pinpoint their exact location if GPS results were not sufficient. In talks after the event participants mentioned that they assumed the GPS results would be good enough, and never used the option to move the marker themselves.

Furthermore, in the first pilot there were too many variables to keep track of. It was possible to measure 28 variables. Some variables were detected too frequently, which made it impossible to keep track of any other variables. The pilot showed us that certain usability aspects of the tool could be greatly improved.

Furthermore, reducing the total number of variables that could be measured would greatly benefit the overall clarity of the application.

During the second pilot, the variables were selected for a very specific domain (a healthy neighbourhood).

In addition, a specific group of participants (only citizens of the neighborhood) were involved in performing the measurements. During one afternoon, a total of 370 measurements were done by interested citizens. It showed to be difficult to motivate people to participate in the measurements. Even though we used the variables that they defined themselves. The biggest issue showed to be bringing the people over the initial threshold of participating in doing the measurements.

We have to still find ways to spark that initial engagement to participate in performing measurements.

The pilot studies indicated that once over this engaging threshold they find it fun to do and appreciate the transparency of what is done with the data and the visual feedback that is given by visualising the data immediately.

In both pilots the visualisations were a great way to showcase results and give an increased sense of ownership over the collected data. It also was a good discussion starter over what new information or patterns could be detected after analysing the visualisations.

There are still two major issues that have to be

overcome. One is the reliability of the data. Drawing

conclusion from the data is difficult, since you need

many people measuring the same thing to filter out any

false data [8]. Getting this amount of data is difficult to

do on a neighbourhood level, where the pool of

participants is scarce. This is especially the case if you

want to perform measurements over a longer period of

time. This bring us to the second issue: motivation. Our

pilots showed that it is difficult to motivate people to

participate on an ad-hoc basis for a longer period of

time. For an organized ‘measuring event’, where people

have signed up beforehand, it is relatively easy to motivate people to participate in the data measuring.

They are already over the first participation threshold after signing up for the event. On the other hand, measuring data consistently over a longer period of time will require the data to have a very high, visible impact to motivate people to keep performing measurements.

V. C ^ONCLUSIONS & F ^{UTURE WORK} While we cannot draw any definite conclusions after the pilots, we have gotten insights into which aspects of the developed tool should be improved and found interesting research areas to further explore in the future. Further improvements to the interface can be made in order to engage more citizens and motivate them to use the application for a longer period of time.

Furthermore the interface should be improved to make it more user friendly. For example, we encountered that a lot of users had issues with setting the location of the measurement on the map in the input form. This has to be improved to increasing the accuracy and reliability of the measurements. Furthermore, an easy to use form creation tool has to be developed that enables anyone to create their own measuring tool for their own neighbourhood. We have also gotten feedback from participants that they would like to see a way to add a picture to their measurements to be able to give more context.

Regarding future research there are various topics that need attention. The thing we are currently investigating is the motivational issues for using such a tool. We will investigate various ways of engaging and motivating people via for example gamification or adding social elements to the tool. Furthermore, research has to be done on how reliable the data is that is being collected, we want to know whether the data is reliable enough for municipalities or other public instances to base decision making on. Finally, we would like to know if such a tool can help citizens to organize themselves into a group and collaborate together in solving issues that surface after using the Measuring Amsterdam tool.

A CKNOWLEDGEMENT

We would like to thank the CitySDK project, which is partly funded by the European Commission and the Look! A healthy neighbourhood project which is funded by SIA-RAAK and the Urban Management program of the University of Applied Science Amsterdam. Finally we want to thank the Amsterdam Creative Industries Network.

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