Living

Digital tools are reshaping emergency response in Lemgo, where a location tracking solution uses real-time maps of incidents and team positions to make coordination easier, more reliable, and transferable to other regions.

Key takeaways

• Start with real user needs: The project originated from an on-the-ground problem identified by local emergency teams.
• Keep it simple: Minimal, easy-to-use features ensure adoption by volunteer organizations with limited IT expertise.
• Collaboration is key: Success depended on cooperation between city administration, German Red Cross and local startups.
• Use open-source for scalability: Making the tool free and open enables replication and adaptation in other regions at low cost.
• Iterative testing matters: Real-life pilots (e.g. Christmas market) were essential for refinement and validation.
• Small improvements can save lives: Cutting 30 seconds from emergency response time demonstrated measurable and impactful success.

In Lemgo, Germany, digital innovation is changing how emergency services operate. A smart location tracking system now visualises incidents and team positions in real time, enabling faster, more reliable coordination. As Nicole Baeumer from the Smart City project digital.interkommunal Kalletal.Lemgo shares, what began as a simple, local idea has evolved into a scalable open-source solution that shows how digital tools can make a measurable difference – even in seconds.

Lemgo and its neighbouring municipality Kalletal in North Rhine-Westphalia are part of the German federal funding program Model Projects Smart Cities (MPSC), which runs from 2021 to 2027. Their overall Smart City strategy focuses on five main areas: health, data-infrastructure-innovation, food, campus-quarter-living, and environment. Within this framework, more than twenty subprojects have been developed, including Smarte Einsatzkrõfteortung - digital.interkommunal, aimed at improving public health and safety..

During major local events, the German Red Cross provides on-site medical services. Until recently, coordination between the control centre and first aid patrols relied on verbal radio messages – a process often slow and prone to error. On average, it took about forty seconds to dispatch a team, a delay that could be critical in emergencies.

To overcome this challenge, the Smart City Team, the German Red Cross, and the local startup Impact IT collaborated on a digital solution. The result is a web-based application that uses smartphones in medical backpacks equipped with the OwnTrack app for live location tracking. The control centre can see where patrols are in real time, upload custom maps such as Christmas market layouts, check device battery status, and mark key sites like hospitals or defibrillators. Simple and intuitive by design, the tool is easy to use even for volunteers with limited technical experience.

First tested during Lemgo’s annual “Kläschen” Christmas Market in 2023 and 2024, the system proved remarkably effective: average response times dropped from forty seconds to just ten. A thirty seconds improvement may seem small, but in an emergency, it can be life-saving. Encouraged by positive feedback, the team has since refined and expanded the system to other events and organisations.

Open by design, the solution is freely available on platforms such as OpenCode and GitLab, complete with installation guides, user manuals, and adaptation instructions. Its open-source model ensures transparency and makes replication in other cities straightforward and affordable.

This event is part of the ISCN Global Mixer, a series of events organized by the International Smart Cities Network. The presentations cover a wide range of topics related to international smart city approaches and provide exciting insights into urban digitalization worldwide - in just 30 minutes.

BerlinTXL, the transformation of a former airport into a pioneering sustainble and digitally-driven urban quarter remains one of Germany's few and exciting "greenfield developments" in the Smart City space. With this session we want to update the International Smart Cities Network on the progress since the last time BerlinTXL visited the "ISCN Global Mixer" as a speaker and thus offer a close-up to the rythm of this major and inspiring project.

Key takeaways

• Transforming an airport into a city of tomorrow: Berlin TXL turns the former Tegel site into a model for sustainable and innovative urban development.
• Building the Urban Tech Republic and Schumacher Quartier: A new hub for research, startups, and housing brings together technology, community, and livability.
• Driving innovation through data and design: Digital twins, open-source platforms, and green, climate-adaptive planning shape the district’s smart infrastructure.
• Creating real-world impact through collaboration: Over 40 companies and international partners are already contributing to Berlin TXL’s transformation.

This Global Mixer offered an inspiring look at the ongoing transformation of the former Berlin Tegel Airport into Berlin TXL – the city of tomorrow in the making. Building on a first part of the presentation from March 2024, Stefan Höffken showcases how one of Berlin’s most iconic sites is evolving into a multifunctional urban district where innovation, sustainability, and livability come together. 

The area will be home to the Urban Tech Republic – a hub for research, startups, and industry focused on future urban technologies – and the Schumacher Quartier, a residential neighborhood with over 6,000 housing units and space for more than 10,000 residents. The project is ambitious in scope, with a planning horizon of two decades.

The focus areas: 

• Promoting innovation in mobility, energy, water, and construction
• Creating a digital infrastructure with open-source platforms and digital twins to manage water, land, and buildings
• Building a data marketplace to enable secure sharing of urban data
• Integrating green spaces and climate-adaptive design

Over 40 startups and companies are already active on site, supported by international partnerships (e.g. with Mitsubishi and Incheon Startup Park).

The project serves as a model for future cities, combining sustainability, digitalization, and innovation in a real-world setting.

This event is part of the ISCN Global Mixer, a series of events organized by the International Smart Cities Network. The presentations cover a wide range of topics related to international smart city approaches and provide exciting insights into urban digitalization worldwide - in just 30 minutes.

In this episode of the ISCN Global Mixer we found out how the Data Powered Positive Deviance Method (DPPD) can be used for positive impact in our cities. Robin Nowok from the GIZ Data Lab presented a case that illustrates how safe public spaces for woman in Mexico City can be identified by combining rigorous statistics and qualitative research.

Key takeaways

• Rigorous statistical analysis: Data-powered positive deviance (DPPD) is a method to help detect and track down hitherto unseen good practices for common-good oriented development - and it’s also very much applicable to smart cities.
• First quant, then qual: DPPD informs statistically and in data-driven manner what aspects to investigate. The quantitative method thus informs where to target the often more resource-intensive qualitative investigations.
• Positively deviant factors for safe public spaces: In the described case from Mexico City, DPPD identified the location of spatial units, population density, no. of bars, no. of financial services and (negatively correlated) the distance to the nearest metro-station as factors relevant for women’s safety and to be further investigated.
• Again, data: The case from Mexico City made use of 60+ (mostly open) data banks, underscoring the importance and opportunities of consolidated provision of data in structured environments.

It’s one of the fundamental promises of smart cities: Leveraging data to detect patterns and translate them into adaptations and improvements for an effective and efficient common good-oriented urban development. 

But how to cut through all the noise that big data inadvertently produces as well, especially when applied to thicker problems of policy and public administration? In this edition of the ISCN Global Mixer we looked at the data-powered positive deviance (DPPD) method as one exciting approach for that.

The core idea is to detect and focalize statistical outliers that defy standard distributions – positive deviants. One of the approach’s classics is a project in Vietnam in 1990 where development economists used it to find individual practices that achieved significantly better results in child nutrition among communities facing similar challenges and restricted resources. The scaling of these successful behaviors achieved improvements in a largely closed system without depending on influx of external resources and technology.

Applied to an urban context, GIZ and its Data Lab cooperated with the United Nations Development Programme (UNDP), the University of Manchester, Codeando México and Cohesión Comunitaria e Innovación Social A.C. to identify safe(r) public spaces for women in Mexico City.

In the research design the number of crimes against women in public spaces was the dependent variable set against 25 independent variables (e.g. geography, infrastructure, demographics). The unit of analysis was AGEB, a geostatistical unit in Mexico. For better comparability among AGEBs, they were grouped homogenously. This returned prediction models for expected crime rates in AGEBs and their public spaces against which positive deviants could be detected. A subsequent round of validation checked qualitatively if results are plausible. For example, in single positive deviants there was a large number of closed housing units or a large footprint of a military college, naturally reducing crime rates in public spaces there. The remaining positively deviant AGEBs were targeted for further qualitative analysis and in-depth field studies. As a result of this DPPD process a recommendation catalogue could be formulated including the proposal of physical interventions in public space, such as removing abandoned vehicles in public area, or promoting diverse foot traffic including children, families, women and elderly.

How could DPPD be applied in your city and community to render your city smarter in a common-good oriented way? Join the discussion and feel free to reach out to us via iscn@giz.de 

For more details on the presented case, you can watch the recording of the keynote above and refer to the links below. 

Moreover, there is a short technical Q&A specifically on the conducted statistical analyses and research designs at the end of this page.
 

Further links:

Paper: Data-powered positive deviance: Combining traditional and non-traditional data to identify and characterize development-related outperformers

GIZ Data Lab Blog: What happened and what’s next for DPPD

GIZ Data Lab Blog: Identifying safe(r) public spaces for women in Mexico City

Podcast-Episode: Data-Powered Positive Deviance

DPPD Handbook

Technical Q&A on research design

1.  How was the homogeneous grouping done? Cluster analysis? ANOVA?

The homogeneous grouping was executed using cluster analysis, which was anchored on three pivotal variables: socio-economic level, daily incoming trips, and population density. This method allowed for the identification of natural groupings within the data, enhancing the interpretability and actionable insights derived from the analysis.

2. Why was homogeneous grouping chosen instead of normalization, e.g., crime against women per area per women population?

Clustering and normalization serve different purposes. Clustering enables the control of multiple factors and grouping similar observations whereas normalization ensures values are on the same scale, enhancing model performance. We normalize our variables before the grouping for numeric stability. In the Mexico use case we wanted to control for three variables: socio-economic level, daily incoming trips and population density. This clustering can reveal the underlying structure and groupings within the data, which can take into account unobserved heterogeneity that we didn't control for in the set of clustering variables. Homogeneous grouping allows us to control for multiple dimensions of similarity across different areas, beyond just a single aspect such as crimes against women. This multi-faceted approach provides a more comprehensive understanding of the data, capturing nuances that single-factor normalization might miss.

3. Which statistical model was used? Was machine learning considered?

For the predictions we used three models: First, we ran a multiple linear regression. Then, we did a LASSO linear regression to perform variable selection to enhance the prediction accuracy and interpretability of the statistical model. Finally, we did a negative binomial regression because this type of regression is designed to fit models in which the performance measure consists of counts with overdispersion. We did the three-regression analysis for each one of the four clusters of the homogenous grouping per category of crime severity and for all crimes, so we have several results depending on the type of regression, cluster, and crime severity. 

Year of publication: 2024

Urban mining is a circular economy approach that aims for systematically reusing waste as a resource, instead of disposing it. In this ISCN Global Mixer, Tanya Tsui from the MIT Senseable Amsterdam Lab illustrates how AI can be used to close material cycles in cities and invites discussion which potential applications the approach offers.

Key takeaways

• From circular to linear: Urban Mining can shift cities’ role from sole consumers of the world’s extracted materials to providers of it as well.
• From nominal to real values: Tanya Tsui’s research approach aspires to train algorithmic models in order to more closely estimate real values of the prevalence of materials in buildings over their lifetime, rather than nominal values emerging at demolition.
• Who wants to compete with the Netherlands? While data availability in the Netherlands for building materials is already quite strong and in high spatial resolution, the presented research project would be interested to know and source from other regions as well and thus widen the scope.

Global materials demand is estimated to more than double from 79 billion tons to 167 billion tons till 2060 (OECD, 2019) and global urbanization is a main driver. At the same time, the immense negative environmental and socioeconomic consequences of virgin mining and material extraction render a shift to approaches of circular economy more and more urgent. Cities can thereby assume not only the role of absorbers of material demand, but also as providers of it – some of the biggest mines in the world are arguably big metropolises!

Against this background, Tanya Tsui, Postdoc researcher at the MIT Senseable City Lab in Amsterdam presented her project on facilitating urban mining with Big Data and AI models.

She set the scene with referencing three examples of AI-supported urban data science: 

• With “Treepedia”, streetview images are algorithmically analysed to estimate green coverage in streets and urban quarters.
• “Skyview” uses streetview images facing the sky from among buildings, to estimate radiation on areas and thus provide proxies for urban heat islands.
• In order to assess the prevalence of secondary building materials, the approach has been so far to work with “archetypes” of buildings to be found in the city. Their extrapolation on the whole building stock returns an estimate of volumes to be expected. 

However, so far these values are nominal and not yet "real". And it estimates largely waste from demolition of buildings but is not granular enough to catch material flows from renovations and the entire lifecycle of buildings. 
For this next step, Tanya tries to link building data and (disaggregated) waste data that exist in cities, always with the necessity of three core data points: material type, amount and location. 

In the Netherlands, building data are organized and provided in already relatively good spatial resolution through the Pdok data base and Register of Buildings and Addresses (BAG). Waste data on the other hand is more difficult to constitute and access. Its tracking usually begins only beyond certain thresholds and often doesn’t entail exact typological and locational breakdowns. In her quest for suitable sources and stakeholders she is thinking about demolition contractors, building material exchange platforms, building or environmental permits but is very open for other suggestions as well.

In the lively discussion after her keynote, Tanya reflected on several questions. One was why she prioritizes building material for urban mining rather than e.g. household wares (refrigerators, washing machines, etc.). To her, this focus is warranted by the sheer scale of the building sector and its climate impact, but also because data is much more accessible for it. Asked about her challenges on an operational level when contacting stakeholders, she stressed that getting relevant and accurate data remains hard to get by. But in order to motivate data provision she also underlined that insights could be properly translated into better allocation and matching of material needs. Especially if the real-time analysis reached down to component levels, quicker and precise market clearance would be a big step towards a circular economy. 

She thus subsumed her input with a call for action to the audience as she is particularly interested in additional data sources for urban building and waste data from Germany and beyond. 

If a cooperation might be of interest to you, you can reach out directly to her or via the International Smart Cities Network at iscn@giz.de 

Further links:

https://senseable.mit.edu/

Definition: XPlanung is a standardized data format for exchanging plans across planning levels and between/to stakeholders

Legal framework: Developments of XPlanung are embedded within the legal frameworks of German Online Access Law (Onlinezugangsgesetz, OZG) and E-Government Law (E-Government-Gesetz)

Two core benefits: Enabling further civic participation and interaction on planning projects; boosting local economy as assessments for developments and investments are streamlined

Next steps to come: Leveraging it as the foundation of digital building permits and as replacement of legally binding documents; integrating one portal for planning data in Germany which links XPlanung, XBau and BIM

The challenge is to change a centuries-old common data standard: Paper. This is how Ken Gericke from the Federal Ministry for Digital and Transport primed the audience in this ISCN Global Mixer on the undertaking of setting up XPlanung, a standardized digital data format for exchanging plans across planning levels and between stakeholders.

The result today is an XML-based data exchange format based on Geography Markup Language Version 3 (GML 3.2.2), the extensible standard for spatial data developed by the Open Geospatial Consortium (OGC) and ISO TC211. (See also central office for XPlanung at xleitstelle.de) 

The two core benefits of having XPlanung and accordingly adjusted Geoportals and digitally visualized presentations of planning maps: First, it enables further civic participation and interaction on planning projects, as access in digital form is broader. Second, it boosts the local economy as the assessments of potential developments and investments are more streamlined and can be cleared more swiftly. An illustrative example of that is the Gigafactory of Tesla, a big carmaker, that was built in Grünheide, Germany. It is said that the standardized digital representation of the planning map in the area in a Geoportal facilitated the assessments of the project by corporate decisionmakers.

But addressing common misconceptions, Ken Gericke also stressed what XPlanung is not (yet). It is not a software application, nor a replacement or alteration of building codes. It is also not a replacement of legally binding planning documents in Germany. 

Nonetheless, the next ambitions are to link XPlanung, XBau (a machine-readable communication standard for construction oversight) and BIM applications into one portal for planning data in Germany.

Have a look for yourself in the recording of the presentation above.

Year of publication: 2024

The transformation and redevelopment of the former Berlin Tegel airport into the "Urban Tech Republic" and other districts is perhaps the closest one can get to a smart greenfield development in Germany.
In this ISCN Global Mixer, Stefan Höffken, the head of the project, gives an overview on their exciting plans and shares experiences on specific use cases they are developing.

Public money, public code: Berlin TXL is developing many interesting software solutions and provides the code in open license for replication.

The right blend: There is much software development and hardware use, but a crucial complementary to that are the "Entwicklungspartnerschaften" [development partnerships] managed through a moderated stakeholder process and carrying approaches to their full use.

AI enhancing use cases: For several use cases at Berlin TXL, artificial intelligence is a key enabler or crucial additional feature. Monitoring with drones, for example, can be done comprehensively and yet remain compliant with privacy concerns.

Be water, my friend: Water has many more utilities than we usually think of. It is not only part of sanitation and nutrition, but also a key factor and medium for cooling, energy management or damage control. This warrants particular emphasis on water management for smart developments.

The iconic hexagonal terminals of the former Berlin Tegel airport are testimony to past innovation, futuristic ambition and smart spatial planning. Now they are standing a bit isolated in an almost empty area of about 500ha. But not for long, as they witness with Berlin TXL a new big transformational smart district development comprising the “Urban Tech Republic” and the Schumacher Quartier and Cité Pasteur as new modern residential estates.

Stefan Höffken, urban planner with digital focus and head of the project, gave us a short overview on Berlin TXL’s overall plans and shared some experiences on the specific use cases they are developing in the project.

Their approach for smart district development has been to initially set up core digital infrastructures, such as a Data Platform or a 3D modelling of the area. To this are linked and further developed several use cases employing both newly developed software and hardware applications, e.g. LoRaWan sensors or drones. All is bound together by so-called “Entwicklungspartnerschaften” [development partnerships] with various actors and organizations from Berlin and steered through moderated stakeholder processes. 

An illustration thereof is the “Smart nature” monitoring of vegetation and identification of plant species in the area. Through drone flights a comprehensive recording of the area is done regularly and AI enables a detailed modelling of heat structures and growth patterns which then informs action for biodiversity preservation and management of the aerial climate. All this is done in cooperation with Grünberlin, a state-owned company focusing on green infrastructure.

Other use cases Stefan mentioned were a software that automatically blurs and anonymizes through AI sensitive objects in aerial images taken by drones, a more detailed and integrated heat and energy metering, and prediction-based water management. Watch the above recording of the session for more information.

Stefan Höffken also highlighted that their project welcomes further cooperation and exchanges, also with international actors. If you want to reach out to the project, feel free to do so also via the International Smart Cities Network at iscn@giz.de.

• More information on Berlin TXL can be found here: https://urbantechrepublic.de/en/
• A documentation and code of the solution for privacy-compliant blurring of aerial images can be found here on Open CoDE: https://software.opencode.de/project/1197#readme [German]
• The Gitlab online repository including the employed data platform can be found here: https://gitlab.com/berlintxl/futr-hub/