Martin Behnisch
Biographic Data
| ID | 6310985 |
|---|---|
| NAME | Martin Behnisch |
| GIVEN NAMES | Martin |
| FAMILY NAME | Behnisch |
| SIGNATURE | BEHNISCH M |
| AFFILIATIONS | Leibniz Institute of Ecological Urban and Regional Development |
| ORCID | 0000-0003-4590-6757 |
| VERIFIED | Yes |
| TOTAL WORKS | 15 |
| TOTAL CITATIONS | 7 |
| WORKS AS AUTHOR | 15 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2013 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 2 |
Urban green space percolation
Urban green spaces (UGS) are increasingly planned as connected networks, yet many connectivity assessments reduce cost variation to a static threshold and provide limited guidance on where to intervene. We develop a percolation-based framework for translating dynamic UGS connectivity into ecological management priorities. The framework constructs a least-cost, cost-weighted UGS network, activates edges in ascending cost-weighted distance to ident…
Understanding incremental densification – Determinants of residential infill on vacant lots
Approach allows to link landowners’ and developers’ interests to infill processes. • Spatial and regulatory prerequisites determine multi-family housing infill. • Multi-family housing infill is further depending on market rationalities. • Low-density housing infill is driven by individual landowners’ interests. • Most vacant lots remain undeveloped, despite the existence of building rights. Urban densification faces challenges due to the declinin…
Prediction of residential and non-residential building usage in Germany based on a novel nationwide reference data set
Building usage is an important variable in modelling the energetic, material and social properties of a building stock. Gathering this data on large geographical scale, and in the necessary temporal and spatial resolution, that means, on building level, is a challenging task. Machine Learning algorithms like Random Forest have proven useful in predicting building-related features in the past but often resort to training sets of limited geographic…
What drives densification and sprawl in cities? A spatially explicit assessment for Vienna, between 1984 and 2018
The spatial arrangement of settlements constitutes a long-lasting legacy and shapes the prospects for transformations toward sustainability. Thus, understanding the drivers of changes in settlement patterns is essential. In this article, we present a spatially explicit, geostatistical analysis of settlement dynamics, and a qualitative investigation of its regulative, demographic, and economic drivers, using the example of Vienna, Austria between …
Understanding land take by low-density residential areas
Using a case study design, the study focuses on identifying the institutional configuration that determines land take by low-density residential areas. Methodologically, it combines geospatial approaches and new institutionalism to capture and understand residential development, taking into account the interests, constraints and agency of local planning authorities, developers and households. The geospatial analysis shows that land take in the ca…
Commentary
Recent advances in data science and urban environmental health research utilise large-scale databases (100s-1000s of cities) to explore the complex interplay of urban characteristics such as city form and size, climate, mobility, exposure, and environmental health impacts. Cities are still hotspots of air pollution and noise, suffer urban heat island effects and lack of green space, which leads to disease and mortality burdens preventable with be…
Mapping soft densification
Infill development policies have been widely adopted as strategies to reduce urban sprawl and to promote sustainable urban transformation. However, little empirical data are available to analyse infill processes and to facilitate building activity on infill potentials. This is especially true for small-scale residential infill, which often takes place on vacant or underused lots as soft densification. To address this issue, a geospatial method is…
Geospatial Modeling Approaches to Historical Settlement and Landscape Analysis
Landscapes and human settlements evolve over long periods of time. Land change, as one of the drivers of the ecological crisis in the Anthropocene, therefore, needs to be studied with a long-term perspective. Over the past decades, a substantial body of research has accumulated in the field of land change science. The quantitative geospatial analysis of land change, however, still faces many challenges; be that methodological or data accessibilit…
Spatial Data Science
The field of data science has had a significant impact in both academia and industry, and with good reason [...]
Heavy-tailed distributions for building stock data
The question of inferring the owner of a set of building stocks (e.g. from which country the buildings are taken) from building-related quantities like number of buildings or types of building event histories necessitates the knowledge of their distributions in order to compare them. If the distribution function is a power law, then a version of the 80/20 rule can be applied to describe the variable. This distribution is an example of a heavy-tai…
Urban big data analytics and morphology
Multi-Indicator Approach for Characterising Urban Green Space Provision at City and City-District Level in Germany
This paper addresses the question of how a sustainable urban development can be supported through simple measured quantities in the context of the specific provision of green space and open space. The specific provision of green space is analysed based on a combination of six indicators that describe, on the one hand, the access of inhabitants to green spaces and on the other hand, the settlement character as well as the strong anthropogenic impr…
A Multifactorial GIS-Based Analytical Method to Determine the Quality of Urban Green Space and Water Bodies
The type, size, and spatial distribution of public green spaces and water bodies are key factors for the microclimatic quality of urban areas, particularly within densely built-up districts, where they influence the quality of life. The analytical method presented in this paper looks at entire cities in a GIS-based approach that identifies potential critical areas of the city (with regard to heat stress or the accessability of green spaces) and q…
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Using quadtree representations in building stock visualization and analysis
What drives densification and sprawl in cities? A spatially explicit assessment for Vienna, between 1984 and 2018
The spatial arrangement of settlements constitutes a long-lasting legacy and shapes the prospects for transformations toward sustainability. Thus, understanding the drivers of changes in settlement patterns is essential. In this article, we present a spatially explicit, geostatistical analysis of settlement dynamics, and a qualitative investigation of its regulative, demographic, and economic drivers, using the example of Vienna, Austria between …
Understanding land take by low-density residential areas
Using a case study design, the study focuses on identifying the institutional configuration that determines land take by low-density residential areas. Methodologically, it combines geospatial approaches and new institutionalism to capture and understand residential development, taking into account the interests, constraints and agency of local planning authorities, developers and households. The geospatial analysis shows that land take in the ca…
Mapping soft densification
Infill development policies have been widely adopted as strategies to reduce urban sprawl and to promote sustainable urban transformation. However, little empirical data are available to analyse infill processes and to facilitate building activity on infill potentials. This is especially true for small-scale residential infill, which often takes place on vacant or underused lots as soft densification. To address this issue, a geospatial method is…
Using quadtree representations in building stock visualization and analysis
A Multifactorial GIS-Based Analytical Method to Determine the Quality of Urban Green Space and Water Bodies
The type, size, and spatial distribution of public green spaces and water bodies are key factors for the microclimatic quality of urban areas, particularly within densely built-up districts, where they influence the quality of life. The analytical method presented in this paper looks at entire cities in a GIS-based approach that identifies potential critical areas of the city (with regard to heat stress or the accessability of green spaces) and q…
You are here
Heavy-tailed distributions for building stock data
The question of inferring the owner of a set of building stocks (e.g. from which country the buildings are taken) from building-related quantities like number of buildings or types of building event histories necessitates the knowledge of their distributions in order to compare them. If the distribution function is a power law, then a version of the 80/20 rule can be applied to describe the variable. This distribution is an example of a heavy-tai…
Urban big data analytics and morphology
Multi-Indicator Approach for Characterising Urban Green Space Provision at City and City-District Level in Germany
This paper addresses the question of how a sustainable urban development can be supported through simple measured quantities in the context of the specific provision of green space and open space. The specific provision of green space is analysed based on a combination of six indicators that describe, on the one hand, the access of inhabitants to green spaces and on the other hand, the settlement character as well as the strong anthropogenic impr…
Spatial Data Science
The field of data science has had a significant impact in both academia and industry, and with good reason [...]
Geospatial Modeling Approaches to Historical Settlement and Landscape Analysis
Landscapes and human settlements evolve over long periods of time. Land change, as one of the drivers of the ecological crisis in the Anthropocene, therefore, needs to be studied with a long-term perspective. Over the past decades, a substantial body of research has accumulated in the field of land change science. The quantitative geospatial analysis of land change, however, still faces many challenges; be that methodological or data accessibilit…
Mapping soft densification
Infill development policies have been widely adopted as strategies to reduce urban sprawl and to promote sustainable urban transformation. However, little empirical data are available to analyse infill processes and to facilitate building activity on infill potentials. This is especially true for small-scale residential infill, which often takes place on vacant or underused lots as soft densification. To address this issue, a geospatial method is…
Prediction of residential and non-residential building usage in Germany based on a novel nationwide reference data set
Building usage is an important variable in modelling the energetic, material and social properties of a building stock. Gathering this data on large geographical scale, and in the necessary temporal and spatial resolution, that means, on building level, is a challenging task. Machine Learning algorithms like Random Forest have proven useful in predicting building-related features in the past but often resort to training sets of limited geographic…
What drives densification and sprawl in cities? A spatially explicit assessment for Vienna, between 1984 and 2018
The spatial arrangement of settlements constitutes a long-lasting legacy and shapes the prospects for transformations toward sustainability. Thus, understanding the drivers of changes in settlement patterns is essential. In this article, we present a spatially explicit, geostatistical analysis of settlement dynamics, and a qualitative investigation of its regulative, demographic, and economic drivers, using the example of Vienna, Austria between …
Understanding land take by low-density residential areas
Using a case study design, the study focuses on identifying the institutional configuration that determines land take by low-density residential areas. Methodologically, it combines geospatial approaches and new institutionalism to capture and understand residential development, taking into account the interests, constraints and agency of local planning authorities, developers and households. The geospatial analysis shows that land take in the ca…
Commentary
Recent advances in data science and urban environmental health research utilise large-scale databases (100s-1000s of cities) to explore the complex interplay of urban characteristics such as city form and size, climate, mobility, exposure, and environmental health impacts. Cities are still hotspots of air pollution and noise, suffer urban heat island effects and lack of green space, which leads to disease and mortality burdens preventable with be…
Understanding incremental densification – Determinants of residential infill on vacant lots
Approach allows to link landowners’ and developers’ interests to infill processes. • Spatial and regulatory prerequisites determine multi-family housing infill. • Multi-family housing infill is further depending on market rationalities. • Low-density housing infill is driven by individual landowners’ interests. • Most vacant lots remain undeveloped, despite the existence of building rights. Urban densification faces challenges due to the declinin…
Urban green space percolation
Urban green spaces (UGS) are increasingly planned as connected networks, yet many connectivity assessments reduce cost variation to a static threshold and provide limited guidance on where to intervene. We develop a percolation-based framework for translating dynamic UGS connectivity into ecological management priorities. The framework constructs a least-cost, cost-weighted UGS network, activates edges in ascending cost-weighted distance to ident…
Geography (12 works) · Engineering (7 works) · Land Use and Ecosystem Services (7 works) · Civil engineering (6 works) · Computer Science (6 works) · Environmental planning (6 works) · Environmental Science (6 works) · Environmental resource management (5 works) · Urban planning (5 works) · Urbanization and City Planning (5 works)