Impacts of spatiotemporal resolution and tiling on SLEUTH model calibration and forecasting for urban areas with unregulated growth patterns

Abstract. The damage triggered by different flood events costs the Italian economy millions of euros each year. This cost is likely to increase in the future due to climate variability and economic development. In order to avoid or reduce such significant financial losses, risk management requires tools which can provide a reliable estimate of potential flood impacts across the country. Flood loss functions are an internationally accepted method for estimating physical flood damage in urban areas. In this study, we derived a new flood loss function for Italian residential structures (FLF-IT), on the basis of empirical damage data collected from a recent flood event in the region of Emilia-Romagna. The function was developed based on a new Australian approach (FLFA), which represents the confidence limits that exist around the parameterized functional depth–damage relationship. After model calibration, the performance of the model was validated for the prediction of loss ratios and absolute damage values. It was also contrasted with an uncalibrated relative model with frequent usage in Europe. In this regard, a three-fold cross-validation procedure was carried out over the empirical sample to measure the range of uncertainty from the actual damage data. The predictive capability has also been studied for some sub-classes of water depth. The validation procedure shows that the newly derived function performs well (no bias and only 10 % mean absolute error), especially when the water depth is high. Results of these validation tests illustrate the importance of model calibration. The advantages of the FLF-IT model over other Italian models include calibration with empirical data, consideration of the epistemic uncertainty of data, and the ability to change parameters based on building practices across Italy.

Download Full-text

Coupling scenarios of urban growth and flood hazards along the Emilia-Romagna coast (Italy)

Natural Hazards and Earth System Science ◽

10.5194/nhess-15-2331-2015 ◽

2015 ◽

Vol 15 (10) ◽

pp. 2331-2346 ◽

Cited By ~ 21

Author(s):

I. Sekovski ◽

C. Armaroli ◽

L. Calabrese ◽

F. Mancini ◽

F. Stecchi ◽

...

Keyword(s):

Urban Growth ◽

Urban Areas ◽

Digital Terrain Model ◽

Flood Hazards ◽

Terrain Model ◽

Digital Terrain ◽

Sleuth Model ◽

Emilia Romagna Region ◽

The Cost ◽

Flood Prone Areas

Abstract. The extent of coastline urbanization reduces their resilience to flooding, especially in low-lying areas. The study site is the coastline of the Emilia-Romagna region (Italy), historically affected by marine storms and floods. The main aim of this study is to investigate the vulnerability of this coastal area to marine flooding by considering the dynamics of the forcing component (total water level) and the dynamics of the receptor (urban areas). This was done by comparing the output of the three flooding scenarios (10, 100 and > 100 year return periods) to the output of different scenarios of future urban growth up to 2050. Scenario-based marine flooding extents were derived by applying the Cost–Distance tool of ArcGIS® to a high-resolution digital terrain model. Three scenarios of urban growth (similar-to-historic, compact and sprawled) up to 2050 were estimated by applying the cellular automata-based SLEUTH model. The results show that if the urban growth progresses compactly, flood-prone areas will largely increase with respect to similar-to-historic and sprawled growth scenarios. Combining the two methodologies can be useful for identification of flood-prone areas that have a high potential for future urbanization, and is therefore crucial for coastal managers and planners.

Download Full-text

Natural Geography and Patterns of Local Population Growth and Decline in Spain: 1960–2011

Sustainability ◽

10.3390/su11184979 ◽

2019 ◽

Vol 11 (18) ◽

pp. 4979 ◽

Cited By ~ 1

Author(s):

Adelheid Holl

Keyword(s):

Population Growth ◽

Rural Areas ◽

Urban Areas ◽

Population Change ◽

Local Population ◽

Growth Patterns ◽

Detailed Data ◽

Precipitation Seasonality ◽

Growth Variation

This paper analyzes the role of natural geography for explaining local population change patterns. Using spatially detailed data for Spain from 1960 to 2011, the estimation results indicated that natural geography variables relate to about half of the population growth variation of rural areas and more than a third of the population growth variation of urban areas during this period. Local differences in climate, topography, and soil and rock formation as well as distance to aquifers and the coast contribute to variations in local population growth patterns. Although, over time, local population change became less related to differences in natural geography, natural geography is still significantly related to nearly a third of the variation in local population change in rural areas and the contribution of temperature range and precipitation seasonality has even increased. For urban areas, weather continues to matter too, with growth being higher in warmer places.

Download Full-text

Using building types and demographic data to improve our understanding and use of urban sprawl simulation

Proceedings of the ICA ◽

10.5194/ica-proc-2-28-2019 ◽

2019 ◽

Vol 2 ◽

pp. 1-8

Author(s):

Mojtaba Eslahi ◽

Rani El Meouche ◽

Anne Ruas

Keyword(s):

Cellular Automata ◽

Urban Growth ◽

Urban Areas ◽

Geographical Information Systems ◽

Dynamic Models ◽

Urban Expansion ◽

Demographic Data ◽

Population Data ◽

Geographical Information ◽

Sleuth Model

Abstract. Many studies, using various modeling approaches and simulation tools have been made in the field of urban growth. A multitude of models, with common or specific features, has been developed to reconstruct the spatial occupation and changes in land use. However, today most of urban growth techniques just use the historical geographic data such as urban, road and excluded maps to simulate the prospective urban maps. In this paper, adding buildings and population data as urban fabric factors, we define different urban growth simulation scenarios. Each simulation corresponds to policies that are more or less restrictive of space considering what these territories can accommodate as a type of building and as a global population.Among the urban growth modeling techniques, dynamic models, those based on Cellular Automata (CA) are the most common for their applications in urban areas. CA can be integrated with Geographical Information Systems (GIS) to have a high spatial resolution model with computational efficiency. The SLEUTH model is one of the cellular automata models, which match the dynamic simulation of urban expansion and could be adapted to morphological model of the urban configuration and fabric.Using the SLEUTH model, this paper provides different simulations that correspond to different land priorities and constraints. We used common data (such as topographic, buildings and demography data) to improve the realism of each simulation and their adequacy with the real world. The findings allow having different images of the city of tomorrow to choose and reflect on urban policies.

Download Full-text

Quantifying Rapid Urbanization and its impact on Urban Green Spaces: Directional and Zonal Analysis integrated with Landscape Expansion Index

10.22541/au.160671256.66476666/v1 ◽

2020 ◽

Author(s):

Eyasu markos woldesemayat

Keyword(s):

Urban Areas ◽

Temporal Dynamics ◽

Addis Ababa ◽

Growth Patterns ◽

Green Spaces ◽

Geographical Information ◽

Rapid Urbanization ◽

Urban Green ◽

Urban Green Spaces ◽

Urban Zone

Addis Ababa, the capital of Ethiopia, is urbanizing rapidly in recent years mainly through the destruction of environmental resources. This study aimed at the dynamics of urban green spaces (UGS). Remote Sensing and Geographical Information System (GIS) was used to extract land use and land cover data. The Landscape Expansion Index (LEI) was employed to measure urban growth patterns. The result showed that a more noticeable growth was observed in the peri-urban zone (40.1km2 to 176.1km2), followed by the inner urban zone (from 67.1km2 to 105km2). The expansion in the urban core zone was marginal and followed a non-unidirectional trend i.e. increased in the first period (1989-1999) and second period (1999-2009) by (0.11% and 4.2%), while decreased in the third period (2009-2019) by 3.6%. The result for LEI dynamics showed that the city experienced a pronounced outlying growth (98%) pattern, while edge expansion and infilling growth were insignificant. Conversely, the UGS declined in the inner urban zone by (18.03%), (28.61%) and (18.97%) in the first, second, and third periods. Similarly, in the peri-urban zone, the UGS persistently declined by (11.5%), (17.1%) and, (28.03%). The directional analysis showed that urban areas significantly expanded in SEE, SSE, SSW, and NEE with a net increase of 5.35, 4.4 km, 2.83, and 2.3 km2/year, respectively. Conventional large-scale /citywide/ dynamics investigations are not robust enough to represent the actual magnitude and directions of change, while the zonal and directional study is more effective in characterizing the Spatio-temporal dynamics for better urban planning towards.

Download Full-text

Analysis of urban development by means of multi-temporal fragmentation metrics from LULC data

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-7-w3-1411-2015 ◽

2015 ◽

Vol XL-7/W3 ◽

pp. 1411-1418 ◽

Cited By ~ 5

Author(s):

M. Sapena ◽

L. A. Ruiz

Keyword(s):

Urban Form ◽

Urban Areas ◽

Morphological Changes ◽

Landscape Fragmentation ◽

Growth Patterns ◽

Temporal Data ◽

Gis And Remote Sensing ◽

Multi Temporal ◽

Remote Sensing Techniques ◽

Selection Of

The monitoring and modelling of the evolution of urban areas is increasingly attracting the attention of land managers and administration. New data, tools and methods are being developed and made available for a better understanding of these dynamic areas. We study and analyse the concept of landscape fragmentation by means of GIS and remote sensing techniques, particularly focused on urban areas. Using LULC data obtained from the European Urban Atlas dataset developed by the local component of Copernicus Land Monitoring Services (scale 1:10,000), the urban fragmentation of the province of Rome is studied at 2006 and 2012. A selection of indices that are able to measure the land cover fragmentation level in the landscape are obtained employing a tool called IndiFrag, using as input data LULC data in vector format. In order to monitor the urban morphological changes and growth patterns, a new module with additional multi-temporal metrics has been developed for this purpose. These urban fragmentation and multi-temporal indices have been applied to the municipalities and districts of Rome, analysed and interpreted to characterise quantity, spatial distribution and structure of the urban change. This methodology is applicable to different regions, affording a dynamic quantification of urban spatial patterns and urban sprawl. The results show that urban form monitoring with multi-temporal data using these techniques highlights urbanization trends, having a great potential to quantify and model geographic development of metropolitan areas and to analyse its relationship with socioeconomic factors through the time.

Download Full-text

EXAMINING URBAN EXPANSION USING MULTI-TEMPORAL LANDSAT IMAGERY: A CASE STUDY OF THE MONTREAL CENSUS METROPOLITAN AREA FROM 1975 TO 2015, CANADA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b8-965-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 965-972

Author(s):

Lingfei Ma ◽

He Zhao ◽

Jonathan Li

Keyword(s):

Land Use ◽

Economic Development ◽

Metropolitan Area ◽

Urban Areas ◽

Landsat Imagery ◽

Urban Expansion ◽

Growth Patterns ◽

Urbanization Process ◽

Continuous Growth ◽

Census Metropolitan Area

Urban expansion, particularly the movement of residential and commercial land use to sub-urban areas in metropolitan areas, has been considered as a significant signal of regional economic development. In 1970s, the economic centre of Canada moved from Montreal to Toronto. Since some previous research have been focused on the urbanization process in Greater Toronto Area (GTA), it is significant to conduct research in its counterpart. This study evaluates urban expansion process in Montréal census metropolitan area (CMA), Canada, between 1975 and 2015 using satellite images and socio-economic data. Spatial and temporal dynamic information of urbanization process was quantified using Landsat imagery, supervised classification algorithms and the post-classification change detection technique. Accuracy of the Landsat-derived land use classification map ranged from 80% to 97%. The results indicated that continuous growth of built-up areas in the CMA over the study period resulted in a decrease in the area of cultivated land and vegetation. The results showed that urban areas expanded 442 km2 both along major river systems and lakeshores, as well as expanded from urban centres to surrounded areas. The analysis revealed that urban expansion has been largely driven by population growth and economic development. Consequently, the urban expansion maps produced in this research can assist decision-makers to promote sustainable urban development, and forecast potential changes in urbanization growth patterns.

Download Full-text

MODELLING URBAN GROWTH FOR BANGKOK AND ASSESSING LINKAGES WITH ROAD DENSITY AND SOCIO-ECONOMIC INDICATORS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w19-255-2019 ◽

2019 ◽

Vol XLII-4/W19 ◽

pp. 255-262

Author(s):

S. A. Kamarajugedda ◽

E. Y. M. Lo

Keyword(s):

Urban Growth ◽

Urban Areas ◽

Turning Point ◽

Urban Expansion ◽

Growth Patterns ◽

Economic Indicators ◽

Sustainable Urban Development ◽

City Development ◽

Road Density ◽

The Future

Abstract. The fastest urbanization is occurring in the Global South which includes many developing nations in Asia. However, a rapid and unplanned urban growth could threaten the sustainability of the process. A key step towards a sustainable urban development is to better understand interdependencies amongst urban growth patterns, infrastructure and socio-economic indicators. Here we chose Bangkok, Thailand as a megacity case study to assess the spatio-temporal urban growth dynamics and specifically its dependency with road density at intra-city scales. The SLEUTH urban growth model is further applied for predicting future expansion over the next decade and to assess the future intra-city expansion. Urban expansion patterns for Bangkok were generated for 1987 and 2017 using Landsat derived urban land-cover maps. Open Street Map (OSM) is used to generate a 2017 road density map. The urban expansion (1987–2017) was observed to follow a radially outward expanding pattern inland, with the logarithmic urban expansion rate having an inverted concave trend with road density. The rising/falling limbs then indicated an increase/decrease of urban expansion for which a road density “turning point” is readily identified and further used to develop a road density-based zoning map that highlights the different intra-city urban expansion rates. The SLEUTH predicted urban growth till year 2027 which also showed expansion outward from existing urban areas. The future expansion trend is also consistent with the turning point trend. This study showed that such spatial-temporal analysis of urban expansion coupled with SLEUTH can be useful for investigating likely outcomes of city development plans.

Download Full-text

A novel big data approach to measure and visualize urban accessibility

Computational Urban Science ◽

10.1007/s43762-021-00010-1 ◽

2021 ◽

Vol 1 (1) ◽

Author(s):

Yuqin Jiang ◽

Diansheng Guo ◽

Zhenlong Li ◽

Michael E. Hodgson

Keyword(s):

New York ◽

Big Data ◽

Urban Areas ◽

Public Transit ◽

Spatiotemporal Resolution ◽

Diurnal Variability ◽

Relative Index ◽

Transit Accessibility ◽

Urban Accessibility ◽

Bus Services

AbstractAccessibility is a topic of interest to multiple disciplines for a long time. In the last decade, the increasing availability of data may have exceeded the development of accessibility modeling approaches, resulting in a modeling gap. In part, this modeling gap may have resulted from the differences needed for single versus multimodal opportunities for access to services. With a focus on large volumes of transportation data, a new measurement approach, called Urban Accessibility Relative Index (UARI), was developed for the integration of multi-mode transportation big data, including taxi, bus, and subway, to quantify, visualize and understand the spatiotemporal patterns of accessibility in urban areas. Using New York City (NYC) as the case study, this paper applies the UARI to the NYC data at a 500-m spatial resolution and an hourly temporal resolution. These high spatiotemporal resolution UARI maps enable us to measure, visualize, and compare the variability of transportation service accessibility in NYC across space and time. Results demonstrate that subways have a higher impact on public transit accessibility than bus services. Also, the UARI is greatly affected by diurnal variability of public transit service.

Download Full-text