Han Liang Lin , Li Ting Chung (p)

Urbanization results in higher building density, and less amount of permeable pavement in urban area that increase urban runoff and cause flood disasters. Besides, the increasing emission of carbon dioxide by urban traffics also leads to urban heat-island (UHI) effect. The losing resilience of floods and heat of cities could be a consequence of natural-built environment relationship degradation. Green strategies of green urbanism focuses on healing the relationship by more green plants, more green buildings, and green infrastructure. The paper aims a quantitative framework of green city strategies assessment on the capacity of flood and heat island resilience that the scale and the space are explicitly specified.
To this end, Kaohsiung is selected for the empirical study because of frequent flood disasters in recent years and UHI effect of 4 °C higher than its neighborhood cities in average. The assessment of flood resilience capacity is based on a quasi-two-dimensional diffuse flow simulation model to obtain the information of flood area, flood water volumes and flood hydrograph, with and without green strategies. Regarding to UHI effect, this study quantifies green intensity index and main factors of urban heat island in Kaohsiung to confirm a regression graph. Finally, methods of point pattern analysis, such as spatial autocorrelation/kernel density analysis, will be evaluated to reveal the spatial patterns of flood and heat island resilience to make the spatial perspective of scale and location easy to be understood by visualization.

Artem Korzhenevych (p), Manisha Jain

This paper uses concepts and measures from the central place theory to describe and analyze the urban settlement hierarchy in the northwestern part of India including mega-cities Delhi and Mumbai. A particular focus is on the functional importance of settlements based on the amenities provision. This approach is contrasted with the population-centered approach currently used in regional planning in India. GIS-based analysis is used to visualize the development gaps. In addition, this paper investigates the factors leading to population growth in different-size settlements. The empirical findings are used to infer on the potential effects of a large investment project – the Delhi-Mumbai Industrial Corridor – on different-size settlements.
The empirical analysis reveals deficits in amenity provision in particular in large and medium-size urban settlements. In contrast, the majority of smaller urban centers are currently well-equipped to sustain additional population growth. With respect to dynamics over time, urban centers in more backward states in the study area displayed stronger improvements in the amenities provision.
The important determinants of urban growth identified in this research are the proximity to large urban centers, population density, employment structure, as well as natural amenities. The findings confirm that spatial disparities might be exacerbated by large investment projects in the absence of regional planning. The paper argues for a strong institutional structure and advocates for place-based policies in order to facilitate implementation of large-scale cross-border infrastructure projects and to harness the potential of smaller urban centers. The policy recommendations could be of direct relevance for India and other countries of the Global South.

Dorota Kamrowska-Załuska (p), Hanna Obracht-prondzynska (p), Karima Kourtit, Peter Nijkamp

Emerging disruptive technologies such as AI, advanced machine learning, IoT, real-time data-based adaptive systems, virtual and augmented reality, particularly if embedded in urban space change the way it is perceived by users. A public realm full of wireless devices and sensors allow citizens not only also to be connected but to interact with the urban space or even to co-create it. It also allows researchers, planners and decision makers to conduct new types of analysis, which can further enhance evidence-based policy making, since Internet users by posting their opinions provide information allowing to assess how changes connected with smart cities are perceived by its dwellers. This paper is aiming to:
●introduce Big Data based-tools for assessing how strong (if at all) is the correlation between implementing smart strategies and solutions and the achievement of the high quality of urban space taking into consideration both urban morphology and quality of life;
●test the above mentioned tools on the basis of selected case studies in order to seek an answer to the question whether implementation of smart strategies and solutions can change the way how users perceive urban space.
By using multiple data sources at various levels in the open information urban systems, both user-generated content (“social” sensors - information generated by online activity) and governmental or public data (both open and confidential micro-data), a testbed based on selected cases is conducted. The proposed study consists of phases resulting from our elaborated theoretical framework, where within each:
●case studies are chosen based on a selected framework measuring smartness. Additionally, for selected cities (Gdańsk&Stockholm) implementing both smart strategies and solutions, data based models for assessing quality and users’ perception of smart spaces, are designed.
●empirical studies on selected cities are conducted. In this part data sources are used to provide tools designed in relation a priori chosen frameworks. In this phase, based on data mining methods, the quality of urban space is measured based on administrative and open data, while the perception of urban space is based on social media sources.
●a matrix analysis based on studied cases, aiming at finding a correlation between quality of urban spaces of cities and their index of smartness, is introduced.
This study aims of a verification of hypotheses and definitions in both conclusions and recommendations related to the role of Big Data in assessing the perception and implementation of smart cities.

Adam Ploszaj (p)

Public investment in human capital is perceived as a tool that gives an opportunity to overcome development barriers in lagging regions. Interventions of this type are very popular in many countries. In the European Union, the European Social Fund is the main instrument for investment in human capital. According to EU regulations, interventions financed from the European Social Fund funds are subject to extensive evaluation processes at different levels (single project, regional/national programmes, and European comprehensive evaluation studies). Quite often, regions—or smaller spatial entities—are considered as a unit of analysis, assuming that the comparison of spatial units that have been exposed to different levels of support can allow identification of the causal effect. Leaving aside a fundamental discussion on the possibility of identifying the causal effect outside the experimental framework, in this research, we argue that the conclusions of evaluation exercises significantly depend on the method employed to measure the European Social Fund expenditure at the local level.
The empirical part of the paper is based on the analysis of the European Social Fund expenditure at the local level (LAU1/LAU2) in Poland in 2007-2013. We compare three disaggregation methods: (1) the official disaggregation used by the Polish government; (2) population-adjusted disaggregation; (3) and a novel and unique disaggregation based on high-resolution address data (postal codes) of 8.78 million participants of projects funded by the European Social Fund in Poland in 2007-2013. The official spatial disaggregation of the European Social Fund in Poland is based on a simple principle of proportionality: voivodships have proportional shares within the project (voivodship share = project divided by the number of provinces); poviats have proportional shares within their voivodship (share of the poviat = share of the voivodship divided by the number of poviats within a given voivodship); municipalities have proportional shares within their poviat (share of the commune = share of the poviat divided by the number of communes within a given poviat). We argue that our method (3) provide the best estimation of the real spatial distribution of expenditures, and the official method (1) is significantly biased. Moreover, we provide evidence that the population-adjusted method (2) is a better approximation of real expenditures than the official method (1). To assess the influence of disaggregation method on evaluation studies we compare regression models with independent variables measured by the three discussed disaggregation methods.