Giuseppe Borruso

Network Density Estimation: A GIS Approach for Analysing Point Patterns in a Network Space

Human activities and more generally the phenomena related to human behaviour take place in a network-constrained subset of the geographical space. These phenomena can be expressed as locations having their positions configured by a road network, as address points with street numbers. Although these events are considered as points on a network, point pattern analysis and the techniques implemented in a GIS environment generally consider events as taking place in a uniform space, with distance expressed as Euclidean and over a homogeneous and isotropic space. Network-spatial analysis has developed as a research agenda where the attention is drawn towards point pattern analytical techniques applied to a space constrained by a road network. Little attention has been put on first order properties of a point pattern (i.e. density) in a network space, while mainly second order analysis such as nearest neighbour and K -functions have been implemented for network configurations of the geographical space. In this article, a method for examining clusters of human-related events on a network, called Network Density Estimation (NDE), is implemented using spatial statistical tools and GIS packages. The method is presented and compared to conventional first order spatial analytical techniques such as Kernel Density Estimation (KDE). Network Density Estimation is tested using the locations of a sample of central, urban activities associated with bank and insurance company branches in the central areas of two midsize European cities, Trieste (Italy) and Swindon (UK).

Geocomputation and Urban Planning

Sixteen years ago, Franklin estimated that about 80% of data contain geo-referenced information. To date, the availability of geographic data and information is growing, together with the capacity of users to operate with IT tools and instruments. Spatial data infrastructures are growing and allow a wide number of users to rely on them. This growth has not been fully coupled to an increase of knowledge to support spatial decisions. Spatial analytical techniques, geographical analysis and modelling methods are therefore required to analyse data and to facilitate the decision process at all levels. Old geographical issues can find an answer thanks to new methods and instruments, while new issues are developing, challenging researchers towards new solutions. This volume aims to contribute to the development of new techniques and methods to improve the process of knowledge acquisition. The Geocomputational expression is related to the development and the application of new theories, methods and tools in order to provide better solutions to complex geographical problems. The geocomputational analysis discussed in this volume, could be classified according to three main domains of applications; the first one related to spatial decision support system and to spatial uncertainty, the second connected to artificial intelligence, the third based on all spatial statistics techniques.

Cities and Smartness: A Critical Analysis of Opportunities and Risks

The term “Smart City” is to-date widely used, but little clarity appears in the definition behind it. Several approaches led to a growing emphasis on the combined use of geographic information and communication technology to build cognitive frameworks in city planning and management. The present paper tackles an effort to define ‘smart cities’ and to identify both elements of smartness, and critical aspects related to the current interpretation of the term. In particular, the risk of considering the technological layer of Smart City as an innovative element has been observed, highlighting, on the contrary, the need to consider Smart Cities in terms of a major urban planning effort to coordinate and harmonize different urban players, sustained by ICT instruments.

Smart Cities in a Smart World

Very often the concept of smart city is strongly related to the flourishing of mobile applications, stressing the technological aspects and a top-down approach of high-tech centralized control systems capable of resolving all the urban issues, completely forgetting the essence of a city with its connected problems. The real challenge in future years will be a huge increase in the urban population and the changes this will produce in energy and resource consumption. It is fundamental to manage this phenomenon with clever approaches in order to guarantee a better management of resources and their sustainable access to present and future generations. This chapter develops some considerations on these aspects, trying to insert the technological issues within a framework closer to planning and with attention to the social impact.

Sustainable Development: Concepts and Methods for Its Application in Urban and Environmental Planning

The idea of sustainable development may appear quite vague, fuzzy and evasive (Pearce et al. 1989). In fact, whereas sustainability is related to a status of maintenance and conservation of the existing conditions, both in space and time and is referred to the capacity to guarantee a support without causing decay, the concept of development implies, instead, an alteration and a transformation of actual status, then a condition of instability.

A Tale of Two Cities: Density Analysis of CBD on Two Midsize Urban Areas in Northeastern Italy

The paper is focused on the observation of urban form and functions and is aimed at identifying a method for the cartographic definition and representation of CBD (Central Business District). The analysis is developed to explore the formation of centers of different order in the urban environment, starting from the locations of a selected set of human activities located in urban areas. An index of concentration of central activities is presented to allow the visualization of the functional urban environment by means of a density surface, therefore highlighting areas where central activities and functions concentrate. The paper is based on analyses related to spatial statistics in a GIS environment. We provide a short review of the literature on CBD research, briefly describe the kernel density estimation method, and propose how this can be used in order to test the index of concentration of activities and therefore delineating CBD, presenting evidence from two urban areas in Northeastern Italy (Trieste and Udine).

Density Analysis on Large Geographical Databases. Search for an Index of Centrality of Services at Urban Scale

Geographical databases are available to date containing detailed and georeferenced data on population, commercial activities, business, transport and services at urban level. Such data allow examining urban phenomena at very detailed scale but also require new methods for analysis, comprehension and visualization of the spatial phenomena. In this paper a density-based method for extracting spatial information from large geographical databases is examined and first results of its application at the urban scale are presented. Kernel Density Estimation is used as a density based technique to detect clusters in spatial data distributions. GIS and spatial analytical methods are examined to detect areas of high services’ supply in an urban environment. The analysis aims at identifying clusters of services in the urban environment and at verifying the correspondence between urban centres and high levels of service.

Geographic Information Analysis for Sustainable Development and Economic Planning: New Technologies

A GIS implementation of a model of systemic vulnerability assessment in urbanized areas exposed to combined risk of landslide and flood

Smart City or Smurfs City

Very often the concept of smart city is strongly related to the widespread of mobile applications, completely forgetting the essence of a city with its connected problems. The real challenge in future years will be the huge population migration from rural areas to cities. It is fundamental to manage this phenomenon with clever approaches in order to save money and environment. This paper develops some considerations on these aspects trying to lead the discussion in a correct direction.

Geocomputation, Sustainability and Environmental Planning

The experience developed by Ian McHarg represents the first attempt to base environmental planning on more objective methods. In particular, he supposed that the real world can be considered as a layer cake and each layer represents a sectoral analysis. This metaphor represents the fundamental of overlay mapping. At the beginning, these principles have been applied only by hand, just considering the degree of darkness, produced by layer transparency, as a negative impact. In the following years, this craftmade approach, has been adopted for data organization in Geographical Information Systems producing analyses with a high level of quality and rigour. Nowadays, great part of studies in environmental planning field have been developed using GIS. The next step relative to the simple use of geographic information in supporting environmental planning is the adoption of spatial simulation models, which can predict the evolution of phenomena. As the use of spatial information has definitely improved the quality of data sets on which basing decision-making process, the use of Geostatistics, spatial simulation and, more generally, geocomputation methods allows the possibility of basing the decision-making process on predicted future scenarios. It is very strange that a discipline such as planning which programs the territory for the future years in great part of cases is not based on simulation models. Sectoral analyses, often based on surveys, are not enough to highlight dynamics of an area. Better knowing urban and environmental changes occurred in the past, it is possible to provide better simulations to predict possible tendencies.The aim of this book is to provide an overview of the main methods and techniques adopted in the field of environmental geocomputation in order to produce a more sustainable development.