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With an increasing urbanization trend over the last decades, urban agglomerations are facing different challenges that affect its inhabitants: pollution, traffic congestion, thriving population growth rates, and economic uncertainty. In the context of Latin America, where less than 20% of its inhabitants live in rural areas and with a projection to decrease to close to 10% by the year 2030, providing solutions to reduce the impact of this increase of population, on at least one of the issues, seems logical.

This study focuses on the urban logistics component to propose a classification method for homogeneous areas, using Factor Analysis (FA) and analysis of variance (ANOVA) as the main supporting tools. The proposed methodology builds up on the square kilometer (KM2) methodology developed by MIT Center for Transportation and Logistics, applying it in a neuralgic section of the downtown area of a mid-sized city in Latin America: Córdoba, Argentina. The selection was made considering the logistic restrictions, commercial density, and the relevance of the area for the city. Our proposed methodology uses relevant variables for urban logistics to perform the statistical analysis. The main goal is to develop a data-driven methodology to identify clusters to guide Córdoba's urban logistics policy and decision-making processes.

The results suggest a clear relationship between the different commercial activities and the location inside the area, splitting the area under study clearly into two main sections with similar overall characteristics and two subsections inside each one of them, which should be considered as a basis for further urban logistic analysis and implementation of specific best practices that fit the particular needs.

The purpose of this paper is to present a spatial decision support system (SDSS) to be used by the local authorities of a city in the planning and response phase of a disaster. The SDSS focuses on the management of public spaces as a resource to increase a vulnerable population’s accessibility to essential goods and services. Using a web-based platform, the SDSS would support data-driven decisions, especially for cases such as the COVID-19 pandemic which requires special care in quarantine situations (which imply walking access instead of by other means of transport).

This paper proposes a methodology to create a web-SDSS to manage public spaces in the planning and response phase of a disaster to increase the access to essential goods and services. Using a regular polygon grid, a city is partitioned into spatial units that aggregate spatial data from open and proprietary sources. The polygon grid is then used to compute accessibility, vulnerability and population density indicators using spatial analysis. Finally, a facility location problem is formulated and solved to provide decision-makers with an adaptive selection of public spaces given their indicators of choice.

The design and implementation of the methodology resulted in a granular representation of the city of Lima, Peru, in terms of population density, accessibility and vulnerability. Using these indicators, the SDSS was deployed as a web application that allowed decision-makers to explore different solutions to a facility location model within their districts, as well as visualizing the indicators computed for the hexagons that covered the district’s area. By performing tests with different local authorities, improvements were suggested to support a more general set of decisions and the key indicators to use in the SDSS were determined.

This paper, following the literature gap, is the first of its kind that presents an SDSS focused on increasing access to essential goods and services using public spaces and has had a successful response from local authorities with different backgrounds regarding the integration into their decision-making process.

The purpose of this study is to examine the impact of neighborhood design templates on residential home values in King County, WA, USA. Previous research examines a number of individual design factors; this study combines these factors into typologies and tests for the impacts of the composite set of design features.

The study analyzes over 27,000 home sales with a hedonic price model to measure the impacts across three large, regional submarkets. Neighborhood design categories are developed using a cluster analysis on a set of individual neighborhood attributes.

The key finding from this research is that the impact of more traditional (“urban”) design packages on home values is highly contextual. For the older and denser neighborhoods in the study area, a more traditional design results in a significantly positive impact on home values. In the new and more suburban regions of the study area, this effect is not found.

Prior work focused on valuing design attributes individually. The study argues that neighborhood design is better conceived of as a “package”, as the value of a given design element may depend on other co-located attributes. This is the first study, to the authors’ knowledge, to treat physical neighborhood design variables as a composite whole and to attempt to value their impact on home values as such.

With the increasing urbanization rates in emerging countries such as the ones in Latin America and the Caribbean, urban logistics solutions and initiatives are widely needed. Urban planners often consider only passenger transportation and leave freight transportation unattended, thus increasing externalities and degrading the transportation of goods. This chapter presents three urban logistics solutions, which intend to tackle problems related to urbanization and last mile delivery operations challenges by evaluating location models for loading and unloading bays, urban transfer centers location models, and freight trip generation models. The presented solutions were proposed by several researchers of the Institute of Innovation in Productivity and Logistics CATENA-USFQ over the last four years and remain theoretical at the moment. However, we present estimated results of potential implementations in three districts of Quito: Historic Center, Entertainment District, and Corporate District. This chapter not only presents the mentioned urban logistics solutions in Quito but also gives an overview of the followed methodology, which can be replicated in countries and cities of similar characteristics of the region.

Abuja, the capital city of Nigeria, is one of the fastest growing capital cities in sub-Saharan Africa. Recently, the city is experiencing an alarming rate of burglary and violent crimes, while the city planning management frameworks lacks adequate and effective crime mapping, monitoring and management techniques necessary for achieving liveable and safe environment for habitation despite its grandiose spatial planning and aesthetically appealing architectural design as a modern city. Based on police crime records (2007–2018) and geospatial analysis, this paper aims to provide adequate understanding on the interplay of land use configuration and burglary crime formation in residential neighbourhoods of Abuja, Nigeria.

The methods used for the purpose of data collection includes; field survey, Block Environmental Inventory, while inferential statistics and Geographic Information System tools was used for data analysis. The analysis established that Lagos, Nsukka and Enugu Streets are hotspots; while Chief Palace street, Ladoke Akintola and Oka-Akoko streets were found to be cold spots.

This study, however, established the applicability of crime pattern, opportunity theory and routine activity theory in understanding the rising burglary crime events in the study areas and the link between physical characteristics of street block typology and burglary crime pattern. The results of the analysis has in a way affirmed the positions of the theories, while disagreed with them in cases where the results indicated contrary outcome.

This paper concluded with inference drawn from the results that supported mixed-use development but with built-in crime prevention through environmental design strategies as effective burglary crime prevention mechanisms that contribute to crime rate reduction.

The purpose of the paper is to define the best deployment alternatives for a public protection and disaster relief (PPDR) mobile network service – the implementation alternatives being either a dedicated network, a commercial network or a hybrid of the two network types. The selection criteria are based on the social benefits that the PPDR mobile service is expected to bring to society. The critical parameters are population density and service availability, which both directly relate to the socioeconomic benefits achieved by providing broadband (BB) mobile services in various demographic areas.

A causal loop model has been developed to define the socioeconomic benefits of the PPDR network, the parameters being population density, service availability, socioeconomic value of the service and the costs of the network. The network solution alternatives are studied using the Finnish PPDR network as a reference – analysing various areas of the country with differing population densities from remote, rural and more densely populated suburban and urban areas.

Socioeconomic value is a common measure for assessing the value of governmental investments; population density has a strong impact on the optimum deployment alternatives as the socioeconomic value is directly proportional to this variable. The flat nationwide fee of the mobile users means that the users are subsidised in sparsely populated areas – and overcharged in densely populated areas. This is the main reason why the commercial network seems to be most feasible in rural areas, whereas the dedicated network works best in urban areas. Based on the case study, the commercial network is most preferable up to the point when the population density reaches 50-125 persons/km². After that point, the dedicated network becomes more appropriate. Proposals are being made to improve the availability of the commercial networks enabling them to serve as a PPDR network: ensuring priority functionality and a protected power supply; allowing PPDR subscribers the exclusive use of one of the 700 MHz spectrum bands in restricted, critical areas; and extending use of the existing narrowband PPDR network in areas where communication availability is crucial.

On the one hand, the financing of BB PPDR mobile networks is an unresolved issue in many countries. On the other hand, the ability of commercial BB networks to provide better quality of service is improving, making viable the alternative to subscribe for radio service from a commercial operator. Therefore, the feasibility study on how to provide an optimum mobile BB service for PPDR organisations is of real value at this time.

The purpose of this paper is to study the interaction between social and commercial ventures in a region. It achieves this objective through investigating the influence of social ventures’ entry, exit and density on the entry rate of commercial ventures.

Organizational ecology is applied for theoretical analysis and the feasible generalized least square method for empirical analysis.

The study, in overall, finds a diffuse competition between the populations of social and commercial ventures. The results have revealed a negative influence of social ventures’ entry and density on the entry rate of commercial ventures and a positive influence of the social ventures’ exit on commercial ventures’ entry rate in a region.

The study is one of the few in its filed that empirically studies the interaction between social and commercial ventures and the first study, which investigates it in the context of Sweden. The previous two studies, however, have only examined either the influence of social ventures entry or social venture density on the entry rate of commercial ventures. This study, however, examines the influence of both of those factors plus the influence of social venture exit on commercial venture entry. The study is also unique regarding the large-scale database it uses including all the 290 municipalities all over Sweden 1990-2014.

The purpose of this paper is to examine the development cycle of Brazilian banking sector during the lengthy period between 1889 and 2009, also identifying an equilibrium number of financial institutions based on the carrying capacity of the environment.

The number of institutions in equilibrium is calculated based on the population density dependence model adopted under the organizational ecology theory. Quantitative data of founding and failure and qualitative data (interviews with the directors, officers and chief executive officers (CEOs) of selected companies) were used.

In all three bank segments (commercial, investment and multiple), the total number of banks in operation on December 31, 2009 was below the carrying capacity. However, in the multiple bank segment, the gap between the actual and potential figures is slightly smaller. As indicated by the respondents, there is almost no room for newcomers in the major bank segments. In counterpart, there is still space for new arrivals in the mid-market bank sector.

The findings presented here may change, as carrying capacity is determined by political, legal and economic factors, including the availability of resources in niches and constraints imposed through laws, rules and other regulatory aspects. However, raising the life cycle of the entire population offers opportunities for future research on individual organizational trajectories, using new theoretical and methodological perspectives, such as dynamic capabilities and process theory.

The main contribution of this paper lies in indicating the growth potential for banking institution populations in Brazil, and may be used not only by potential newcomers eager to enter the sector, but also as a tool for assessing anti-trust policies.

The development cycle of Brazilian financial institution populations is unknown, and carrying capacity is a construct less explored by academic literature, particularly in Brazil. This is a unique study since a demography of an entire banking population in a developing country does not exist, besides there is not such a financial institution like the multiple bank in Brazil.

Land transactions are a key indicator of urban sustainable development and urban space expansion. Therefore, this paper aims to study the spatial correlation of different types of land transactions.

Based on the big data of land micro transactions in Yangtze River Delta urban agglomeration, this paper uses the generalized forecast error variance decomposition (GFEVD) method to measure the correlation level of urban land markets. Also, social network analysis (SNA) is used to describe spatial correlation network characteristics of an urban agglomeration land market. In the meantime, the factors that influence the spatial correlation of urban land markets are investigated through a quadratic assignment procedure (QAP).

The price growth rate of urban residential land was higher than that of industrial land and commercial land. The spatial relevance of urban residential land is the highest, while the spatial relevance of the urban commercial land market is the lowest. The urban industrial land market, commercial land market and residential land market all present a typical network structure. Population distance (POD) and Engel coefficient distance (EGD) are negatively correlated with the correlation degree of the urban residential land network; traffic distance (TRD) and economic distance (ECD) are negatively correlated with the correlation degree of the urban industrial land network and commercial land network.

This paper uses a systematically-integrated series of problem-solving models to better explain the development path of urban land markets and to realize the integration of the interdisciplinary methods of geography, statistics and big data analysis.

The aim of this study is to determine which of previously used survey measures can be considered the most appropriate to assess children’s advertising exposure. First, three levels of content specificity for assessing children’s exposure to advertising were distinguished as follows: exposure to the medium, exposure to broad content and exposure to specific (i.e. commercial) content. Second, using longitudinal data from 165 children between 8 and 11 years old, the test-retest reliability and content validity of survey measures from all three levels were examined.

Due to societal concerns about the effects of advertising on children’s well-being, research into this topic is expanding. To enhance knowledge accumulation and bring uniformity to the field, a validated standard survey measure of advertising exposure is needed. The aim of this study is to provide such measures for television and internet advertising.

The findings suggest that all measures provided solid estimates for children’s television and internet advertising exposure. Yet, due to minor differences in reliability and validity, it may be concluded that television advertising exposure can best be measured by asking children how often they watch certain popular (commercial) television networks, either weighting or not weighting for advertising density. Internet advertising exposure can best be measured by asking children how often they use the internet or how often they visit certain popular websites, weighting for advertising density.

The current measures for children’s advertising exposure through traditional media can easily be adapted to fit new media.