Shining a spotlight on small rural businesses: How does their performance compare with urban?

Rural enterprises play an important economic role, contributing to national prosperity and wellbeing but are often a blind spot within rural development and wider economic policies and evidence. This paper presents an urban-rural analysis of a large scale survey of Small and Medium-sized Enterprises (SMEs). It applies Propensity Score Matching to allow for an assessment of the effects of rurality on business performance. Results show that England's rural firms have similar levels of turnover to their urban counterparts, but are more likely to report a profit. The analysis also reveals rural firms to be significantly stronger exporters of goods and services and to have goods or services suitable for exporting. However, there are some weaknesses and obstacles to business success that concern significantly more rural than urban firms, that vary with the rurality of local districts, and which require the attention of policy makers and support providers seeking to achieve spatially-balanced and more equitable economic development

Unveiling patterns of international communities in a global city using mobile phone data

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Realities of crime, society and landuse in the Mediterranean : JANUS I

Why create a conceptual model? Such a question lingered through the reviews of the environmental criminology literature, the GIS literature and the 2007 Maltese scenario readings. The reviews, together with an understanding of the complex Maltese data availability situation, highlighted the need to bring together each aspect and build a mindmap that helps set out a process to depict a basic and generic model on how crime, social and landuse issues interact together. The review process also identified techniques and datasets that can be used in the identification and understanding of crime. !e use of these datasets is best explained through a conceptual model that is relevant to CRIme and to the SOcial and LAnduse aspects, herein embedded as the acronym CRISOLA. The model took shape through a tiered 3-phase process, with each iterative phase building up from an abstract level (Phase 1) through the identification of the main datasets (Phase 2) to a final individual attribute listing (Phase 3). The model is not exhaustive as it covers potential datasets that yet need to be created/surveyed, statistical measures identified as well as inclusion of other crime relevant theories. The model can be evolved in future studies as it attempts to highlight areas of study that will not be tackled in this research and which may/may not be found to be significant, entailing further change.peer-reviewe

A trade hierarchy of cities based on transport cost thresholds

Empirical evidence has been lacking to explain trade agglomerations within countries. Starting with a novel micro-database of road freight shipments between Spanish municipalities for the period 2003–07, we break down city (municipal) trade flows into the extensive and intensive margins and assess trade frictions and trade concentration relying on a unique generalized transport cost measure and three internal borders: NUTS-5 (municipal), NUTS-3 (provincial) and NUTS-2 (regional). We find a stark accumulation of trade flows up to a transport cost value of €189 (approximately 170 km) and conclude that this high density is not due to administrative borders effects but to significant changes in the trade-to-transport costs relationship. To support this hypothesis, we propose and conduct an endogenous Chow test to identify significant thresholds at which trade flows change structurally with transport costs. These breakpoints allow us to split the sample when controlling for internal borders, and to define trade market areas corresponding to specific transport costs that consistently reveal an urban hierarchy of cities. The results provide clear evidence with which to corroborate the predictions of Central Place TheoryThis work was supported by MINECO [grant number ECO2013-46980-P]; and the Ministerio de Educacion, Cultura y Deporte [grant number FPU-2010

Time-lagged inversedistance weighting for air temperature analysis in an equatorial urban area (Guayaquil, Ecuador)

It is well known that sudden variations of air temperature have the potential to cause severe impacts on human health. Therefore, it becomes necessary to provide information capable of quantifying the severity of the problem, considering that the continuous increase of temperature due to global warming and urban development will cause more intense effects in heavily populated areas. Due to its geographical location and local characteristics, Ecuador, a country located on the western coast of South America, is characterized by a high vulnerability to climatic extremes. The present research develops an evaluation of urban climate change effects through the analysis of extreme temperature indices using four meteorological stations situated in the city of Guayaquil (southwest Ecuador). Since the available data are not adequate for extreme temperature indices criteria, it was necessary to employ an infilling method for times series in an innovative way that can be applicable at the small scale. Thus, a cross-correlation-enhanced inverse distance weighting (CC-IDW) method was proposed. The method entails a spatial interpolation based on data of urban stations situated outside of Guayaquil by taking into account cross-correlation among times series at precise lags that leads to an improvement in the way of estimating the missing values. Subsequently, a homogeneity test, data quality control and the calculation of extreme temperature indices chosen from those proposed by the World Meteorological Organization (WMO) were implemented. The results show that there is a general tendency of warming with quite homogenous temperatures for all considered stations. However, it should be recognized that the climate pattern of this region is strongly modulated by the El Niño Southern Oscillation (ENSO) cycle. Only for two extreme indices: the highest maximum temperature (TXx) and the warm days (TX90p), are the resulting trend co-efficients statistically significant. The study suggests a deteriorated climatic condition due to heat stress that warrants further study using the available database for the city of Guayaquil

The Political Economy of Housing Investment in the Short‐Term Rental Market: Insights from Urban Portugal

Short-term rentals (STRs) emerged as holiday accommodations, disrupting the hospitality industry in the decade before COVID-19. Mainstream explanations for their growth revolved around digital tourism platforms like Airbnb as market disruptors and the sharing economy rationale. At the same time, critical scholars explored the capitalisation of greater rent gaps in urban central locations. However, these explanations are insufficient to explain the growth of STRs. We supplement them by building bridges between the urban political economy and the geographies of financialisation through the cases of Lisbon and Porto before the pandemic. The paper focuses on tourism-induced housing investment, taking a closer look at the profile of investors in association with STR property managers in the context of the late-entrepreneurial urban regime. We conclude that tourism development has allowed opportunities for housing financialisation through STR professionalisation, enhancing the allocation of interest-bearing capital in tourism-oriented real estate.Los alquileres de corta duración (STRs en sus siglas en inglés) surgieron como alojamientos vacacionales, revolucionando la industria turística en la década anterior al Covid-19. Las principales explicaciones sobre su irrupción apuntan a que las plataformas de turismo digital, como Airbnb, alteraron el mercado a través de la lógica de la economía colaborativa, mientras que voces críticas también han explorado la capitalización de brechas de renta más amplias en áreas urbanas centrales. Sin embargo, estas explicaciones son insuficientes para comprender el rápido crecimiento de esta tipología de alojamiento. Aquí se complementan tales ideas mediante la articulación entre la economía política urbana y las geografías de la financiarización a través de los casos de Lisboa y Oporto antes de la pandemia. El artículo se centra en la inversión inmobiliaria inducida por el turismo, examinando el perfil de los inversores privados en conjunción con las empresas de gestión de propiedades para alquiler turístico en el contexto del urbanismo empresarial tardío. Así, concluimos que el desarrollo turístico reciente ha ampliado el horizonte de financiarización de la vivienda gracias a la profesionalización del alquiler vacacional, maximizando los beneficios del capital invertido en bienes inmobiliarios para uso turístico.info:eu-repo/semantics/publishedVersio

Rural businesses and levelling up: A rural-urban analysis of business innovation and exporting in England's north and midlands

In the face of persistent and widening regional imbalances in economic and social outcomes, the UK Government seeks to ‘level up’ less prosperous communities, reigniting debates on the relationships between geography and business innovation. A key question concerns whether cities provide a more favourable environment for business innovation and exporting. However, the comparative performance of urban and rural Small and Medium Sized Enterprises (SMEs) within less prosperous regions has received little attention. Using Longitudinal Small Business Survey data, we apply Propensity Score Matching to study urban-rural differences in SME performance in the North and Midlands of England. The findings reveal no systematic, significant differences in goods, service and process innovation or exporting between rural and urban SMEs, suggesting that the emphasis on urban focused growth in the levelling up agenda appears misplaced

Robust building identification from street views using deep convolutional neural networks

Street view imagery (SVI) is a rich source of information for architectural and urban analysis using computer vision techniques, but its integration with other building-level data sources requires an additional step of visual building identification. This step is particularly challenging in architecturally homogeneous, dense residential streets featuring narrow buildings, due to a combination of SVI geolocation errors and occlusions that significantly increase the risk of confusing a building with its neighboring buildings. This paper introduces a robust deep learning-based method to identify buildings across multiple street views taken at different angles and times, using global optimization to correct the position and orientation of street view panoramas relative to their surrounding building footprints. Evaluating the method on a dataset of 2000 street views shows that its identification accuracy (88%) outperforms previous deep learning-based methods (79%), while methods solely relying on geometric parameters correctly show the intended building less than 50% of the time. These results indicate that previous identification methods lack robustness to panorama pose errors when buildings are narrow, densely packed, and subject to occlusions, while collecting multiple views per building can be leveraged to increase the robustness of visual identification by ensuring that building views are consistent

Transportation Infrastructure and Development in Ghana

We study the impact of transportation infrastructure on agriculture and development in colonial Ghana. Two railway lines were built between 1901 and 1923 to connect the coast to mining areas and the large hinterland city of Kumasi. This unintendedly opened vast expanses of tropical forest to cocoa cultivation, allowing Ghana to become the world's largest producer. This attracted migrants to producing areas and the economic surplus drove urbanization. Using data at a very fine spatial level, we find a strong effect of railroad connectivity on cocoa production due to reduced transportation costs. We then show that the economic boom in cocoa-producing areas was associated with demographic growth and urbanization. We _nd no spurious effect from lines that were not built yet, and lines that were planned but never built. We show that our results are robust to considering nearest neighbor estimators. Lastly, railway construction has durably transformed the economic geography of Ghana, as railway districts are more developed today, despite thirty years of marked decline in rail transportation

A review on deep learning techniques for 3D sensed data classification

Over the past decade deep learning has driven progress in 2D image understanding. Despite these advancements, techniques for automatic 3D sensed data understanding, such as point clouds, is comparatively immature. However, with a range of important applications from indoor robotics navigation to national scale remote sensing there is a high demand for algorithms that can learn to automatically understand and classify 3D sensed data. In this paper we review the current state-of-the-art deep learning architectures for processing unstructured Euclidean data. We begin by addressing the background concepts and traditional methodologies. We review the current main approaches including; RGB-D, multi-view, volumetric and fully end-to-end architecture designs. Datasets for each category are documented and explained. Finally, we give a detailed discussion about the future of deep learning for 3D sensed data, using literature to justify the areas where future research would be most valuable.Comment: 25 pages, 9 figures. Review pape