Spatial Units Of Analysis Research Articles

The Methodology of Landscape Quality (LQ) Indicators Analysis Based on Remote Sensing Data: Polish National Parks Case Study

Landscape quality (LQ) encompasses diverse characteristic of the natural and cultural environment. The most effective tool to analyze LQ is the use of indicators. The main problem in the assessment of LQ is not the lack of indicators, but its multitude. That is why, the indicators’ categorization is a problematic issue. The paper aims to introduce and test the methodology for selecting the suitable indicators based on the example of two national parks located in the south-east part of Poland. The method composed of the following stages: (1) Selection of spatial units being analyzed; (2) selection of indicators type(s); (3) selection of specific indicators; (4) calculation of indicator set no 1; (5) analysis of the correlation between indicators’ pairs; (6) selection of a final set of indicators; (7) analysis of effectiveness. The latter stage, refers to the statistical analysis of significance between results obtained dependently on the data sources, a spatial unit of analysis and analyzed regions. The results showed that the categorization composed of ten, mainly composite indicators, can be applied to conclude on different levels of LQ of protected areas. The differences between two analyzed data sources, different spatial units, as well as diverse regions, occurred to be statistically insignificant. Generally, the results of the effectiveness analysis showed that a final categorization of LQ indicators is adequate to conclude on the diverse dimensions of LQ of analyzed protected areas.

Examining Micro-Level Homicide Patterns in Toronto, 1967 Through 2003

This article assesses the spatial distribution and developmental pattern of micro-level homicide clusters in Toronto between 1967 and 2003. The spatial unit of analysis is the street segment and is defined as the two block faces on both sides of a street between two intersections. Three time periods (1967 to 1979, 1980 to 1989, and 1990 to 2003) covering 1,671 homicides were pooled to ensure sufficient numbers for analysis. Given the qualitative strength of the dataset, a series of descriptive statistics and geospatial statistics are used. Toronto’s developmental homicide pattern is characterized by a dense concentration of single-homicide street segments within the downtown core between 1967 and 1989, with the mass dispersal of multi-homicide street segments across the city between 1990 and 2003. Single-homicide street segments accounted for 84% and 81% of homicides between 1967 and 1979 and 1980 and 1989, respectively. However, multi-homicide street segments tripled between 1990 and 2003, rising from 16% of homicides in the first time period to 45% in the third. This reflects a change in the character of homicides with drug and gang-related homicides increasing in the third time period.

A classification of European agricultural land using an energy-based intensity indicator and detailed crop description

With agricultural areas covering almost half of European land, proper management of agro-ecosystems is key to achieve the European Union’s environmental and climate objectives. This requires spatially explicit methods and indicators. We developed an approach for the classification of agricultural land by combining two main dimensions i) land cover, using detailed geo-spatialized census data covering 63 individual crops; ii) management intensity, measured as the anthropogenic energy required in the primary crop production. As a result we identified 10 main crop systems further classified into 30 ‘crop-management systems’ at a spatial resolution of 5 arcminutes. The resulting maps show the spatial patterns of agricultural management intensity across Europe, both in absolute terms (total energy input per hectare) and relative to the dominant crop system in the spatial unit of analysis. The use of multiple intensity dimensions provides new, more detailed insights on agricultural intensity by which areas that were previously classified as low-medium intensive - some permanent crops systems or irrigated arable land - appear now as highly intensive. An expert-based evaluation was carried out on the intensity maps and corroborated the obtained results. The generated maps can be used to support decision-making in designing more targeted, context-specific agricultural and territorial policies. In particular, findings can be relevant in the context of the Common Agricultural Policy post 2020 and the Biodiversity Strategy towards 2030, both of which will benefit from more detailed spatially explicit information to achieve their stated objectives.

Location Choice of New Business Establishments: Understanding the Local Context and Neighborhood Conditions in the United States

With the continuing shift toward e-commerce, physical business locations with a brick-and-mortar presence become an endangered element of urban fabric, land use, and the local economy. City governments and local municipalities have created and implemented a variety of strategies and incentives to stimulate new business activity within their jurisdictions. A policy of enhancing the business climate is productive in some regions but not in others. To understand these variations in outcomes, this research focuses on examining the relationship between the uniqueness of certain regions, spatially bounded characteristics, and how both affect where new establishments locate. A two-level model is introduced to employ the census tract as a spatial unit of analysis and analyzes new establishments within 27 medium-sized metropolitan statistical areas in the United States. That quantitative model allows this study to determine key regional and neighborhood factors, as well as the existence of previously unmeasured factors, influencing location decisions of new establishments. The results of this study confirm the importance of economic, demographic, and geographic conditions at the neighborhood level, providing a better understanding of the vulnerability of the local economy.

A Reliability Check of Walkability Indices in Seoul, Korea

The purpose of this study is to evaluate walkability levels using popular indices and check the measurement reliability between those indices. This study evaluates the city of Seoul, using 100 × 100 m grid points (N = 44,000) as spatial units of analysis. In this study, four types of indices were used to measure walkability levels: Walkability index (WI), Walk score (WS), Pedshed (Ps), and Movability index (MI). This study utilizes Pearson’s R, Brand–Altman plot with limit of agreement (LOA), and intraclass correlation coefficient (ICC) as reliability check methods. The measurement reliability among the four indices was found to be relatively high. The Pearson’s R values were between 0.308 and 0.645, and the range of inside LOA of Brand–Altman plots was 94.5% to 95.5%. The ICC value of the four indices was 0.544, indicating moderate reliability. The results reveal a relatively high level of measurement reliability between the four indices. On the basis of this study’s results, the level of walkability in other cities in Korea can be ascertained. The study may provide future direction for walkability index development that considers urban environmental characteristics. From the results, we expect that future urban planning and policies will aim to improve walkability.

Assessing landscape response to tectonics in the Jalisco block and adjacent areas (west-central Mexico) using topographic analysis

To elucidate how landscapes respond to either tectonic and climatic changes through time, geomorphologists use the analysis of river and basin morphometry. Many studies have evaluated how rivers, and consequently hillslopes, respond to changes in the rate of rock uplift in compressional settings. The case of extensional settings, however, has received less attention. We examine the case of landscape response to tectonics in west-central Mexico to identify zones where there is notable increase in the tectonic activity. The study of west-central Mexico is challenging for the morphometric analysis since there is an interplay between the development of extensional structures and pulses of volcanism, mainly from Eocene to Oligocene and Pliocene to Pleistocene, that have buried large parts of the landscape. For our purpose we use the normalized channel steepness index (ksn) to evaluate how this index correlates with other morphometric parameters such as the hillslope gradient, hillslope convexity and concavity, terrain rugosity and first order stream gradient (Fosg). We use the landform units map as the spatial unit of analysis and from this, we estimate the morphometric properties of the landscape. The results of the morphometric analysis indicate that the topography of Eocene to Oligocene volcanic landscapes are well explained (~70%) by the ksn and in less degree for Pliocene to Pleistocene landscape where ksn accounts for ~55–66% of landscape morphometry. Our results also indicate that the ksn is the best suited among all morphometric variables used in this study and confirm that the landscape of west-central Mexico is dominated by river incision. Landscape incision is particularly high on the northern part of the Jalisco block, mainly along the Ameca river and its tributaries such as Atenguillo river, and north of the Jalisco block, along the Santiago river and on tributaries incising the Sierra Madre Occidental. Finally, the morphometric analysis indicates that even for the case where the volcanic rocks and sediments are filling up large parts of the landscape, river incision resulting from the tectonic activity is capable to cut into lavas, propagating the change imposed by the tectonic activity.

Morphological tessellation as a way of partitioning space: Improving consistency in urban morphology at the plot scale

Urban Morphometrics (UMM) is an expanding area of urban studies that aims at representing and measuring objectively the physical form of cities to support evidence-based research. An essential step in its development is the identification of a suitable spatial unit of analysis, where suitability is determined by its degree of reliability, universality, accessibility and significance in capturing essential urban form patterns. In Urban Morphology such unit is found in the plot, a fundamental component in the morphogenetic of urban settlements. However, the plot is a conceptually and analytically ambiguous concept and a kind of spatial information often unavailable or inconsistently represented across geographies, issues that limit its reliability and universality and hence its suitability for Urban Morphometric applications. This calls for alternative methods of deriving a spatial unit able to convey reliable plot-scale information, possibly comparable with that provided by plots.This paper presents Morphological Tessellation (MT), an objectively and universally applicable method that derives a spatial unit named Morphological Cell (MC) from widely available data on building footprint only and tests its informational value as proxy data in capturing plot-scale spatial properties of urban form. Using the city of Zurich (CH) as case study we compare MT to the cadastral layer on a selection of morphometric characters capturing different geometrical and configurational properties of urban form, to test the degree of informational similarity between MT and cadastral plots.Findings suggest that MT can be considered an efficient informational proxy for cadastral plots for many of the tested morphometric characters, that there are kinds of plot-scale information only plots can provide, as well as kinds only morphological tessellation can provide. Overall, there appears to be clear scope for application of MT as fundamental spatial unit of analysis in Urban Morphometrics, opening the way to large-scale urban morphometric analysis.

Rank-size distribution of urban employment in labour market areas

This paper examines the rank-size distribution of employment in cities considering the designation of unified labour market areas (LMAs). The empirical application refers to Greece and the dataset includes population and employment at the municipal and local commune levels as well as LMAs originating from commuting patterns between communes. It is found that the employment rank-size distribution based on municipalities substantially overestimates the size of smaller peripheral town areas, where labour agglomerations typically take place at finer spatial scales, and underestimates agglomerations around larger urban areas. The use of LMAs as an alternative spatial unit of analysis entails a less even geographical distribution of workers, particularly when adopting a 10% commuting threshold. Moreover, a threshold regression is used to facilitate the distinction between urban and rural areas, specifying the LMA size beyond which substantial urbanisation effects arise and signifying that about 80% of the total employment corresponds to urban employment. The findings can have important implications for the formulation and evaluation of policies to manage labour agglomeration economies in metropolitan and peripheral urban and rural areas for the sustainable and equitable growth of a country.

Predicting the spatiotemporal legality of on-street parking using open data and machine learning

ABSTRACTSearching for a parking spot in metropolitan areas is a great challenge, especially in highly populated areas such as downtown districts and job centres. On-street parking is often a cost-effective choice compared to parking facilities such as garages and parking lots. However, limited space and complex parking regulation rules make the search process of on-street legal parking very difficult. To this end, we propose a data-driven framework for understanding and predicting the spatiotemporal legality of on-street parking using the NYC parking tickets open data, points of interest (POI) data and human mobility data. Four popular types of spatial analysis units (i.e. point, street, census tract, and grid) are used to examine the effects of spatial scale in machine learning predictive models. The results show that random forest works the best with the minimum root-mean-square-error (RMSE) for predicting ticket counts and with the highest accuracy scores for spatiotemporal legality classification across all four spatial analysis scales. Moreover, several prominent categories of places such as those with retail stores, health-care services, accommodation and food services are positively associated with the number of parking violation tickets.

Intersections and Non-Intersections: A Protocol for Identifying Pedestrian Crash Risk Locations in GIS.

Intersection and non-intersection locations are commonly used as spatial units of analysis for modeling pedestrian crashes. While both location types have been previously studied, comparing results is difficult given the different data and methods used to identify crash-risk locations. In this study, a systematic and replicable protocol was developed in GIS (Geographic Information System) to create a consistent spatial unit of analysis for use in pedestrian crash modelling. Four publicly accessible datasets were used to identify unique intersection and non-intersection locations: Roadway intersection points, roadway lanes, legal speed limits, and pedestrian crash records. Two algorithms were developed and tested using five search radii (ranging from 20 to 100 m) to assess the protocol reliability. The algorithms, which were designed to identify crash-risk locations at intersection and non-intersection areas detected 87.2% of the pedestrian crash locations (r: 20 m). Agreement rates between algorithm results and the crash data were 94.1% for intersection and 98.0% for non-intersection locations, respectively. The buffer size of 20 m generally showed the highest performance in the analyses. The present protocol offered an efficient and reliable method to create spatial analysis units for pedestrian crash modeling. It provided researchers a cost-effective method to identify unique intersection and non-intersection locations. Additional search radii should be tested in future studies to refine the capture of crash-risk locations.

A Machine Learning Approach to Study the Relationship between Features of the Urban Environment and Street Value

Understanding what aspects of the urban environment are associated with better socioeconomic/liveability outcomes is a long standing research topic. Several quantitative studies have investigated such relationships. However, most of such works analysed single correlations, thus failing to obtain a more complete picture of how the urban environment can contribute to explain the observed phenomena. More recently, multivariate models have been suggested. However, they use a limited set of metrics, propose a coarse spatial unit of analysis, and assume linearity and independence among regressors. In this paper, we propose a quantitative methodology to study the relationship between a more comprehensive set of metrics of the urban environment and the valorisation of street segments that handles non-linearity and possible interactions among variables, through the use of Machine Learning (ML). The proposed methodology was tested on the French Riviera and outputs show a moderate predictive capacity (i.e., adjusted R 2 = 0.75 ) and insightful explanations on the nuanced relationships between selected features of the urban environment and street values. These findings are clearly location specific; however, the methodology is replicable and can thus inspire future research of this kind in different geographic contexts.

Comparison of malaria incidence rates and socioeconomic-environmental factors between the states of Acre and Rond\xf4nia: a spatio-temporal modelling study

BackgroundPlasmodium falciparum malaria is a threat to public health, but Plasmodium vivax malaria is most prevalent in Latin America, where the incidence rate has been increasing since 2016, particularly in Venezuela and Brazil. The Brazilian Amazon reported 193,000 cases in 2017, which were mostly confirmed as P. vivax (~ 90%). Herein, the relationships among malaria incidence rates and the proportion of accumulated deforestation were contrasted using data from the states of Acre and Rondônia in the south-western Brazilian Amazon. The main purpose is to test the hypothesis that the observed difference in incidence rates is associated with the proportion of accumulated deforestation.MethodsAn ecological study using spatial and temporal models for mapping and modelling malaria risk was performed. The municipalities of Acre and Rondônia were the spatial units of analysis, whereas month and year were the temporal units. The number of reported malaria cases from 2009 until 2015 were used to calculate the incidence rate per 1000 people at risk. Accumulated deforestation was calculated using publicly available satellite images. Geographically weighted regression was applied to provide a local model of the spatial heterogeneity of incidence rates. Time-series dynamic regression was applied to test the correlation of incidence rates and accumulated deforestation, adjusted by climate and socioeconomic factors.ResultsThe malaria incidence rate declined in Rondônia but remained stable in Acre. There was a high and positive correlation between the decline in malaria and higher proportions of accumulated deforestation in Rondônia. Geographically weighted regression showed a complex relationship. As deforestation increased, malaria incidence also increased in Acre, while as deforestation increased, malaria incidence decreased in Rondônia. Time-series dynamic regression showed a positive association between malaria incidence and precipitation and accumulated deforestation, whereas the association was negative with the human development index in the westernmost areas of Acre.ConclusionLandscape modification caused by accumulated deforestation is an important driver of malaria incidence in the Brazilian Amazon. However, this relationship is not linearly correlated because it depends on the overall proportion of the land covered by forest. For regions that are partially degraded, forest cover becomes a less representative component in the landscape, causing the abovementioned non-linear relationship. In such a scenario, accumulated deforestation can lead to a decline in malaria incidence.

As different as night and day: Scaling analysis of Swedish urban areas and regional labor markets

The urban scaling framework views cities as integrated socioeconomic networks of interactions embedded in physical space. A crucial property of cities highlighted by this approach is that cities act to mix populations, a mixing both facilitated and constrained by physical infrastructure. Operationalizing a view of cities as settings for social interactions and population mixing—assembling a set of spatial units of analysis which contain the relevant social aspects of urban settlements—implies choices about the use of existing data, the assignation of data to locations, and the delineation of the boundaries of urban areas, all of which are far from trivial research decisions. Metropolitan areas have become the spatial unit of choice in urban economics and economic geography for investigating urban life as they are seen as encompassing the distinct phenomena of “urbanity” (proximity, density) and social interactions indirectly captured through a unified labor market. However, the population size and areal extent of metropolitan areas, as most often defined, render opaque the distinction between two salient types of urban population: those who work and those who reside within a metropolitan area. These two sets of individuals, among whom of course there is great overlap, putatively engage in different economic and social interactions which are in turn differently embedded in physical space. Availing ourselves of Swedish micro-level data for two distinct spatial units, tätorts (“dense localites”) and local labor markets, we can distinguish which types of populations and which types of spatial agglomerations are responsible for the observed scaling effects on productivity and physical infrastructure. We find that spatially contiguous labor markets are not enough to generate some of the most salient urban scaling phenomena.

Revealing the relationship of human convergence–divergence patterns and land use: A case study on Shenzhen City, China

Understanding the relationship between human mobility and land use has been a longstanding topic in multiple disciplines, including transport geography and urban planning. Recently, urban collective mobility patterns have become a hot research direction and has been explored at an unprecedented space–time scale due to the emerging big human tracking datasets (e.g., mobile phone data). However, only a few studies have comprehensively quantified the effects of land use on human mobility patterns while considering the influence of the scale of spatial analysis units. This study attempts to reinforce this knowledge by investigating urban human convergence–divergence patterns and their relationship with land use distribution characteristics at three popular types of spatial analysis units of human mobility studies (voronoi polygons, grid cells, and traffic analysis zones) using mobile phone data. A case study on Shenzhen, China is implemented, and results indicate that eight distinct convergence–divergence patterns could be extracted to describe urban collective mobility patterns despite the use of different types of spatial analysis unit. Moreover, the scale of spatial analysis units exerts a few effects on the quantification of the influence of land use distribution on human convergence–divergence patterns, but some common characteristics could be summarized from these discrepant results. The findings can help policy makers understand urban human mobility and can serve as a guide for urban management and planning.

Perceptions of regional inequality and the geography of discontent: insights from the UK

ABSTRACTThis paper examines the issue of whether the UK displays high levels of interregional inequality or only average levels of inequality. The question arises due to major differences in public perceptions. Following on from recent UK public debates, the UK evidence is examined in the context of 28 different indicators and 30 different Organisation for Economic Co-operation and Development (OECD) countries. Answering this question involves a careful consideration of the ways in which we use different spatial units of analysis, different measures of prosperity and different indices of inequality in order to understand interregional inequality, and the issues that arise are common to all countries. In the specific case of the UK, the result is clear. The UK is one of the most regionally unbalanced countries in the industrialized world.

Spatial heterogeneity in spatial interaction of human movements—Insights from large-scale mobile positioning data

Distance decay is a primary characteristic of spatial interaction in human movements, and it has been incorporated into many spatial interaction models. Existing approaches mainly rely on travel survey datasets to fit the frictional coefficient of distance decay. However, limited sample size and spatiotemporal resolution make the determination of the spatial interaction characteristic from a comprehensive view difficult. Recently, this situation has been reversed due to emerging large human trajectory datasets, which have stimulated a body of literatures to re-examine the traditional issue of distance decay. However, these studies only focused on distance decay from a global perspective and neglected the spatial non-stationarity of spatial interaction. This study aims to reveal the spatial heterogeneity of distance decay of human movements extracted from massive mobile phone location data from Shenzhen, China. The power law function is utilized to fit the distance decay coefficients for inflow and outflow of each spatial analysis unit. Then, geographically weighted regression is employed to quantify the relationship between distance decay coefficients and land use distribution and between distance decay coefficients and traffic facilities. Results show that considerable spatial non-stationarity appears in the distance decay of spatial interaction, and the regression coefficients indicate the spatial variations of the influence of land use and traffic facilities on distance decay across urban space. These findings provide an in-depth insight into the distance decay characteristics of human movements in a more microcosmic space.

Geographic disparities and temporal changes in risk of death from myocardial infarction in Florida, 2000\u20132014

BackgroundIdentifying disparities in myocardial infarction (MI) burden and assessing its temporal changes are critical for guiding resource allocation and policies geared towards reducing/eliminating health disparities. Our objectives were to: (a) investigate the spatial distribution and clusters of MI mortality risk in Florida; and (b) assess temporal changes in geographic disparities in MI mortality risks in Florida from 2000 to 2014.MethodsThis is a retrospective ecologic study with county as the spatial unit of analysis. We obtained data for MI deaths occurring among Florida residents between 2000 and 2014 from the Florida Department of Health, and calculated county-level age-adjusted MI mortality risks and Spatial Empirical Bayesian smoothed MI mortality risks. We used Kulldorff’s circular spatial scan statistics and Tango’s flexible spatial scan statistics to identify spatial clusters.ResultsThere was an overall decline of 48% in MI mortality risks between 2000 and 2014. However, we found substantial, persistent disparities in MI mortality risks, with high-risk clusters occurring primarily in rural northern counties and low-risk clusters occurring exclusively in urban southern counties. MI mortality risks declined in both low- and high-risk clusters, but the latter showed more dramatic decreases during the first nine years of the study period. Consequently, the risk difference between the high- and low-risk clusters was smaller at the end than at the beginning of the study period. However, the rates of decline levelled off during the last six years of the study, and there are signs that the risks may be on an upward trend in parts of North Florida. Moreover, MI mortality risks for high-risk clusters at the end of the study period were on par with or above those for low-risk clusters at the beginning of the study period. Thus, high-risk clusters lagged behind low-risk clusters by at least 1.5 decades.ConclusionMyocardial infarction mortality risks have decreased substantially during the last 15 years, but persistent disparities in MI mortality burden still exist across Florida. Efforts to reduce these disparities will need to target prevention programs to counties in the high-risk clusters.

Planificación de áreas de influencia de embalses. Propuesta desde la Capacidad de Acogida

El punto de partida de esta investigación es la identificación de un vacío en la planificación y ordenación de las áreas de influencia de los embalses para la generación de energía, entendidos como cuerpos de agua en los que se concentra una parte importante de la oferta de recursos hídricos. Con base en la descripción de la problemática y la importancia de considerar los megaproyectos hidroeléctricos como unidades de análisis espacial, se propone la Capacidad de Acogida del Territorio como un herramienta metodológica para evaluar los impactos acumulativos, en tanto permite entender la configuración del territorio luego de la entrada en operación del proyecto hidroeléctrico. Esto puede convertirse en un insumo para la toma de decisiones con respaldo técnico que responda a la pregunta de qué hacer sosteniblemente en dichos territorios.

Zoning and weighting in urban heat island vulnerability and risk mapping in Helsinki, Finland

Climate change is likely to increase the risks related to heat waves in urban areas. We map spatial pattern of heat wave vulnerability and risk in the Helsinki metropolitan area in southern Finland. First, we assess differences that zoning, i.e., differences in spatial units of analysis, and weighting, i.e., weights given to indicators when constructing the index, cause in map production. Second, we evaluate how maps of consensus and certainty could pave the way for visualizing and assessing uncertainties in risk and vulnerability indices. For vulnerability, we use socioeconomic data using 5 different zoning options and 11 different weighting options. For risk, we add two extra layers to vulnerability maps: hazard map showing the spatial pattern of heat based on Landsat satellite images and exposure map showing the spatial pattern of population. We found that when different zoning options are used, the spatial pattern of vulnerability may differ dramatically. In risk maps, the differences between zoning options are smaller. Contrary to previous literature, differences in indicator weighting alter the final maps slightly. The consensus and certainty maps show their potential, e.g., in pointing out areas which may have both high risk/vulnerability and high certainty for risk/vulnerability. Finally, we discuss other possibilities in tackling the uncertainties in mapping and propose new avenues for research.

Os processos de urbanização e conversão florestal na Amazônia paraense – um estudo multiescalar

Na Amazônia brasileira, as dinâmicas de urbanização e conversão florestal possuem lógicas complexas, dependentes de fatores e agentes atuando em diferentes escalas. Este artigo explora a evolução espacial e temporal da urbanização e da conversão florestal na Amazônia, a partir de elementos mensuráveis presentes nestes processos: a expansão urbana, dimensão observável dos processos de urbanização; e o desmatamento, uma medida dos processos de conversão florestal. A identificação de padrões espaço-temporais similares, avaliados quanto às tendências de evolução temporal do grau de urbanização e incremento de desmatamento em 2000, 2010 e 2014, foi base para a proposta de uma tipologia das relações entre expansão urbana e desmatamento para os estados da Amazônia Legal e municípios do Pará. O estudo explorou também as relações entre expansão urbana e desmatamento, com uso de regressões geograficamente ponderadas (GWR), observando duas unidades espaciais de análise para o estado do Pará: os limites municipais e uma grade celular (20x20km). Esta abordagem multiescalar evidenciou a dificuldade de se estabelecerem relações conclusivas quanto à co-ocorrência, ou concordância, da evolução dos processos de urbanização e conversão florestal. Os resultados contribuem para o estabelecimento de uma agenda sobre modelos de desenvolvimento regional que retome o urbano como essencial para uma Amazônia socialmente justa e ambientalmente responsável.