Nonresidential Land Research Articles

Development Strategy Based on Combination Typologies of Building Carbon Emissions and Urban Vibrancy—A Multi-Sourced Data-Driven Approach in Beijing, China

When confronting the dual challenges of rapid urbanization and climate change, although extensive research has investigated the factors influencing urban carbon emissions and the practical strategies regarding urban vibrancy, the unclear mutual nexus between them and the development strategy for collaborative optimization requires further in-depth analysis. This study explores the delicate balance between urban vibrancy and low-carbon sustainability within the confines of Beijing’s Fifth Ring Road. By integrating OpenStreetMap, land use, population, and buildings’ carbon emission data, we have developed a reproducible method to estimate total carbon emissions and emission intensity. Furthermore, we have introduced vibrancy index data to distinguish the vibrancy evaluation of residential and non-residential land and applied cross-combinational classification technology to dissect the spatial correlation between urban carbon emissions and urban vibrancy. The results reveal that the four combination typologies show more significant differences and regularity in residential land. Based on the discovery of spatial correlation, this study puts forward corresponding development strategy suggestions for each of these four typologies based on the geographical location and requirements of urban development policies. In conclusion, our study highlights the importance of integrating carbon emissions and urban vibrancy comprehensively in sustainable urban planning and proposes that various land use combinations need targeted development strategies to achieve this goal, which need to consider population, energy, service facilities, and other diverse aspects.

Study on the Actual Situation of Non-residential Land Uses Located in Residential-Only Areas

Study on the Actual Situation of Non-residential Land Uses Located in Residential-Only Areas

Access-based land value appreciation for assessing project benefits

The traditional mobility-oriented travel-time saving benefit assessment method has been repeatedly questioned for numerous intrinsic flaws, motivating the search for alternative benefit assessment approaches. Although a wealth of literature confirms the capitalization effect of access benefits induced by transport improvements to land or real estate value, the access-based land value uplift method hasn’t yet been widely recognized and employed as an official tool assisting transport decision-making. The present paper collects 136 empirical studies and aims to disentangle the obstacles hindering the promotion of the access-based assessment method by systematically reviewing methodological design, the access metrics used, and the target real estate sub-markets or land use types upon which access benefits are quantified. First, it was found that almost half of the sampled studies just investigated the general effects of transport operation on real estate prices without incorporating sufficient temporal and locational considerations, thereby failing to isolate project-specific incremental impacts. Second, while the hedonic pricing model remains the most popular model, a trend towards embracing more advanced modelling techniques such as spatial lag, spatial error, and Difference-in-Differences (DID) models to control for bias caused by spatial dependence has been observed. Third, Euclidean distance and distance buffer rings are the most widely used operational measures of access. Primal access measures covering the number of opportunities available at a destination and travel impedance are recommended. Last, over 86% of sampled empirical studies target the residential real estate market. The lack of non-residential land uses in the literature presents a significant research gap that should be addressed.

Sustainable land use as panacea for efficient households’ trips in Osun State Nigeria

An underlying factor with the problems of trip making in urban areas is related to the land use potentials and the condition of its transport system. The land use pattern of any settlement is the basis for its physical planning, especially in rapidly growing cities. This study examines land use and households’ trip in selected cities of Osun State, Nigeria. Data were obtained for the pattern of land uses. Data were also randomly obtained from 2,134 households to examine their trip pattern. Descriptive statistics was used to analyse land use pattern, while correlation analysis was used to examine relationship between land use and households’ trip pattern. Findings revealed that all the cities studied possessed characteristics of mixed land use. The study shows that 57.15% of the land use is for residential while non-residential accounted for 42.85%. Further, households made an average of 27 trips per week and travelled an average of 10.65kilometres per day in the selected cities. Results from correlation analysis indicated that there is low positive correlation between residential land use and trip frequency (r = 0.302) as well as distance travel (r = 0.222). Further, with correlation coefficient (r) of 0.613 and 0.559, there is strong positive correlation between non-residential land use and trip frequency as well as distance traveled by the household. The study recommends holistic urban planning to reduce future urban land use problems that may lead cities into a chaotic situation.

Immunity to poliovirus in Afghanistan: A household sampling method for serological assessment based on geographical information systems.

Afghanistan continues to experience challenges affecting polio eradication. Mass polio vaccination campaigns, which aim to protect children under the age of 5, are a key eradication strategy. To date, the polio program in Afghanistan has only employed facility-based seroprevalence surveys, which can be subject to sampling bias. We describe the feasibility in implementing a cross-sectional household poliovirus seroprevalence survey based on geographical information systems (GIS) in three districts. Digital maps with randomly selected predetermined starting points were provided to teams, with a total target of 1,632 households. Teams were instructed to navigate to predetermined starting points and enrol the closest household within 60 m. To assess effectiveness of these methods, we calculated percentages for total households enrolled with valid geocoordinates collected within the designated boundary, and whether the Euclidean distance of households were within 60 m of a predetermined starting point. A normalized difference vegetation index (NDVI) image ratio was conducted to further investigate variability in team performances. The study enrolled a total of 78% of the target sample with 52% of all households within 60 m of a pre-selected point and 79% within the designated cluster boundary. Success varied considerably between the four target areas ranging from 42% enrolment of the target sample in one place to 90% enrolment of the target sample in another. Interviews with the field teams revealed that differences in security status and amount of non-residential land cover were key barriers to higher enrolment rates. Our findings indicate household poliovirus seroprevalence surveys using GIS-based sampling can be effectively implemented in polio endemic countries to capture representative samples. We also proposed ways to achieve higher success rates if these methods are to be used in the future, particularly in areas with concerns of insecurity or spatially dispersed residential units.

서울시 도시형태 관련 지표가 토지가격에 미치는 영향

Urban forms have a significant effect on the quality of life, mobility patterns, land use, development and real estate prices as well as society, economy and environment. In spite of the significance, few studies have examined the effects of urban form indicators on real estate prices. This study measured twenty urban form indicators on five categories for Seoul in order to evaluate urban forms and the effects were investigated by land prices. The multi-level regression model was used to identify the effect on residential and non-residential land prices. As a result, residential and non-residential land prices were high in the cases of buildings with high density, street with excellent networks, higher building-to-land ratio, affordable clearance to next buildings and better land shapes. On the other hand, the prices were low in the cases of many neighboring parcels, overabundant buildings along street networks and distant clearance to next buildings. This study presented a model for urban forms to understand the change in urban space structures and anticipate the effect on land prices.

The most places at risk surrounding the COVID-19 treatment hospitals in an urban environment- case study: Tehran city

Investigations on the spatial patterns of COVID-19 spreading indicate the possibility of the virus transmission by moving infected people in an urban area. Hospitals are the most susceptible locations due to the COVID-19 contaminations in metropolises. This paper aims to find the riskiest places surrounding the hospitals using an MLP-ANN. The main contribution is discovering the influence zone of COVID-19 treatment hospitals and the main spatial factors around them that increase the prevalence of COVID-19. The innovation of this paper is to find the most relevant spatial factors regarding the distance from central hospitals modeling the risk level of the study area. Therefore, eight hospitals with two service areas for each of them are computed with [0–500] and [500–1000] meters distance. Besides, five spatial factors have been considered, consist of the location of patients’ financial transactions, the distance of streets from hospitals, the distance of highways from hospitals, the distance of the non-residential land use from the hospitals, and the hospital patient number. The implementation results revealed a meaningful relation between the distance from the hospitals and patient density. The RMSE and R measures are 0.00734 and 0.94635 for [0–500 m] while these quantities are 0.054088 and 0.902725 for [500–1000 m] respectively. These values indicate the role of distance to central hospitals for COVID-19 treatment. Moreover, a sensitivity analysis demonstrated that the number of patients’ transactions and the distance of the non-residential land use from the hospitals are two dominant factors for virus propagation. The results help urban managers to begin preventative strategies to decrease the community incidence rate in high-risk places.

서울시 지하철에 대한 가로망 접근성의 토지가격 효과 연구*

As metropolitan cities around the world face multiple urban issues, public transport attracts attention as a measure for sustainable development. Therefore, the effects of access to public transit on neighborhoods are important academic and policy subject. This study measures five accessibility indicators for the Seoul subway system, considering both the frequency of subway operation and the characteristics of the street network, and analyzes their effects on residential and non-residential land prices. The key findings of this study confirm that the five subway accessibility indicators have a positive effect on residential and non-residential land prices. In particular, they generate a high premium to the prices of non-residential land rather than residential land. In addition, the higher the operation frequency of the nearest subway station or the straighter the street network reaching the subway stations, the greater the land price premium. Further, a land that people are frequently passing through to access a station or has more routes to a station generates higher price premium. The results of this study provide a reference for understanding the effects of public transport services and predicting the response of neighborhood real estate markets to the service.

A comparison of a gradient boosting decision tree, random forests, and artificial neural networks to model urban land use changes: the case of the Seoul metropolitan area

ABSTRACT This study compares the performance of gradient boosting decision tree (GBDT), artificial neural networks (ANNs), and random forests (RF) methods in LUC modeling in the Seoul metropolitan area. The results of this study showed that GBDT and RF have higher predictive power than ANN, indicating that tree-based ensemble methods are an effective technique for LUC prediction. Along with the outstanding predictive performance, the DT-based ensemble models provide insights for understanding which factors drive LUCs in complex urban dynamics with the relative importance and nonlinear marginal effects of predictor variables. The GBDT results indicate that distance to the existing residential site has the highest contribution to urban land use conversion (30.4% of the relative importance), while other significant predictor variables were proximity to industrial and public sites (combined 32.3% of relative importance). New residential development is likely to be adjacent to existing residential sites, but nonresidential development occurs at a distance (about 600 m) from such sites. The distance to the central business district (CBD) had increasing marginal effects on residential land use conversion, while no significant pattern was found for nonresidential land use conversion, indicating that Seoul has experienced more population suburbanization than employment decentralization.

Conversion of industrial contaminated sites into residential areas –safeguarding health and wellbeing

Abstract Across Europe, urban settlements are changing and expanding. Due to lack of land resources, urban development often relies on the re-use and conversion of former industrial areas - land which has had special functions before but now is being used for urban, public, recreational and residential functions. This challenge is often referred to as land recycling or brownfield conversion, and is associated with urban processes labelled e.g. urban sprawl, renewal, revitalization, regeneration or redevelopment. Urban revitalization and the use of former non-residential land for urban functions raise potential challenges related to health, wellbeing and equity. The use of former industrial areas can be associated with contamination which, if not fully remediated, can pose health risks to new residents. With increasing urbanization in many countries there is a need for cities to provide new residential areas. The WHO Regional Office for Europe is developing a compilation of health impacts of such urban land-take processes - including a review of lessons learned and a collection of practical experiences - that can provide better understanding of the scope of the problem, related health challenges and guidance for action for countries and cities where such conversion projects take place. The work will contribute to achieving SDG 11 by discussion practical processes for multi-stakeholder engagement in all stages of urban development that build consensus, inclusion, resilience and sustainability when converting former industrial areas into residential areas. The presentation will focus on environmental, social and health implications and experiences of urban and residential use of land resources with formerly different functions. The presentation will include lessons learned regarding the assessment of risk associated with land contamination etc.

Neighborhood Effects on Crime in San Francisco: An Examination of Residential, Nonresidential, and “Mixed” Land Uses

ABSTRACT Communities and crime research presumes that purely nonresidential land uses are associated with more crime, whereas purely residential land use is associated with less crime. However, few studies have evaluated this prevailing assumption in tandem with the notion of “mixed” land use. Drawing on a sample of San Francisco census blocks, this study examines the neighborhood effects of several land use measures on crime counts using negative binomial regression. The results provide moderate support for the prevailing assumption; however, we also find that the effects of mixed land use are nuanced based on differing measurement approaches. Whereas two types of mixed-use buildings have differential effects on crime (i.e. commercial mixed-use and residential mixed-use) we determine that the amount of heterogeneity among eight specific land uses, next to or in direct proximity of each other, is consistently related to more crime. The implications of these findings for criminology and public policy are discussed.

Thematic Geovisualization of the Data Profile of Kaligesing, Purworejo, Central Java

The scientific field has a variety of purposes, one of which is the presentation of data and information which can be used by other parties to support their decision making. Moreover, the information is presented spatially. This research aims to map the data profile of Kaligesing district to establish the region’s potential through thematic geovisualization of its data profile, such as slopes, land use, livelihoods and population. The primary data were obtained from visual interpretation of remote sensing images to extract land use information, and DEM processing to extract slope information. Secondary data were provided by the Kaligesing district government. In order to build tiered spatial modelling, each thematic map was classified and weighted according to its contribution to the potential of the region. Based on this modelling, each village was given a compilation of weights, which were used as a basis for regional potential analysis. From the results of the thematic mapping, Kaligesing has three villages that have the potential for development in the agricultural, trade and service sectors, supported by the potential of human resources, and the abundant non-residential land available.

Integrating Activity-Based Geographic Information and Long-Term Remote Sensing to Characterize Urban Land Use Change

The land use structure is a key component to understand the complexity of urban systems because it provides a snapshot of urban dynamics and how people use space. This paper integrates socially sensed activity data with a remotely sensed land cover product in order to infer urban land use and its changes over time. We conducted a case study in the Washington D.C.–Baltimore metropolitan area to identify the pattern of land use change from undeveloped to developed land, including residential and non-residential uses for a period covering 1986–2008. The proposed approach modeled physical and behavioral features of land parcels from a satellite-based impervious surface cover change product and georeferenced Tweets, respectively. A model assessment with random forests classifiers showed that the proposed classification workflow could classify residential and non-residential land uses at an accuracy of 81%, 4% better than modeling the same land uses from physical features alone. Using the timestamps of the impervious surface cover change product, the study also reconstructed the timeline of the identified land uses. The results indicated that the proposed approach was capable of mapping detailed land use and change in an urban region, and represents a new and viable way forward for urban land use surveying that could be especially useful for surveying and tracking changes in cities where traditional approaches and mapping products (i.e., from remote sensing products) may have a limited capacity to capture change.

Mental Health, Substance Abuse, Co-occurring Disorders, and 3-Year Recidivism of Felony Parolees

Having a mental health, substance use, or co-occurring disorder (COD) can make successful reintegration a difficult process for returning offenders. However, our understanding of how these disorders varies as far as their impact on recidivism during the reentry stage is lacking. Using a sample of 4,381 parolees released to the City of Philadelphia, this study assessed how disorder type may influence repeat criminal offending by examining the independent effects of mental illness, substance abuse/dependence, and CODs on recidivism controlling for individual and neighborhood contextual factors and nonresidential land uses. In addition, we separated our measure of recidivism into two categories, re-incarceration for a new offense and re-incarceration for a parole violation to see whether different predictors emerged. Our findings showed that there were indeed differences according to disorder type, as well as between neighborhood variables and these findings also varied according to reason for incarceration. Moreover, we found evidence contrary to the assumption that COD offenders would have the worst outcomes due to the comorbid nature of their disorders.

Design and Development of Settlement Spatial Dynamics Model Using Dynamic and Neural Network Systems

The general objective of this study is to project the distribution of settlement land through the development and application of spatial dynamics models using dynamic system methods and neural networks, while the specific objectives are: 1) Compiling spatial data on land characteristics and evaluating settlement land in Maros Regency; 2) Multitemporal mapping of settlement and non-residential land in 2010, 2016 and 2017; 3) Compile Causal loop diagrams and model simulations to determine the dynamics of population projections and the size of residential land needs until 2038; 4) Synchronizing land requirements with the allocation of space utilization in Maros Regency Spatial Planning. 5) Design and build a spatial model to predict the development and direction of settlement land using change prediction of Artificial Neural Network (ANN); and map the projected spatial distribution model of residential land every 5 (five) years. This research conducted a recent approach to multi-temporal observation and spatial optimization studies by integrating dynamic systems with neural network methods in solving settlement land management problems. The approach with the population growth projection model relates to the spatial dynamics of residential land use using the neural network to facilitate policy makers in decision making for settlement land management. The method used consists of 5 stages: stage I. analysis of land characteristics and land use of multi-temporal settlements, through land surveys, laboratory analysis, and spatial characteristics of land; then the land suitability analysis uses matching method; Phase II: mapping multi-temporal land use in 2010, 2016 and 2017 using analysis and extraction of satellite image information. The image used is Landsat medium resolution satellite imagery; stage III: arranging units and interactions and behaviour between units in a system of population growth dynamics, then making causal loop diagrams and then implementing dynamic software systems to simulate population growth used and residential land needs every five years; stage IV: testing the synchronization of the allocation of space utilization in the Spatial Palnning document of the District with the settlement land resulting from the projection; stage V: build a residential land use projection model which begins with change analysis, transition potential, and change prediction with the neural network method; then the projection result of settlement land needs is mapped every five years interval from 2018 - 2038 based on the results of the analysis. The implication of the results of the study will provide an overview of the harmony between the pattern of space and the trend of the development direction of the settlement, so that the resulting method can be used as a basis for revising the Spatial Planning in the future.

Understanding the Differential Effects of Land Uses On Crime: An Examination Across Philadelphia Neighbourhoods

Abstract Although criminology presumes that the physical structural qualities of neighbourhoods have important consequences for crime, there is a dearth of research to test the effects of different land uses on crime rates. Using data on a sample of Philadelphia census blocks, I compute the percentage of the block area classified into seven land use categories, and I use Poisson models to test the localized and broader spatial impact of each of these land uses on crime, controlling for key sociodemographic characteristics. The results show that to effectively understand the interplay between physical structure and crime rates, researchers should disaggregate nonresidential land uses, account for the surrounding environment in which neighbourhoods are situated and analyse a specific form of moderation.

Urban Form, Growth, and Accessibility in Space and Time: Anatomy of Land Use at the Parcel-Level in a Small to Medium-Sized American City

Using the City of Corvallis, Oregon, a small to medium sized American city, as a test-bed, this paper examines the City’s urban growth in relation to urban accessibility. This relationship is explored in an anatomic spatial-temporal fashion, taking account of: the number and size of developed land use parcels over time; urban accessibility from residential to non-residential land use areas; and the statistical relationships between urban form and urban accessibility. This investigation of land use is structured around use-classification and examined within a range of dimensional and demographic measurements over 5-year time periods from 1853 to 2014; concurrently, urban accessibility is measured by the least-cost path distance as calculated through the OD cost matrix analysis in GIS. The results indicate that the city grew spatially at different rates and its urban accessibility experienced both ups and downs over time. The city’s population growth corresponded closely with urban growth and its decreasing population density negatively impacted on the city’s urban accessibility to commerce, industry, and office for most time periods. Significantly, while the urban density increased steadily after 1950s concurrent with an increase in urban sprawl, in contrast to previous studies on the metropolitan condition, the urban density had no evident impact on urban accessibility in Corvallis. Instead, increasing the land-use mix was a more effective and feasible approach to reduce urban travel path distance and enhance accessibility than increasing population density or urban development density. Accordingly, this research provides evidence-based policy recommendations for planning sustainable urban mobility and urban form in small to medium-sized cities.

Household-Level Population Estimates and Building Setbacks near Underground Natural Gas Storage Wells in PA, OH, NY, WV, MI, and CA

Health impacts have been reported in people living near oil and gas operations (e.g., asthma exacerbation). Towards assessing these risks, studies quantifying proximate populations generally assume an even spatial distribution of population within an administrative unit (e.g., census block). These proportion population allocation (PPA) methods can be improved by excluding non-residential land use areas (i.e. dasymetric mapping), but are still too imprecise to obtain reliable estimates of population densities at small spatial scales (e.g., 50m2). Here we present ABODE (Allocation By Occupied Domicile Estimation), a novel household-level population estimation method that combines geospatial addresses, building footprints, and U.S. Census data. We assess ABODE and PPA methods by comparing population estimates within 200m of active underground natural gas storage (UGS) wells (n = 8,963) in six U.S. states. Overall, ABODE predicts slightly higher population estimates compared to PPA (48,126 vs. 43,052); however estimation bias varied by state and by individual well. ABODE captured higher populations within 200m of UGS wells in OH, WV, and PA with increases of 6, 54, and 66% compared to the PPA method, respectively. Conversely, the ABODE method captures fewer populations in NY, MI, and CA, with decreases of 4, 7, and 26%, respectively. By explicitly identifying households near active UGS wells in the six states, we also quantify well-to-building relationships and compare to state surface setback regulations where they exist. An estimated 9.4% (840) of UGS wells across the six states have at least one building within a regulated setback distance. This results in 2,769 households, or an estimated 4,816 people, living within these states’ respective setbacks (e.g., 100 - 625ft.). The spatially-resolved nature of the ABODE method provides a clear advancement from previous proximity-based methods towards understanding present and future land-use conflicts.

Low-Income Housing and Crime: The Influence of Housing Development and Neighborhood Characteristics

This study examines the distribution of crime across various types of low-income housing developments and estimates the main and interactive effects of housing development and neighborhood characteristics on crime. Negative binomial regression models were estimated to observe the influence of security and design features, neighborhood concentrated disadvantage, residential stability, and nearby nonresidential land use on crime at the housing developments. The findings suggest that low-income housing developments are not uniformly criminogenic, and both development characteristics and neighborhood conditions are relevant for understanding crime in low-income housing developments. Implications for prevention are discussed.

Influence of on-network, traffic, signal, demographic, and land use characteristics by area type on red light violation crashes

The focus of this research paper is on extraction of predictor variables pertaining to on-network, traffic, signal, demographic, and land use characteristics, by area type, and examining their influence on the number of red light violation crashes. Data for the city of Charlotte, North Carolina was extracted and used for analysis. Three different sets of signalized intersections were selected in the three different area types - Central Business District (CBD), urban, and suburban areas. Each set is comprised of sixty signalized intersections (total 180 signalized intersections). The number of red light violation crashes from January 2010 to December 2014, within the vicinity of each selected signalized intersection, was considered as the dependent variable to develop crash estimation models for each area type. The crash estimation models by area type were compared with the crash estimation model developed considering all the 180 signalized intersections together. Different predictor variables were found to be significant at a 95% confidence level in three different areas. Log-link model with Negative Binomial distribution was observed to best fit the data used in this research. Findings indicate that enforcement, either manually or using red light running cameras (RLCs), at signalized intersections with high traffic volume in the CBD area; at signalized intersections with high traffic volume, high all-red clearance time, near high density of horizontal mixed non-residential and open space/recreational type land uses in urban area; at signalized intersections with high traffic volume, speed limit on the major approach, the number of lanes on the minor approach, and all-red clearance time and areas surrounded with horizontal mixed non-residential and retail type land use in suburban areas, would lead to a reduction in the number of red light violation crashes.