Value In Neighborhood Research Articles

Redlining, greenlining: Discourses of race and nature in Home Owners Loan Corporation neighborhood appraisals

Studies drawing correlations between Home Owners Loan Corporation “redlining” maps and present-day environmental outcomes have raised public attention about the role of systemic racial discrimination in producing uneven outcomes in the urban environment. In this analysis, we draw on a novel data source—narrative descriptions of neighborhood characteristics included in the area descriptions that accompanied City Survey maps—to explore the mechanisms that historically link HOLC maps and disparate environmental quality. Focusing on nine mid-sized cities in the Midwest, we draw on a combined inductive and deductive coding approach to consider how perceived urban environmental quality and neighborhood racial demographics shaped assessments of neighborhood value and risk. In doing so, we illustrate the ways in which nature, class, and race were already coupled at the time of the HOLC City Survey. The area descriptions in the HOLC City Survey illustrate the ways in which this coupling became codified and reinforced over time through housing and environmental policies, the impacts of which we still struggle to disrupt today.

Implementasi Metode K-Nearest Neighbor Untuk Klasifikasi Penyakit Tanaman Mentimun Pada Citra Daun

Cucumber is a vegetable that is widely consumed by Indonesian people. However, cucumber plants are susceptible to disease attack which causes substantial yield loss. Examples of disease in cucumber plants are downy mildew, powdery mildew, and cucumber mozaic virus. This disease can be recognized visually because it has a characteristic color and texture. Through an image, information can be learned about the cucumber plant disease. This study aims to build a disease classification system on cucumber leaf images so that it can provide information on the type of disease. The application of the system consisting of pre-processing, feature extraction, classification, and evaluation stages. The pre-processing stages resizes the RGB image and then converts it to Grayscale. The feature extraction stage uses the GLCM (Gray Level Co-Occurence) method. The classification stage uses the K-NN (K-Nearest Neighbor) algorithm. Evaluation stage is a confusion matrix. The results of the cucumber leaf disease classification test used the K-Nearest Neighbor algorithm, produced the best accuracy value by using the neighborhood value k=1 reaching 90%.

How Learning from Informal Settlements Contributes to the Community Resilience of Neighbourhoods

Urbanization in the twenty-first century has experienced a significant slowdown, particularly in the Global North. However, population growth continues to escalate, with most of this increase occurring in the Global South. Most of that growth manifests in the urban environment as informal settlements. These types of neighbourhoods deploy unique strategies to emerge and grow and can teach us much about the value of neighbourhoods as urban units in an uncertain future plagued by the challenges imposed by climate change, political polarization, and urban conflict. However, literature on neighbourhoods devotes little time to exploring these urban manifestations as areas of exploration and learning; instead, most of the scholarship focuses on ways to eradicate these places from our cities. As a result of this perspective, I argue that we have overlooked three important lessons that can be gleaned from informal settlements: incrementality, sustainability, and self-reliance. These characteristics contribute to the resilience of the communities, making informal settlements the predominant neighbourhoods of the twenty-first century. This paper examines global informal neighbourhood practices, unveiling unique community strategies. It reassesses neighbourhood value and offers insights into the urban changes necessary to tackle the next century's challenges.

KNN Algoritması Uzaklık Metrik Yöntemlerinin Buğday Tohumları Veri Seti Üzerinde Sınıflandırma Başarısının Tespit Edilmesi

Machine learning algorithms are widely used in product sorting processes in the food industry. The 
 attributes of the products are used in the classification process. Attributes vary for each product. In this 
 study, using the k nearest neighbor (KNN) algorithm, the classification of the wheat groups of Kama, 
 Rosa and Canada was performed. The Seeds dataset provided in UCI (University of California, Irvine) 
 machine learning open source data storage was used. There are 70 examples of each wheat class in the 
 data set. In addition, the classification estimation success of distance metrics and the number of training 
 data was measured. Each of the wheat samples was randomly selected and a soft X-ray technique was 
 used to visualize the inner core structure of the wheat in the experimental environment with high 
 quality. According to the training rates ranging from 50% to 90% of the data set, the classification 
 success of the KNN algorithm was tested. In the KNN algorithm, the neighborhood values 1, 3 and 5 
 were selected to affect the classification success. The successes of the Euclidean, Chebyshev, 
 Manhattan and Mahalanobis distance metric methods of the KNN algorithm were tested according to 
 each k neighborhood value. According to the results obtained, with the Mahalanobis metric method, a 
 classification success rate of 0.9924 accuracy was obtained according to the AUC (Area Under the Curve) 
 success metric by using the neighborhood value of k = 3. In the literature, there is no study comparing 
 the KNN algorithm, neighborhood values and distance vectors together on food data sets using varying 
 training and test data. Therefore, it is thought that the study will make an important contribution to 
 the literature.

Dense gas and star formation in the outer Milky Way

We present maps and spectra of the HCN(1−0) and HCO+(1−0) lines in the extreme outer Galaxy, at galactocentric radii between 14 and 22 kpc, with the 13.7 m Delingha telescope. The nine molecular clouds were selected from a CO/13CO survey of the outer quadrants. The goal is to better understand the structure of molecular clouds in these poorly studied subsolar metallicity regions and the relation with star formation. The lines are all narrow, less than 2 km s−1 at half power, enabling the detection of the HCN hyperfine structure in the stronger sources and allowing us to observationally test hyperfine collision rates. The hyperfine line ratios show that the HCN emission is optically thin with column densities estimated at N(HCN) ≈ 3 × 1012 cm−2. The HCO+ emission is approximately twice as strong as the HCN (taken as the sum of all components), in contrast with the inner Galaxy and nearby galaxies where they are similarly strong. For an abundance ratio χHCN/χHCO+ = 3, this requires a relatively low-density solution for the dense gas, with n(H2) ~ 103−104 cm−3. The 12CO/13CO line ratios are similar to solar neighborhood values, which are roughly 7.5, despite the low 13CO abundance expected at such large radii. The HCO+/CO and HCO+/13CO integrated intensity ratios are also standard at about 1/35 and one-fifth, respectively. The HCN is weak compared to the CO emission, with HCN/CO ~ 1 /70 even after summing all hyperfine components. In low-metallicity galaxies, the HCN deficit is attributed to a low [N/O] abundance ratio; however, in the outer disk clouds, it may also be due to a low-volume density. At the parsec scales observed here, the correlation between star formation, as traced by 24 μm emission as is standard in extragalactic work, and dense gas via the HCN or HCO+ emission is poor, perhaps due to the lack of dynamic range. We find that the lowest dense gas fractions are in the sources at high galactic latitude (b > 2°, h ≳ 300 pc above the plane), possibly due to lower pressure.

Zero-Inflated Time Series Clustering Via Ensemble Thick-Pen Transform

This study develops a new clustering method for high-dimensional zero-inflated time series data. The proposed method is based on thick-pen transform (TPT), in which the basic idea is to draw along the data with a pen of a given thickness. Since TPT is a multi-scale visualization technique, it provides some information on the temporal tendency of neighborhood values. We introduce a modified TPT, termed ‘ensemble TPT (e-TPT)’, to enhance the temporal resolution of zero-inflated time series data that is crucial for clustering them efficiently. Furthermore, this study defines a modified similarity measure for zero-inflated time series data considering e-TPT and proposes an efficient iterative clustering algorithm suitable for the proposed measure. Finally, the effectiveness of the proposed method is demonstrated by simulation experiments and two real datasets: step count data and newly confirmed COVID-19 case data.

Topographic Position Index to Landform Classification and Spatial Planning, Using GIS, for Wadi Araba, South West Jordan

This study aimed to carry out a classification of landforms in Wadi Araba, southwest Jordan, using the topographic Position Index (TPI) by GIS. TPI was used at different spatial scales (mean, 50 m, 75 m, 100 m, 200 m, 500 m, 1000 m, 1500 m, and 2000 m) for such classification. Landform classification goes by ten levels: Valleys Bottoms, Midslope Drainage, Upland Drainages, Gentle Valleys, Plains, Open Slopes, Upper Slopes, Local Ridges, Midslope Ridges, and High Ridges. This requires defined classes using the TPI for all scales. The analysis showed that there were ten types of landforms; most of them were ridges, valleys, and steep slopes. In addition, the proportions of these types varied according to the variation of indicator values in the scale. Landform map according to the Neighborhood Values mean (m) showed that areas with upland drainages were about 14%, while areas with Gentle Valleys represented about 5.52%, of the total area of the study area. Finally, it was found possible to rely on the classification maps of the landforms for the purpose of spatial planning, as this included determining the land uses that are appropriate for each landform, as well as determining the levels of risk for landslides and floods.

Neighborhood deprivation and residential property values do not affect weight loss at 1 or 3 years after bariatric surgery.

This study examined the association between individual- and neighborhood-level sociodemographic factors and surgical weight loss at 1 year (short term) and 3 years (long term). Data were obtained from the baseline survey of the BELONG (Bariatric Experience Long Term) prospective longitudinal cohort study. Individual-level self-reported data on sex, race and ethnicity, education, and household income were obtained by survey. Data from the 2010 US Census were used to calculate area Neighborhood Deprivation Index score and median value of owner-occupied housing units at the census tract level. Patients (N=1341) had a mean age of 43.4 (SD 11.3) years, were mostly female (86%), were mostly Black or Hispanic (52%), had some college education (83%), and had annual household incomes ≥$51,000 (55%). Percentage total weight loss was 25.8% (SD 9.0%) at year 1 and 22.2% (SD 10.5%) at year 3. Race and ethnicity and age were significant predictors of weight loss at 1 and 3 years with a small effect of self-reported household income at year 1. There were no significant associations between census tract-level Neighborhood Deprivation Index score or value of owner-occupied housing units and weight loss at either time point. Health systems could improve the chances of weight-loss maintenance after surgery by addressing factors related to racial and ethnic disparities and to income disparities.

Green Walls and Public Healthcare Facilities in Port Harcourt Metropolis, Nigeria

Generally, the architectural design and physical development of green walls in urban hemisphere has gone beyond aesthetics and traditional values. However, the multifunctional approach and quality healthcare contributions of green infrastructure are perhaps the contemporary trend urban dwellers value most. The economic, social, environmental and healthcare understanding of urban green spaces is on increase, as many environmental advocates affirmed that urban green spaces enhance sustainable city development. Conversely, this article concentrated more on public healthcare services or provision of green spaces in Nigeria urban environment, using Port Harcourt as a situation study. The healthcare benefit, environmental protection and sustainable city ideology of greens are imperative, but many have hardly been given any significant attention for smart city growth or development. Distribution of questionnaire for field survey constitute the study instrument while Statistical Package for Social Scientist (SPSS) served as the data analytical method and 5% of The sample was also derived from onscreen digitization of the neighbourhoods under investigation and the findings showed that green space sustain biodiversity, densification, environmental degradation, human health/wellbeing and mitigation against climate change are the healthcare services green spaces provide to the urban environment. However, population increase, urbanisation, poor physical planning/ zoning and government policy are the major determinants for the damages urban green spaces recorded as a healthcare facility. The analysis maintained that absences of green spaces in urban environment are responsible for poor urban morphology, loss of resilience/smart city, quality air, depreciation of property and neighbourhood value etc. As usage understanding, budget constraint and inappropriate cost benefit analysis are the challenges of urban green spaces in developing cities. The recommendations made by the research include the adoption of green space creation exercise. The minimum of two green spaces should be created and maintained per urban street schemes, participation of urban residents, corporate and non-government organisations in urban green spaces planning, development and the inspiration of mini green space for individual building plans among others. This will enhance sustainable urban healthcare facilities and development in Nigeria.

Values of the New Urbanist Neighborhood Planning Based on the Concept of Sustainability

Values of the New Urbanist Neighborhood Planning Based on the Concept of Sustainability

The circuit realization of a fifth‐order multi‐wing chaotic system and its application in image encryption

SummaryIn this paper, a new fifth‐order eight‐wing chaotic system is proposed on the basis of a five‐dimensional four‐wing chaotic system. The resulting new system is used as a basis for introducing a method that can control both the number of wings of the system and the direction of the change in the number of wings. Add a rounding function with a cosine function or a sine function to the system equation. The number of wings of the control system is controlled by changing the parameter values of the rounding function. This method can significantly increase the complexity of the system when applied in multiple directions of the system equations. And this method can achieve the purpose of controlling the wing number of the whole system by changing only one parameter. By observing the phase diagram and bifurcation diagram of the system, it is found that it has more complex chaotic properties and rich dynamical behavior and there are coexisting attractors. The chaotic properties of the system are confirmed by observing the Poincaré cross section and the 0–1 test results of the system. Afterward, the simulation circuit of the fifth‐order system is built in the simulation software Multisim. And the obtained phase diagram is consistent with the results obtained in MATLAB which confirmed the realizability of the system. An FPGA hardware implementation of this fifth‐order eight‐wing system has also been completed at the end of the paper to demonstrate the implementability of the system. And the results are highly consistent with those obtained in the MATLAB software.The fifth‐order chaotic system is combined with the dislocation algorithm and diffusion algorithm to apply to the image encryption system. The encryption system contains a variety of encryption algorithms for secondary encryption of images including Arnold dislocation and repeat‐free scrambling algorithm. The diffusion algorithm includes the addition of the modulus length diffusion and multiplicative operation diffusion based on the GF(257) domain. After a series of performance analysis, the encryption system is proved to have good secrecy effect and great application value in the image encryption neighborhood.

A matching method for land valuation

One approach to land valuation, particularly used by accessors, is to base price estimates of target properties on comparable properties that recently sold. These properties are chosen to be close matches of the target unit and their transaction prices are used to predict the market price of the target unit. But the choice of comparables is typically not consistent and transparent. In this study, a systematic analytical procedure for choosing comparables that is easy to implement is developed. A hedonic regression using these comparables is then run and the predicted value of the target unit is the assessed value. One of the advantages of this procedure is that it should be straightforward for assessors and public finance officials to use and understand and easy to explain to residents.This approach is used to estimate land value as well as market prices (that includes the value of the structure) for single family residential properties using data from Maricopa County Arizona from 2007-2018. The best estimators obtain a median prediction accuracy error of 10% and more than 60% of these predictions have a prediction accuracy error within 10% for later years in the sample. These are within the bounds obtained by Zillow for 666 U.S. counties. Market prices are disaggregated into structure, lot, and neighborhood values. On average, these three components make up approximately 30%, 20%, and 50% of total average price. This provides for a nice “rule of thumb” for decomposing the market average property value into these three components; two of which relate to land value.

The urban exposome and type 2 diabetes: A deep learning approach

BACKGROUND AND AIM: Type 2 diabetes (T2D) is a chronic disease with high individual and societal burden. Risk of T2D is partly influenced by urban characteristics, such as air pollution, or indirectly through their relation with lifestyle behaviours (e.g. walkability, food environment). Environmental characteristics are generally studied individually in their association with T2D, but occur simultaneously in real life, and may associate with T2D in nonlinear and non-additive ways. We aimed to identify which factors of the urban exposome are associated with T2D by applying artificial neural networks (ANN). In addition, we compared results of ANN model with penalized regression LASSO, as a more conventional method. METHODS: We analyzed baseline data from 14,829 participants of the Occupational and Environmental Health Cohort study living across the Netherlands. Self-reported questionnaire data were used to identify participants diagnosed with T2D (n=676(4.6%)). Exposome variables (n=86) were linked to individual home addresses, including air pollution, traffic noise, green-space, chemicals in drinking water, built environmental and neighborhood socio-demographic characteristics. Models were adjusted for individual socio-demographic variables. Nested cross-validation was used to determine the optimal model parameters of both approaches (ANN and LASSO), and the cross-validated predictive accuracies were compared. RESULTS: One exposure was selected by each approach. Living in neighborhoods with a higher share of non-Western immigrants (selected by ANN) was associated with a higher risk of T2D. Higher average home value in residential neighborhood (selected by LASSO) was associated with a lower risk of T2D. Cross-validated prediction error logLoss (sd) was 0.177 (0.0060) for ANN and 0.167 (0.0022) for LASSO. CONCLUSIONS: Neighborhood socio-demographic characteristics are associated with the risk of T2D. Accuracy of the ANN model was lower than LASSO, which might be due to the low prevalence of diabetes and relatively weak signal in the data. KEYWORDS: Multiple exposures, Machine learning, Neighborhood socio-demographic characteristics

Developmental language disorder in sequential bilinguals: Characterising word properties in spontaneous speech.

The current study sought to investigate whether word properties can facilitate the identification of developmental language disorder (DLD) in sequential bilinguals by analyzing properties in nouns and verbs in L2 spontaneous speech as potential DLD markers. Measures of semantic (imageability, concreteness), lexical (frequency, age of acquisition) and phonological (phonological neighbourhood, word length) properties were computed for nouns and verbs produced by 15 sequential bilinguals (5;7) with DLD and 15 age-matched controls with diverse L1 backgrounds. Linear mixed modelling revealed a significant interaction of group and word category on phonological neighbourhood values but no differences across imageability, concreteness, frequency, age of acquisition, and word length measures in spontaneous speech. Outcomes suggest that group-level differences may not be apparent at the word-level, due to the heterogeneous nature of DLD and potential similarities in production during early L2 acquisition.

Flood forecasting in urban reservoir using hybrid recurrent neural network

Flood forecasting can provide accurate inferences and early warnings for flood control work during the flood season. Due to the variability of local rainfall and the complexity of geographic conditions, existing prediction methods were unable to accurately predict the flooding process in a particular basin. Additionally, the water level sensor generates a significant amount of noise in the inbound flow data during period measurement. To address these issues, this article proposes a real-time flood forecasting model, which is used to accurately predict flood trends and peak times in the flood period. The model uses a convolution kernel function to smooth out local noise and neighborhood values, minimizing the impact of non-stationary series on the training process while retaining the true evolution of the flood in the original data. In our model, we develop a time series attention mechanism that is used to apply various weights to time series input vectors, such as outflow flow and rainfall from upstream reservoirs, this mechanism also improves the accuracy of short-term series prediction. To obtain additional information about the output of the recurrent neural network layer, we also include a multivariate autoregressive integrated moving average module. This method can add a linear component to the output, allowing the prediction result to adapt to the input period's scale shift. This article develops matching models for interval and full basin floods based on the geographical characteristics of China's urban Reservoir and the river basin, thresholds are established based on the outflow from upstream reservoirs, which enables the flood forecasting system to dynamically adjust model parameters in response to the threshold, it also circumvents the scaling problem inherent in flood time series at various scales. We trained and predicted using 25 different types of floods in Ankang Reservoir from 2010 to 2020. Three on-site real-time forecasts of catastrophic flooding at the Ankang Reservoir were conducted in September 2021 to validate the model's accuracy. The algorithm's efficiency in forecasting flood inflows is demonstrated through comparisons to traditional hydrological models and other machine learning networks, and our model consistently forecasts the peak time and total flood volume with the least amount of error in the comparison algorithm.

Methodology to filter out outliers in high spatial density data to improve maps reliability

The considerable volume of data generated by sensors in the field presents systematic errors; thus, it is extremely important to exclude these errors to ensure mapping quality. The objective of this research was to develop and test a methodology to identify and exclude outliers in high-density spatial data sets, determine whether the developed filter process could help decrease the nugget effect and improve the spatial variability characterization of high sampling data. We created a filter composed of a global, anisotropic, and an anisotropic local analysis of data, which considered the respective neighborhood values. For that purpose, we used the median to classify a given spatial point into the data set as the main statistical parameter and took into account its neighbors within a radius. The filter was tested using raw data sets of corn yield, soil electrical conductivity (ECa), and the sensor vegetation index (SVI) in sugarcane. The results showed an improvement in accuracy of spatial variability within the data sets. The methodology reduced RMSE by 85 %, 97 %, and 79 % in corn yield, soil ECa, and SVI respectively, compared to interpolation errors of raw data sets. The filter excluded the local outliers, which considerably reduced the nugget effects, reducing estimation error of the interpolated data. The methodology proposed in this work had a better performance in removing outlier data when compared to two other methodologies from the literature.

Priority Modeling for Public Urban Park Development in Feasible Locations using GIS, Intuitionistic Fuzzy AHP, and Fuzzy TOPSIS

As feasible locations of public urban park in Bogor Municipality have been acquired in a previous study, decision makers are urgently needed to be informed on which locations should be prioritized for public urban park (PUP) development. Therefore, this study aggregates four multi-spatial criteria for PUP development priority modeling, namely distance to slum neighborhood, accessibility, slope, and land value. These four criteria in form of vector datasets were weighted using intuitionistic fuzzy analytical hierarchy process (IF-AHP) to consider the hesitancy, vagueness, and fuzziness might arise from experts’ judgement as well as from multi-spatial data processing. Resulted criteria weights from IF-AHP show that accessibility weight 0.261, land value weight 0.259, distance to slum weight 0.255, and slope weight 0.225, respectively. Criteria weights were inputted into fuzzy technique for order preference by similarity to the ideal solution (TOPSIS) and geographic information system (GIS) to rank location priority. Results from fuzzy TOPSIS show that very high priority class which has the biggest CCi values range (0.654-0.76) provides 0.14 km2 area of feasible PUP development scattered in 10 locations. The biggest area for feasible PUP development is generated by medium priority class (CCi values 0.439-0.546) in 26 locations and approximately area of 0.38 km2.

Is the molecular KS relationship universal down to low metallicities?

ABSTRACT In recent years, it has been speculated that in extreme low-metallicity galactic environments, stars form in regions that lack H2. In this paper, we investigate how changing the metallicity and ultraviolet (UV) field strength of a galaxy affects the star formation within, and the molecular gas Kennicutt–Schmidt (KS) relation. Using extremely high-resolution arepo simulations of isolated dwarf galaxies, we independently vary the metallicity and UV field to between 1 per cent and 10 per cent solar neighbourhood values. We include a non-equilibrium, time-dependent chemical network to model the molecular composition of the interstellar medium and include the effects of gas shielding from an ambient UV field. Crucially, our simulations directly model the gravitational collapse of gas into star-forming clumps and cores and their subsequent accretion using sink particles. In this first publication, we find that reducing the metallicity and UV field by a factor of 10 has no effect on star formation and minimal effect on the cold, dense star-forming gas. The cold gas depletion times are almost an order of magnitude longer than the molecular gas depletion time due to the presence of star formation in H i dominated cold gas. We study the H2 KS relationship that arises naturally within the simulations and find a near-linear power-law index of N = 1.09 ± 0.014 in our fiducial $10{{\ \rm per\ cent}}$ solar metallicity model. As the metallicity and UV field are reduced, this becomes moderately steeper, with a slope of N = 1.24 ± 0.022 for our $1{{\ \rm per\ cent}}$ solar metallicity and $1{{\ \rm per\ cent}}$ solar UV field model.

Machine learning approaches to characterize the obesogenic urban exposome

BackgroundCharacteristics of the urban environment may contain upstream drivers of obesity. However, research is lacking that considers the combination of environmental factors simultaneously. ObjectivesWe aimed to explore what environmental factors of the urban exposome are related to body mass index (BMI), and evaluated the consistency of findings across multiple statistical approaches. MethodsA cross-sectional analysis was conducted using baseline data from 14,829 participants of the Occupational and Environmental Health Cohort study. BMI was obtained from self-reported height and weight. Geocoded exposures linked to individual home addresses (using 6-digit postcode) of 86 environmental factors were estimated, including air pollution, traffic noise, green-space, built environmental and neighborhood socio-demographic characteristics. Exposure-obesity associations were identified using the following approaches: sparse group Partial Least Squares, Bayesian Model Averaging, penalized regression using the Minimax Concave Penalty, Generalized Additive Model-based boosting Random Forest, Extreme Gradient Boosting, and Multiple Linear Regression, as the most conventional approach. The models were adjusted for individual socio-demographic variables. Environmental factors were ranked according to variable importance scores attributed by each approach and median ranks were calculated across these scores to identify the most consistent associations. ResultsThe most consistent environmental factors associated with BMI were the average neighborhood value of the homes, oxidative potential of particulate matter air pollution (OP), healthy food outlets in the neighborhood (5 km buffer), low-income neighborhoods, and one-person households in the neighborhood. Higher BMI levels were observed in low-income neighborhoods, with lower average house values, lower share of one-person households and smaller amount of healthy food retailers. Higher BMI levels were observed in low-income neighborhoods, with lower average house values, lower share of one-person households, smaller amounts of healthy food retailers and higher OP levels. Across the approaches, we observed consistent patterns of results based on model’s capacity to incorporate linear or nonlinear associations. DiscussionThe pluralistic analysis on environmental obesogens strengthens the existing evidence on the role of neighborhood socioeconomic position, urbanicity and air pollution.

Do Places Have Value?: Quantifying the Intrinsic Value of Housing Neighborhoods Using MGWR

Real estate market analysis has long been an active area of inquiry and one that reveals much about people’s preferences regarding housing attributes. It is well-known that house prices tend to exhibit strong spatial dependency and that they vary across space due to differences in structural and neighborhood characteristics. It is perhaps less well-known but gaining recognition that the influence of various structural and neighborhood characteristics on house prices might vary over space. However, very few, if any, applications in real estate research have recognized and measured the spatial scales over which different factors affect house prices or been able to quantify the ‘intangible’ impacts certain locations have on house prices. Using house price data in King County, WA, this research applies a multiscale extension to GWR, multiscale geographically weighted regression (MGWR), to measure and investigate spatial variations in the processes affecting house prices at varying scales. In a novel attempt, this research quantifies the intrinsic value certain locations have beyond the determinants used to define traditional hedonic price models. The research also demonstrates the utility of MGWR to hedonic price analysis and its ability to identify intricate housing submarkets often overlooked by other techniques.