Urban land value maps - a methodological approach

Land value maps are generally used in mass appraisal for\ud the determination of property taxes. In view of the complex\ud nature of property management processes, land value maps\ud may also serve a variety of purposes which are not dictated\ud by legal requirements. This study proposes a concept for the\ud development of a land value map which may be applied for\ud non-tax purposes. The proposed map was developed with the\ud use of statistical and geostatistical methods. A reference layer\ud corresponding to a representative property was developed,\ud and statistical models were used to determine coefficients that\ud adjust property value in view of its non-spatial attributes.\ud A theoretical concept was presented, and it was used to\ud develop a land value map for the city of Olsztyn in northeastern\ud Poland

Internalizing the social costs of smoke emissions into strategic fuels planning models

2016 Summer.Includes bibliographical references.Emissions of fine particulate matter from prescribed burns are a growing concern for wildland fire managers. Stringent air quality regulations and community discern over the emissions from prescribed fire smoke often severely restrict the ability to implement restorative and precautionary fuels treatments. While some extent of emissions are unavoidable, strategic planning can help reduce their impacts. Estimating the cost of smoke and incorporating it into landscape level fire planning may reduce the burden on wildland fire officials confronted with a complex set of choices and constraints. Currently, no decision-support systems are available for strategically incorporating the cost of smoke in fire planning at the landscape level. A decision model is developed to address this void by estimating the value of fire and fuels management at the landscape level by including the cost of smoke in cellular level estimates social returns. By working with locally defined emission standards and translating them into a cost per unit of smoke impact, I was able to internalize the external impact of smoke emissions into a strategic fuels planning model by reprioritizing the optimal selection of landscape grid cells to target for prescribed fire investments. This has the potential to aid the fire planner in analyzing trade-offs for prescribed fire management. In a case study at King's Canyon National Park, emissions standards are used to estimate a relative unit cost of impact (per unit of emissions). The unit cost is subtracted from cellular estimates of marginal social returns to re-prioritize the spatial design of landscape scale fuel treatments

Racial differences in the built environment—body mass index relationship? A geospatial analysis of adolescents in urban neighborhoods

Background: Built environment features of neighborhoods may be related to obesity among adolescents and potentially related to obesity-related health disparities. The purpose of this study was to investigate spatial relationships between various built environment features and body mass index (BMI) z-score among adolescents, and to investigate if race/ethnicity modifies these relationships. A secondary objective was to evaluate the sensitivity of findings to the spatial scale of analysis (i.e. 400- and 800-meter street network buffers). Methods: Data come from the 2008 Boston Youth Survey, a school-based sample of public high school students in Boston, MA. Analyses include data collected from students who had georeferenced residential information and complete and valid data to compute BMI z-score (n = 1,034). We built a spatial database using GIS with various features related to access to walking destinations and to community design. Spatial autocorrelation in key study variables was calculated with the Global Moran’s I statistic. We fit conventional ordinary least squares (OLS) regression and spatial simultaneous autoregressive error models that control for the spatial autocorrelation in the data as appropriate. Models were conducted using the total sample of adolescents as well as including an interaction term for race/ethnicity, adjusting for several potential individual- and neighborhood-level confounders and clustering of students within schools. Results: We found significant positive spatial autocorrelation in the built environment features examined (Global Moran’s I most ≥ 0.60; all p = 0.001) but not in BMI z-score (Global Moran’s I = 0.07, p = 0.28). Because we found significant spatial autocorrelation in our OLS regression residuals, we fit spatial autoregressive models. Most built environment features were not associated with BMI z-score. Density of bus stops was associated with a higher BMI z-score among Whites (Coefficient: 0.029, p < 0.05). The interaction term for Asians in the association between retail destinations and BMI z-score was statistically significant and indicated an inverse association. Sidewalk completeness was significantly associated with a higher BMI z-score for the total sample (Coefficient: 0.010, p < 0.05). These significant associations were found for the 800-meter buffer. Conclusion: Some relationships between the built environment and adolescent BMI z-score were in the unexpected direction. Our findings overall suggest that the built environment does not explain a large proportion of the variation in adolescent BMI z-score or racial disparities in adolescent obesity. However, there are some differences by race/ethnicity that require further research among adolescents

Sea Level Rise Impacts on the City of Cape Coral, Southwest Florida from 2020 to 2050

Sea level rise, a consequence of global climate change, has been affecting the U.S. coasts with flooding and exacerbated storm surges. Florida is highly vulnerable because it has low-lying topography and coastlines on both the Atlantic Ocean and the Gulf of Mexico. The City of Cape Coral, Southwest Florida, is known as a ‘waterfront wonderland’ with 400 miles of canals that provide waterfront property to the residents. Most of the canals are navigable, and many have access to the Gulf of Mexico. The city is vulnerable to sea level rise because of its canals, site between the Matlacha Pass and the Caloosahatchee River, and much development in hazardous areas. In this research, I estimated the inundated area with Sea Level Rise Calculator tool, for three postulated sea level rise scenarios, by the U.S. Army Corps of Engineers (USACE), on the City of Cape Coral from 2020 to 2050 and created a Coastal Vulnerability Index (CoVI) using Principal Component Analysis (PCA). PCA reduced 25 variables to six factors that explained 78% of the variance in the data. The study revealed that the whole city has a medium to high vulnerability to sea level rise induced coastal flooding. Projected flooding showed the vulnerable areas for future flooding, whereas CoVI identified the vulnerable populations and their locations in the city. One important finding is that the wealthy people in Cape Coral are more vulnerable than the poor people. My research has significant implications in disaster preparedness, response, and recovery. It can act as a guideline for the city for disaster management and can be updated with the most recent data

Proxies of Design: A Case Study and Analysis of Place and Commercial Real Estate in Seattle

What kinds of relationships exist between individual buildings and greater society in Seattle? Focusing on the role of design in shaping the value and desirability of commercial properties, the study examines and utilizes a large temporal and spatial dataset to test price analogs between common building attributes and metrics. By employing a hedonic pricing model, the study seeks to identify the impact of these attributes on property values and ultimately relate them to architectural and contextual design, from a micro to a macro level. The empirical findings are not necessarily novel or groundbreaking, but rather, they shed light on the significance of building attributes not ordinarily thought of as proxies for design. The goal of this study is to inform commercial real estate practitioners, investors, planners, architects, and community members involved in the shaping of built environments. The research contributes to the existing literature on building valuation and offers insights of a unique place and its intriguing market for commercial real estate

Fuzzy clustering of spatial interval-valued data

In this paper, two fuzzy clustering methods for spatial intervalvalued data are proposed, i.e. the fuzzy C-Medoids clustering of spatial interval-valued data with and without entropy regularization. Both methods are based on the Partitioning Around Medoids (PAM) algorithm, inheriting the great advantage of obtaining non-fictitious representative units for each cluster. In both methods, the units are endowed with a relation of contiguity, represented by a symmetric binary matrix. This can be intended both as contiguity in a physical space and as a more abstract notion of contiguity. The performances of the methods are proved by simulation, testing the methods with different contiguity matrices associated to natural clusters of units. In order to show the effectiveness of the methods in empirical studies, three applications are presented: the clustering of municipalities based on interval-valued pollutants levels, the clustering of European fact-checkers based on interval-valued data on the average number of impressions received by their tweets and the clustering of the residential zones of the city of Rome based on the interval of price values

Fuzzy clustering of spatial interval-valued data

In this paper, two fuzzy clustering methods for spatial interval-valued data are proposed, i.e. the fuzzy C-Medoids clustering of spatial interval-valued data with and without entropy regularization. Both methods are based on the Partitioning Around Medoids (PAM) algorithm, inheriting the great advantage of obtaining non-fictitious representative units for each cluster. In both methods, the units are endowed with a relation of contiguity, represented by a symmetric binary matrix. This can be intended both as contiguity in a physical space and as a more abstract notion of contiguity. The performances of the methods are proved by simulation, testing the methods with different contiguity matrices associated to natural clusters of units. In order to show the effectiveness of the methods in empirical studies, three applications are presented: the clustering of municipalities based on interval-valued pollutants levels, the clustering of European fact-checkers based on interval-valued data on the average number of impressions received by their tweets and the clustering of the residential zones of the city of Rome based on the interval of price values

Bayesian hierarchical models for housing prices in the Helsinki-Espoo-Vantaa region

Tässä tutkielmassa esitellään bayesläisten hierarkisten mallien käyttöä asuntojen hintojen mallintamiseen. Tutkielmassa käytetään aineistoa, jossa kuvataan tapahtuneita asuntokauppoja Helsingistä, Espoosta ja Vantaalta. Tutkielmassa estimoidaan yhteensä viisi robustia regressiomallia. Malleissa käytetään Studentin t-jakaumaa likelihood-jakaumana, sillä aineistotarkastelut antavat viitteitä tietojen kirjausvirheistä. Neljässä mallissa on hierarkinen rakenne, joka perustuu myytyjen asuntojen kaupunginosiin. Malleista tuotetaan myös yhdistelmämalli käyttäen n.k. model stacking-menetelmää. Mallien toimivuutta tarkastellaan posterior-jakaumasta johdettavien ennustejakaumien perusteella: Ennustejakaumista poimitaan otos, jonka perusteella muodostetaan jakaumat valituille tunnusluvuille. Tunnuslukujen jakaumia verrataan oikeasta aineistosta laskettuihin, toteutuneisiin tunnuslukuihin. Mallien ennustekykyä vertaillaan tutkimalla ennustejakaumien kalibraatiota sekä terävyyttä. Lisäksi malleille lasketaan logaritmiset pisteet käyttäen leave-one-out ristiinvalidointia. Ristiinvalidoinnin laskennassa käytetään n.k. Pareto smoothed importance sampling-menetelmää. Ennustejakaumista tuotetaan myös piste-estimaatit käyttäen otoskeskiarvoja. Piste-estimaateille lasketaan R^2-suure. Mallien tulokset ovat valtaosin uskottavia. Malleissa käytetyt selittävät muuttujat käyttäytyvät pääosin etukäteen odotetulla tavalla ja mallien ennusteet ovat järkeviä valtaosalle havainnoista. Tulokset viittaavat siihen, että hintamekanismi eroaa oleellisesti Helsingin keskustassa verrattuna muihin tutkittuihin alueisiin. Mallit kärsivät kuitenkin huonosta kalibroinnista sekä siitä, että kalliiden asuntojen hintaennusteet ovat valtaosin liian alhaisia.Objectives: The objective of this thesis is to illustrate the advantages of Bayesian hierarchical models in housing price modeling. Methods: Five Bayesian regression models are estimated for the housing prices. The models use a robust Student’s t-distribution likelihood and are estimated with Hamiltonian Monte Carlo. Four of the models are hierarchical such that the apartments’ neighborhoods are used as a grouping. Model stacking is also used to produce an ensemble model. Model checks are conducted using the posterior predictive distributions. The predictive distributions are also evaluated in terms of calibration and sharpness and using the logarithmic score with leave-one-out cross validation. The logarithmic scores are calculated using Pareto smoothed importance sampling. The R^2-statistics from the point predictions averaged from the predictive distributions are also presented. Results: The results from the models are broadly reasonable as, for the most part, the coefficients of the explanatory variables and the predictive distributions behave as expected. The results are also consistent with the existence of a submarket in central Helsinki where the price mechanism differs markedly from the rest of the Helsinki-Espoo-Vantaa region. However, model checks indicate that none of the models is well-calibrated. Additionally, the models tend to underpredict the prices of expensive apartments

Analisis Perubahan Tata Guna Lahan di Kabupaten Bantul Menggunakan Metode Global Moran’s I

Abstract. Bantul Regency is a part of Yogyakarta Special Province Province which experienced land use changes. This research aims to assess the changes of shape and level of land use, to analyze the pattern of land use changes, and to find the appropriateness of RTRW land use in Bantul District in 2011-2015. Analytical methods are employed including Geoprocessing techniques and analysis of patterns of distribution of land use changes with Spatial Autocorrelation (Global Moran's I). The results of this study of land use in 2011, there are thirty one classifications, while in 2015 there are thirty four classifications. The pattern of distribution of land use change shows that land use change in 2011-2015 has a Complete Spatial Randomness pattern. Land use suitability with the direction of area function at RTRW is 24030,406 Ha (46,995406%) and incompatibility of 27103,115 Ha or equal to 53,004593% of the total area of Bantul Regency.Keywords: Geographical Information System, Land Use, Geoprocessing, Global Moran's I, Bantul Regency. Abstrak. Analisis Perubahan Tata Guna Lahan di Kabupaten Bantul Menggunakan Metode Global Moran’s I. Kabupaten Bantul merupakan bagian dari Provinsi Daerah Istimewa Yogyakarta yang mengalami perubahan tata guna lahan. Penelitian ini bertujuan untuk mengkaji perubahan bentuk dan luas penggunaan lahan, menganalisis pola sebaran perubahan tata guna lahan, serta kesesuaian tata guna lahan terhadap RTRW yang terjadi di Kabupaten Bantul pada tahun 2011-2015. Metode analisis yang digunakan antara lain teknik Geoprocessing serta analisis pola sebaran perubahan tata guna lahan dengan Spatial Autocorrelation (Global Moran’s I). Hasil dari penelitian ini adalah penggunaan tanah pada tahun 2011, terdapat tiga puluh satu klasifikasi, sedangkan pada tahun 2015 terdapat tiga puluh empat klasifikasi. Pola sebaran perubahan tata guna lahan menunjukkan bahwa perubahan tata guna lahan tahun 2011-2015 memiliki pola Complete Spatial Randomness. Kesesuaian tata guna lahan dengan arahan fungsi kawasan pada RTRW adalah seluas 24030,406 Ha atau mencapai 46,995406 % dan ketidaksesuaian seluas 27103,115 Ha atau sebesar 53,004593 % dari total luas wilayah Kabupaten Bantul. Kata Kunci: Sistem Informasi Georafis, tata guna lahan, Geoprocessing, Global Moran’s I, Kabupaten Bantul