INTRODUCTION: Sight-resight studies are performed to estimate population sizes, in this case dog populations in rabies endemic areas. AIM: This study compares one- and two-day sight-resight methods with two-day as the standard to explore the feasibility and accuracy of the one-day method in different vaccination campaign strategies and dog population characteristics. METHODS: 2015 household survey data and sight-resight data are analyzed to find the percentage of free roaming and confined dogs in the community and use those to adjust the population estimate formulas. 2016 sight-resight data are analyzed as a two-day campaign and as if it had been a one-day campaign. In a sensitivity analysis, confidence intervals are explored in relation to vaccination coverage. RESULTS: Before missed mark and proportion free-roaming corrections, the one-day method results in slightly underestimated population estimates to the two-day method when the vaccination campaign is central point, overestimated when door-to-door, and far underestimated when capture, vaccinate, release. After corrections door-to-door estimates were accurate whereas central point and capture, vaccinate, release estimates substantially underestimated population sizes. DISCUSSION: Results suggest that the one-day mark-resight method could be used to conserve resources depending on the vaccination method and estimated coverage.

The Greater Chassahowitkza Ecosystem black bear (Ursus americanus floridanus) population of west-central Florida is likely to be the smallest documented population of the species. It has experienced almost no recruitment since 1997 and exhibits behavior that appears to be a response to human activities. The local diet is dominated by the fruit of saw palmetto and sabal palm, species that exhibit patchy distributions and irregular mast production. These food supplies are often separated by busy highways that have killed 6 bears since 1997, 21% of known individuals. Motion-activated camera surveys suggest that the bear population is declining in this rapidly urbanizing part of Florida; results of the 2002 survey estimated 28 " 18 bears in the GCE, while 2003 estimates recorded 12 " 7 individuals (Lincoln-Petersen). Additionally, blood and hair samples suggest the genetics of this population are extremely depauperate. I recommend a different fire regime in palm-dominated habitats, restoring landscape connectivity to nearby bear populations, and supplementation of the population. Because the threats to this population are manifold and its immediate future is in doubt, a combination of conservation and management tools will be required to prevent extinction of this isolated black bear population.

Wildlife management and conservation frequently rely on understanding mechanisms that influence distribution and abundance of animals. I quantified space use for a population of inland riverine adult male alligators in Mississippi. Results indicated habitat selection is a scale-dependent process and aquatic vegetation, water depth, and water temperature may be important factors influencing alligator foraging and thermoregulation. Apparent habitat suitability and low alligator density did not manifest in an observed body size-based dominance hierarchy. I also analyzed long-term Mississippi alligator spotlight survey data for trends and effects of environmental covariates on counts. Model results indicated alligator counts have increased over time. This response likely reflects benefits accrued from decades of protection and wetland conservation. Distance sampling does not appear to be a feasible monitoring technique for riverine alligator populations. Nevertheless, it is important that survey protocols and monitoring programs account for imperfect detection and model important covariates.

Population estimation is an important task in all wildlife conservation. Such estimations are often difficult in low-density species such as big carnivores. The painted dog (Lycaon pictus) is an endangered species and the first aim of IUCNs action plan is to assess the size and the distribution of the remaining population. This study is the first step towards a quantitative method to distinguish painted dog footprints from footprints of feral domestic dogs and hyaenas. Footprint photographs were collected and digitally processed and total pad area and angles between the digits and backpad of the paw were measured. Both the pad area and the angles show a statistically significant difference between the species. However, further analysis shows that there is no significant difference in pad area between painted dog females and domestic dog males. Size of the pads alone is therefore not suitable as a measure to determine the species from an unknown footprint. The angles between backpad and digits seem to be more suitable to distinguish between species. Therefore, a combination of pad size and the angle between backpad and digits might be useful to estimate the species from an unknown footprint in the field.

The St. Croix ground lizard, Ameiva polops, is a United States endangered species endemic to St. Croix, U.S. Virgin Islands. It was extirpated from St. Croix Proper by invasive mongooses, and remaining populations are on small, nearby cays. In the summer of 2008, as part of the recovery plan for this species, I worked in a multi-agency effort to translocate a population of A. polops to Buck Island Reef National Monument, U.S. Virgin Islands to focus on two main objectives: 1) examine the detection probability of A. polops and infer the consequences of it on population estimates; and 2) examine whether A. polops may deplete its prey base or alter the arthropod assemblage at the translocation site. We used a soft-release strategy for the translocation, in which 57 lizards were initially contained in a series of eight 10 m x 10 m enclosures in the habitat on Buck Island for monitoring. As part of the initial monitoring I conducted visual surveys through all enclosures, with the known number of lizards, to calculate the detection probability and to demonstrate how many individuals would be estimated using visual encounter surveys of this known population. Adjacent to enclosures housing A. polops were control enclosures, without A. polops, which I used to test whether the translocated lizards would impact their prey base over 6 weeks. I found that the detection probability of A. polops is very low (<0.25), which causes population sizes to be severely underestimated, even using some mark-resight techniques. My study of A. polops on the prey community indicated that the lizards generally had no effect on abundance or diversity of arthropods in general, though they may cause small changes for particular taxa. My results help corroborate other evidence that accuracy of population enumeration techniques needs to be improved in order to adequately understand the status of wildlife populations. Additionally, prey resources do not seem to be limiting A. polops in the short-term, and I expect the population will grow, expanding through Buck Island. Future monitoring will be carried out by the National Park Service using robust mark-resight techniques.

The United States Fish and Wildlife Service (USFWS) expressed the need to develop appropriate management strategies for apparently high-density, growing black bear populations in the Roanoke-Neuse-Tar-Cape Fear ecosystem in coastal North Carolina and Virginia. In order to provide the scientific information necessary to develop these strategies, I investigated population densities and genetic structure of black bears at 3 national wildlife refuges [Great Dismal Swamp (GDSNWR), Pocosin Lakes (PLNWR), and Alligator River (ARNWR)]. Density estimates were derived from DNA samples collected noninvasively at each of the 3 refuges for 2 consecutive summers. Hair samples were analyzed for individual identification using 6-7 microsatellite markers. Estimated densities were some of the highest reported in the literature and ranged from 0.56-0.63 bears/km2 at GDSNWR to 0.65-1.12 bears/km2 at ARNWR to 1.23-1.66 bears/km2 at PLNWR. Sex ratios were male-biased in all areas of all refuges. Genetic variability and structure of bears at these refuges was assessed using 16 microsatellite markers for 40 bears from each refuge. Genetic variability of the 3 refuge populations was substantially high compared to other bear populations in North America, with observed heterozygosities ranging from 0.6729 at GDSNWR to 0.7219 at ARNWR. FST and DS values were relatively low (0.0257-0.0895 and 0.0971-0.3640, respectively), indicating movement of bears and gene flow across the landscape is adequate to prevent high levels of genetic differentiation and structure among the refuge bears. Genetic statistics at GDSNWR indicate that this population is isolated to some degree by geography (i.e., the Albemarle Sound) and encroaching urban development (i.e., the towns of Suffolk and Chesapeake). ARNWR has the potential to become isolated in the future if movement corridors to the south of the refuge are not maintained. Harvest of bears is likely warranted at PLNWR and ARNWR, though extreme caution must be taken the first few seasons as hunter success will be extremely high. Further research is needed to determine population growth rates, reproductive parameters, and survival rates at all 3 refuges, particularly if a hunting season will be established and maintained in these areas. Methods for regularly monitoring bear populations at these refuges also should be incorporated into biological programs, as bears comprise a significant component of the ecosystem at these refuges and cannot be ignored when outlining management goals. / Master of Science

The long-term success of wildlife conservation depends on maximizing the benefits of limited funds and data in pursuit of population and habitat objectives. The ultimate currency for wildlife management is progress toward long-term preservation of ample, wild, free wildlife populations and to this end, funds must be wisely spent and maximal use made from limited data. Through simulation-based analyses, I evaluated the efficacy of various models for estimating population abundance from harvest data. Because managers have different estimators to choose from and can also elect to collect additional data, I compared the statistical performance of different estimation strategies (estimator + dataset) relative to the financial cost of data collection. I also performed a value of information analysis to measure the impact that different strategies have on a representative harvest management decision. The latter analysis is not based on the cost of data, but rather on the management benefit derived from basing decisions on different datasets. Finally, I developed a hybrid modeling framework for mapping habitat quality or suitability. This framework makes efficient use of expert opinion and empirical validation data in a single, updatable statistical structure. I illustrate this method by applying it across an entire state.

Im Bereich der städtebasierten siedlungsstrukturellen Analysen spielen Gebäudedaten mit Informationen zur Gebäudenutzung und dem Gebäudetyp eine wichtige Rolle. Auf diesen Daten basiert die Modellierung von demografischen und sozioökonomischen Kenngrößen, welche bei Aufgaben der Siedlungsentwicklung oder in der Infrastrukturplanung zum Einsatz kommen. Vonseiten der amtlichen Vermessungsanstalten stehen kleinräumige und flächendeckende Daten zur Gebäudenutzung nur in begrenztem Umfang zur Verfügung. Eine darauf aufbauende Bevölkerungsabschätzung ist aus diesem Grund nur eingeschränkt möglich. Das Ziel dieser Arbeit war es, die Integration von nutzergenerierten Geodaten aus dem OpenStreetMap (OSM) Projekt für den Einsatz zur Abschätzung gebäudebasierter Bevölkerungs- und Wohnungszahlen zu untersuchen. Der Fokus liegt dabei besonders auf der Abgrenzung von Wohn- und Nichtwohnnutzung innerhalb von Gebäuden. Diese Informationen sind in den amtlichen Geobasisdaten nicht zu finden, können jedoch aus OSM Punkt- und Polygondaten extrahiert werden. Hauptgegenstand der Untersuchung ist die Entwicklung eines Modells zur Integration der Nichtwohnnutzungsinformationen aus OSM, welches Gewerbeinformationen anhand der OSM-Tags analysiert. Dazu war neben einer geeigneten Typologie, die Festlegung von Regeln zur Verarbeitung mehrerer Gewerbe in einem amtlichen Gebäude notwendig. Über räumliche Verschneidungen der Datensätze erfolgt die eigentliche Datenintegration. Zur Umsetzung des Modells wurden drei Python-Skripte erarbeitet, welche alle notwendigen Vorverarbeitungsschritte und anschließenden Modellberechnungen automatisiert durchführen. Zusätzlich zur Bestimmung des Nichtwohnnutzungsanteiles in den Gebäuden, fand eine Wohnung- und Bevölkerungsabschätzung mittels gebäudetypischer Kenngrößen auf Gebäudebasis statt. Mittels der Abschätzungsergebnisse ohne und mit OSM-Gewerbeinformationen, konnte eine Bewertung dieser Methode erfolgen. Eine Beurteilung der Qualität des Modells im Vergleich zur Realität benötigt geeignete Validierungsdaten. Diese wurden in Form von baublockbezogenen statistischen Einwohner- und Wohnungszahlen von der Stadt Dresden bereitgestellt und zur Ergebnisdiskussion herangezogen. Regionale Unterschiede konnten anhand von Übersichts- und Detailkarten sowie statistischen Analysen herausgearbeitet werden.:Inhaltsverzeichnis ............................................................................. I Abkürzungsverzeichnis ..................................................................... V Abbildungsverzeichnis ...................................................................... VII Tabellenverzeichnis .......................................................................... IX 1 Einleitung ...................................................................................... 11 1.1 Motivation ................................................................................... 11 1.2 Zielstellung und Aufbau der Arbeit ............................................. 12 2 Theoretische und praktische Grundlagen ...................................... 15 2.1 Räumliche Modellierung der Siedlungsstruktur ........................... 15 2.1.1 Siedlungsstrukturelle Begriffe ................................................. 15 2.1.2 Räumliche Daten ..................................................................... 18 2.2 Semantische Integration ............................................................ 23 2.2.1 Interoperabilität ...................................................................... 24 2.2.2 Datenintegration ..................................................................... 25 2.3 Semantik in Geodaten ................................................................ 28 2.3.1 Attributierung in OpenStreetMap ............................................. 28 2.3.2 Nutzungsinformationen in amtlichen Geobasisdaten ............... 31 3 Modellierung sozioökonomischer Kenngrößen ............................... 33 3.1 Bedarf an kleinräumigen Nutzungsinformationen ....................... 33 3.2 Modellierungsansätze ................................................................. 35 3.2.1 Ableitung sozioökonomischer Daten aus der Fernerkundung .. 35 3.2.2 Modellierung mit Hilfe von Geobasisdaten ............................... 36 3.3 Vorteile und Mängel der vorgestellten Ansätze .......................... 40 3.4 Möglichkeiten von OSM zur Verbesserung der Modellierung ....... 42 4 Methodik ........................................................................................ 43 4.1 Herangehensweise ..................................................................... 43 4.2 Flächen- und Gebäudenutzungen in amtlichen und OSM-Daten .. 44 4.2.1 Typologie der Flächen- und Gebäudenutzung .......................... 44 4.2.2 Typologie der Flächenbeanspruchung von Gewerben .............. 47 4.3 Modell zur Abschätzung des Nichtwohnnutzungsanteils ............. 50 4.3.1 Gebäudetypische Kenngrößen ................................................. 51 4.3.2 Vorgehensweise ...................................................................... 52 5 Daten ............................................................................................. 55 5.1 Untersuchungsgebiet .................................................................. 55 5.2 OpenStreetMap ........................................................................... 56 5.3 Amtliche Geobasisdaten .............................................................. 57 6 Praktische Umsetzung .................................................................... 59 6.1 Datenmanagement ...................................................................... 59 6.1.1 Eingesetzte Software ............................................................... 59 6.1.2 OSM-Import .............................................................................. 60 6.1.3 Datenorganisation ................................................................... 64 6.2 Praktische Umsetzung des Modell zur Abschätzung des Nichtwohnnutzungsanteils ................................................................ 65 6.2.1 Vorverarbeitung ....................................................................... 65 6.2.2 Modellberechnungen und Bevölkerungsabschätzung .............. 74 6.3 Validierung des Modells .............................................................. 79 6.3.1 Validierungsdaten .................................................................... 79 6.3.2 Durchführung ........................................................................... 80 7 Ergebnisse ..................................................................................... 83 7.1 Untersuchung zur Vollständigkeit der semantischen Informationen anhand der Gewerbe POI .......................................................................................................... 83 7.1.1 Datengrundlagen ..................................................................... 84 7.1.2 Gewerbevergleich durch zufällige Straßenwahl ........................ 84 7.1.3 Gezielter Gewerbevergleich durch Ortsbegehung .................... 86 7.2 Charakterisierung des Ergebnisdatensatzes .............................. 88 7.3 Validierung .................................................................................. 91 7.4 Ergebnisvisualisierung ................................................................ 93 8 Ergebnisdiskussion ........................................................................ 97 8.1 Kleinräumige Betrachtung ........................................................... 97 8.2 Dateninkonsistenz ...................................................................... 101 8.3 Methodische Schwächen und Stärken ........................................ 103 8.4 Weitere Analysemöglichkeiten .................................................... 104 9 Zusammenfassung ........................................................................ 105 9.1 Fazit ........................................................................................... 105 9.2 Ausblick ...................................................................................... 106 Literaturverzeichnis .......................................................................... 109 A Anhang .......................................................................................... 115 Anhang 1: IÖR Flächenschema ......................................................... 115 Anhang 2: Flächen- und Gebäudenutzungstypologie........................ 116 Anhang 3: Klassifikationsschema nach (Burckhardt, 2012) .............. 118 Anhang 4: Skript-Auszug 1 - Datenverarbeitung in der OSM-Punktdatei ................................................................................ 120 Anhang 5: Skript-Auszug 2 - Die Funktion „calculateGWTotal“ .......... 121 Anhang 6: Karten - Untersuchungsgebiet Dresden in 1:100.000 ..... 122 Anhang 7: CD-Inhalt ......................................................................... 122 / Building data with information of building uses and building types play an important role for city-based settlement structure analyses. The estimation of demographic and socio-economic parameters is based on this data. They were used in the field of settlement development or in infrastructure planning. The availability of area-wide and small-scale data of building uses from surveying authorities is limited. For this reason, the estimation of population based on this data cannot be realised sufficiently. Therefore, it was the aim of this research paper to analyse the integration of user-generated geodata from OpenStreetMap (OSM)-project for estimating building-based population and housing units. The research focuses on the separation of residential and non-residential usage within buildings. Not being detected in official geodatasets, the information can be retrieved from OSM-point and polygondata. The development of a model for the integration of non-residential information from OSM is the main subject of this research. It contains the analyses of commercial information out of the OSM Tags. Besides an appropriate typology specifications are necessary to process multiple businesses within one official building. The actual data integration occurs with the help of spatial intersections between the datasets. The implementation of the model is based on three Python-scripts, executing all pre-processing and following calculation steps automatically. In addition to the identification of non-residential building-parts, an estimation of population and housing units per building, based on typical building parameters took place. By means of the estimation results with and without OSM-information it was possible to valuate this method. Validation data is necessary to measure the quality of the model in comparison to reality. This datasets was provided by the City of Dresden, consisting of statistical population and building unit numbers based on building blocks, and used for the discussion of the results. To describe regional differences, maps with overview and detailed scales as well as statistical schemata where used.:Inhaltsverzeichnis ............................................................................. I Abkürzungsverzeichnis ..................................................................... V Abbildungsverzeichnis ...................................................................... VII Tabellenverzeichnis .......................................................................... IX 1 Einleitung ...................................................................................... 11 1.1 Motivation ................................................................................... 11 1.2 Zielstellung und Aufbau der Arbeit ............................................. 12 2 Theoretische und praktische Grundlagen ...................................... 15 2.1 Räumliche Modellierung der Siedlungsstruktur ........................... 15 2.1.1 Siedlungsstrukturelle Begriffe ................................................. 15 2.1.2 Räumliche Daten ..................................................................... 18 2.2 Semantische Integration ............................................................ 23 2.2.1 Interoperabilität ...................................................................... 24 2.2.2 Datenintegration ..................................................................... 25 2.3 Semantik in Geodaten ................................................................ 28 2.3.1 Attributierung in OpenStreetMap ............................................. 28 2.3.2 Nutzungsinformationen in amtlichen Geobasisdaten ............... 31 3 Modellierung sozioökonomischer Kenngrößen ............................... 33 3.1 Bedarf an kleinräumigen Nutzungsinformationen ....................... 33 3.2 Modellierungsansätze ................................................................. 35 3.2.1 Ableitung sozioökonomischer Daten aus der Fernerkundung .. 35 3.2.2 Modellierung mit Hilfe von Geobasisdaten ............................... 36 3.3 Vorteile und Mängel der vorgestellten Ansätze .......................... 40 3.4 Möglichkeiten von OSM zur Verbesserung der Modellierung ....... 42 4 Methodik ........................................................................................ 43 4.1 Herangehensweise ..................................................................... 43 4.2 Flächen- und Gebäudenutzungen in amtlichen und OSM-Daten .. 44 4.2.1 Typologie der Flächen- und Gebäudenutzung .......................... 44 4.2.2 Typologie der Flächenbeanspruchung von Gewerben .............. 47 4.3 Modell zur Abschätzung des Nichtwohnnutzungsanteils ............. 50 4.3.1 Gebäudetypische Kenngrößen ................................................. 51 4.3.2 Vorgehensweise ...................................................................... 52 5 Daten ............................................................................................. 55 5.1 Untersuchungsgebiet .................................................................. 55 5.2 OpenStreetMap ........................................................................... 56 5.3 Amtliche Geobasisdaten .............................................................. 57 6 Praktische Umsetzung .................................................................... 59 6.1 Datenmanagement ...................................................................... 59 6.1.1 Eingesetzte Software ............................................................... 59 6.1.2 OSM-Import .............................................................................. 60 6.1.3 Datenorganisation ................................................................... 64 6.2 Praktische Umsetzung des Modell zur Abschätzung des Nichtwohnnutzungsanteils ................................................................ 65 6.2.1 Vorverarbeitung ....................................................................... 65 6.2.2 Modellberechnungen und Bevölkerungsabschätzung .............. 74 6.3 Validierung des Modells .............................................................. 79 6.3.1 Validierungsdaten .................................................................... 79 6.3.2 Durchführung ........................................................................... 80 7 Ergebnisse ..................................................................................... 83 7.1 Untersuchung zur Vollständigkeit der semantischen Informationen anhand der Gewerbe POI .......................................................................................................... 83 7.1.1 Datengrundlagen ..................................................................... 84 7.1.2 Gewerbevergleich durch zufällige Straßenwahl ........................ 84 7.1.3 Gezielter Gewerbevergleich durch Ortsbegehung .................... 86 7.2 Charakterisierung des Ergebnisdatensatzes .............................. 88 7.3 Validierung .................................................................................. 91 7.4 Ergebnisvisualisierung ................................................................ 93 8 Ergebnisdiskussion ........................................................................ 97 8.1 Kleinräumige Betrachtung ........................................................... 97 8.2 Dateninkonsistenz ...................................................................... 101 8.3 Methodische Schwächen und Stärken ........................................ 103 8.4 Weitere Analysemöglichkeiten .................................................... 104 9 Zusammenfassung ........................................................................ 105 9.1 Fazit ........................................................................................... 105 9.2 Ausblick ...................................................................................... 106 Literaturverzeichnis .......................................................................... 109 A Anhang .......................................................................................... 115 Anhang 1: IÖR Flächenschema ......................................................... 115 Anhang 2: Flächen- und Gebäudenutzungstypologie........................ 116 Anhang 3: Klassifikationsschema nach (Burckhardt, 2012) .............. 118 Anhang 4: Skript-Auszug 1 - Datenverarbeitung in der OSM-Punktdatei ................................................................................ 120 Anhang 5: Skript-Auszug 2 - Die Funktion „calculateGWTotal“ .......... 121 Anhang 6: Karten - Untersuchungsgebiet Dresden in 1:100.000 ..... 122 Anhang 7: CD-Inhalt ......................................................................... 122

Im Bereich der städtebasierten siedlungsstrukturellen Analysen spielen Gebäudedaten mit Informationen zur Gebäudenutzung und dem Gebäudetyp eine wichtige Rolle. Auf diesen Daten basiert die Modellierung von demografischen und sozioökonomischen Kenngrößen, welche bei Aufgaben der Siedlungsentwicklung oder in der Infrastrukturplanung zum Einsatz kommen. Vonseiten der amtlichen Vermessungsanstalten stehen kleinräumige und flächendeckende Daten zur Gebäudenutzung nur in begrenztem Umfang zur Verfügung. Eine darauf aufbauende Bevölkerungsabschätzung ist aus diesem Grund nur eingeschränkt möglich. Das Ziel dieser Arbeit war es, die Integration von nutzergenerierten Geodaten aus dem OpenStreetMap (OSM) Projekt für den Einsatz zur Abschätzung gebäudebasierter Bevölkerungs- und Wohnungszahlen zu untersuchen. Der Fokus liegt dabei besonders auf der Abgrenzung von Wohn- und Nichtwohnnutzung innerhalb von Gebäuden. Diese Informationen sind in den amtlichen Geobasisdaten nicht zu finden, können jedoch aus OSM Punkt- und Polygondaten extrahiert werden. Hauptgegenstand der Untersuchung ist die Entwicklung eines Modells zur Integration der Nichtwohnnutzungsinformationen aus OSM, welches Gewerbeinformationen anhand der OSM-Tags analysiert. Dazu war neben einer geeigneten Typologie, die Festlegung von Regeln zur Verarbeitung mehrerer Gewerbe in einem amtlichen Gebäude notwendig. Über räumliche Verschneidungen der Datensätze erfolgt die eigentliche Datenintegration. Zur Umsetzung des Modells wurden drei Python-Skripte erarbeitet, welche alle notwendigen Vorverarbeitungsschritte und anschließenden Modellberechnungen automatisiert durchführen. Zusätzlich zur Bestimmung des Nichtwohnnutzungsanteiles in den Gebäuden, fand eine Wohnung- und Bevölkerungsabschätzung mittels gebäudetypischer Kenngrößen auf Gebäudebasis statt. Mittels der Abschätzungsergebnisse ohne und mit OSM-Gewerbeinformationen, konnte eine Bewertung dieser Methode erfolgen. Eine Beurteilung der Qualität des Modells im Vergleich zur Realität benötigt geeignete Validierungsdaten. Diese wurden in Form von baublockbezogenen statistischen Einwohner- und Wohnungszahlen von der Stadt Dresden bereitgestellt und zur Ergebnisdiskussion herangezogen. Regionale Unterschiede konnten anhand von Übersichts- und Detailkarten sowie statistischen Analysen herausgearbeitet werden. / Building data with information of building uses and building types play an important role for city-based settlement structure analyses. The estimation of demographic and socio-economic parameters is based on this data. They were used in the field of settlement development or in infrastructure planning. The availability of area-wide and small-scale data of building uses from surveying authorities is limited. For this reason, the estimation of population based on this data cannot be realised sufficiently. Therefore, it was the aim of this research paper to analyse the integration of user-generated geodata from OpenStreetMap (OSM)-project for estimating building-based population and housing units. The research focuses on the separation of residential and non-residential usage within buildings. Not being detected in official geodatasets, the information can be retrieved from OSM-point and polygondata. The development of a model for the integration of non-residential information from OSM is the main subject of this research. It contains the analyses of commercial information out of the OSM Tags. Besides an appropriate typology specifications are necessary to process multiple businesses within one official building. The actual data integration occurs with the help of spatial intersections between the datasets. The implementation of the model is based on three Python-scripts, executing all pre-processing and following calculation steps automatically. In addition to the identification of non-residential building-parts, an estimation of population and housing units per building, based on typical building parameters took place. By means of the estimation results with and without OSM-information it was possible to valuate this method. Validation data is necessary to measure the quality of the model in comparison to reality. This datasets was provided by the City of Dresden, consisting of statistical population and building unit numbers based on building blocks, and used for the discussion of the results. To describe regional differences, maps with overview and detailed scales as well as statistical schemata where used.

Population estimation in inter-censual years has many important applications. In this research, high-resolution pan-sharpened IKONOS image, LiDAR data, and parcel data are used to estimate small-area population in the eastern part of the city of Denton, Texas. Residential buildings are extracted through object-based classification techniques supported by shape indices and spectral signatures. Three population indicators -building count, building volume and building area at block level are derived using spatial joining and zonal statistics in GIS. Linear regression and geographically weighted regression (GWR) models generated using the three variables and the census data are used to estimate population at the census block level. The maximum total estimation accuracy that can be attained by the models is 94.21%. Accuracy assessments suggest that the GWR models outperformed linear regression models due to their better handling of spatial heterogeneity. Models generated from building volume and area gave better results. The models have lower accuracy in both densely populated census blocks and sparsely populated census blocks, which could be partly attributed to the lower accuracy of the LiDAR data used.