Geocoding of trees from street addresses and street-level images

We introduce an approach for updating older tree inventories with geographic coordinates using street-level panorama images and a global optimization framework for tree instance matching. Geolocations of trees in inventories until the early 2000s where recorded using street addresses whereas newer inventories use GPS. Our method retrofits older inventories with geographic coordinates to allow connecting them with newer inventories to facilitate long-term studies on tree mortality etc. What makes this problem challenging is the different number of trees per street address, the heterogeneous appearance of different tree instances in the images, ambiguous tree positions if viewed from multiple images and occlusions. To solve this assignment problem, we (i) detect trees in Google street-view panoramas using deep learning, (ii) combine multi-view detections per tree into a single representation, (iii) and match detected trees with given trees per street address with a global optimization approach. Experiments for trees in 5 cities in California, USA, show that we are able to assign geographic coordinates to 38% of the street trees, which is a good starting point for long-term studies on the ecosystem services value of street trees at large scale

Geographically Referenced Data for Social Science

An estimated 80% of all information has a spatial reference. Information about households as well as environmental data can be linked to precise locations in the real world. This offers benefits for combining different datasets via the spatial location and, furthermore, spatial indicators such as distance and accessibility can be included in analyses and models. HSpatial patterns of real-world social phenomena can be identified and described and possible interrelationships between datasets can be studied. Michael F. GOODCHILD, a Professor of Geography at the University of California, Santa Barbara and principal investigator at the Center for Spatially Integrated Social Science (CSISS), summarizes the growing significance of space, spatiality, location, and place in social science research as follows: "(...) for many social scientists, location is just another attribute in a table and not a very important one at that. After all, the processes that lead to social deprivation, crime, or family dysfunction are more or less the same everywhere, and, in the minds of social scientists, many other variables, such as education, unemployment, or age, are far more interesting as explanatory factors of social phenomena than geographic location. Geographers have been almost alone among social scientists in their concern for space; to economists, sociologists, political scientists, demographers, and anthropologists, space has been a minor issue and one that these disciplines have often been happy to leave to geographers. But that situation is changing, and many social scientists have begun to talk about a "spatial turn," a new interest in location, and a new "spatial social science" that crosses the traditional boundaries between disciplines. Interest is rising in GIS (Geographic Information Systems) and in what GIS makes possible: mapping, spatial analysis, and spatial modelling. At the same time, new tools are becoming available that give GIS users access to some of the big ideas of social science."

Get To The “Point”: Improving Location Services in Tippah County Mississippi

Point location using geographic information systems (GIS) technology has become integrated into everyday society and daily decision-making by utilizing addresses to provide goods and services. A need exists at a national, state, and local level for an address database. The objectives of this study were to [1] determine the most suitable address data model to be used in Mississippi, [2] determine how positional accuracy changes between urban and rural areas, and [3] determine spatial variations in aerial imagery. Address data model comparisons were conducted using match rates between street, parcel, and point address models. Positional accuracy was determined for urban and rural areas using GPS points and margin of error. A mean center and standard distance calculation were performed using one standard deviation. [1] The point address data model (93% matched) and parcel data model (93% matched) outperformed the street data model (06%). [2] The results show that 65% of the average mean points fell within 13 feet – 38 feet from the structure. The average distance from mean was 27.87 feet in urban areas and 82.98 feet in rural areas [3] 75% of the total points fell within the margin of error in urban areas and 80% of the total points in rural areas. Match rates were influenced by both the quality of reference and input address datasets. Using an average point location is acceptable for addressing in urban and rural areas. There was no significant shift or change between the 2006 and 2015 imageries. Address collection using the point address data model and high-resolution aerial imagery is an accurate, cost-efficient way to build an address database

Integration of Satellite and Financial Data to Model Future Economic Impact of Citrus Crops (Final Project Report)

This study analyzed the health and overall landcover of citrus crops in Florida. The analysis was completed using Landsat satellite imagery available free of charge from the University of Maryland Global Landcover Change Facility. The project hypothesized that combining citrus production (economic) data with citrus area per county derived from spectral signatures would yield correlations between observable spectral reflectance throughout the year, and the fiscal impact of citrus on local economies. A positive correlation between these two data types would allow us to predict the economic impact of citrus using spectral data analysis to determine final crop harvests

Smartphone-assisted spatial data collection improves geographic information quality: pilot study using a birth records dataset

It is well known that the conventional, automated geocoding method based on self-reported residential addresses has many issues. We developed a smartphone-assisted aerial image-based method, which uses the Google Maps application programming interface as a spatial data collection tool during the birth registration process. In this pilot study, we have tested whether the smartphone-assisted method provides more accurate geographic information than the automated geocoding method in the scenario when both methods can get the address geocodes. We randomly selected 100 well-geocoded addresses among women who gave birth in Alachua county, Florida in 2012. We compared geocodes generated from three geocoding methods: i) the smartphone-assisted aerial image-based method; ii) the conventional, automated geocoding method; and iii) the global positioning system (GPS). We used the GPS data as the reference method. The automated geocoding method yielded positional errors larger than 100 m among 29.3% of addresses, while all addresses geocoded by the smartphoneassisted method had errors less than 100 m. The positional errors of the automated geocoding method were greater for apartment/condominiums compared with other dwellings and also for rural addresses compared with urban ones. We conclude that the smartphone-assisted method is a promising method for perspective spatial data collection by improving positional accuracy

a Berlin case study

Durch den Prozess der Urbanisierung verändert die Menschheit die Erdoberfläche in großem Ausmaß und auf unwiederbringliche Weise. Die optische Fernerkundung ist eine Art der Erdbeobachtung, die das Verständnis dieses dynamischen Prozesses und seiner Auswirkungen erweitern kann. Die vorliegende Arbeit untersucht, inwiefern hyperspektrale Daten Informationen über Versiegelung liefern können, die der integrierten Analyse urbaner Mensch-Umwelt-Beziehungen dienen. Hierzu wird die Verarbeitungskette von Vorverarbeitung der Rohdaten bis zur Erstellung referenzierter Karten zu Landbedeckung und Versiegelung am Beispiel von Hyperspectral Mapper Daten von Berlin ganzheitlich untersucht. Die traditionelle Verarbeitungskette wird mehrmals erweitert bzw. abgewandelt. So wird die radiometrische Vorverarbeitung um die Normalisierung von Helligkeitsgradienten erweitert, welche durch die direktionellen Reflexionseigenschaften urbaner Oberflächen entstehen. Die Klassifikation in fünf spektral komplexe Landnutzungsklassen wird mit Support Vector Maschinen ohne zusätzliche Merkmalsextraktion oder Differenzierung von Subklassen durchgeführt...thesi

Geographically referenced data for social science

"Die Autoren beschreiben in ihrem Beitrag den Mehrwert der Nutzung von Geodaten in den Sozialwissenschaften. Sie liefern vor allem eine Reihe praktischer Hinweise auf die Bezugsquellen von Geodaten. Beispielhaft legen sie dar, wie sich Geodaten mit Daten des Sozio-oekonomischen Panels (SOEP) kombinieren lassen. Der erste Teil ihres Beitrags enthält eine nähere Beschreibung von Geodaten und des Geographischen Informationssystems (GIS). Das zweite Kapitel beschäftigt sich mit den Zielgruppen und Nutzern, es erläutert die der Dokumentation zugrunde liegende Definition der Geodaten und zeigt die Vorzüge und Herausforderungen beim Einsatz von Geodaten in der sozialwissenschaftlichen Forschung auf. Das dritte Kapitel verdeutlicht anhand zahlreicher Beispiele das Potenzial von Geodaten als eine zusätzliche Informationsquelle für die empirische Forschung. Abschließend wird ein Überblick über verfügbare Geodaten in Deutschland und die am meisten nachgefragten Daten in den Sozialwissenschaften gegeben. Der Anhang des Beitrags enthält Internet-Links für Datenquellen, Hinweise zur verfügbaren GIS-Software und weitere Informationen." (ICI