Suchergebnisse

Existing literature about the impact of farmland titling on farmland transfer takes no account of farmland plot characteristics, which makes them unable to effectively identify the causal relationship between farmland titling and farmland transfer. After the theoretical analysis, based on land plot level micro-survey data, we adopt the instrumental variable (IV) and conditional mixed process (CMP) methods to ease the endogeneity problem in the model and conduct a quantitative analysis. The results show that the land titling program has significant and positive effects on the transfer-out of farmland. Through a heterogeneity test, we observe a more pronounced promotional effect in regions with a higher economic development level and in farmland transfer deals with government facilitation. Moreover, the further application of a mediating effect model shows that the land titling program increases the net income from farmland transfer-out through increasing the value of farmland and reducing the transaction costs, thus promoting the transferring out of farmland. The findings contribute to providing empirical evidence for how the government may facilitate and support the attaining of more efficient scale operations of farmland.

Cropland maps are useful for the management of agricultural fields and the estimation of harvest yield. Some local governments have documented field properties, including crop type and location, based on site investigations. This process, which is generally done manually, is labor-intensive, and remote-sensing techniques can be used as alternatives. In this study, eight crop types (beans, beetroot, grass, maize, potatoes, squash, winter wheat, and yams) were identified using gamma naught values and polarimetric parameters calculated from TerraSAR-X (or TanDEM-X) dual-polarimetric (HH/VV) data. Three indices (difference (D-type), simple ratio (SR), and normalized difference (ND)) were calculated using gamma naught values and m-chi decomposition parameters and were evaluated in terms of crop classification. We also evaluated the classification accuracy of four widely used machine-learning algorithms (kernel-based extreme learning machine, support vector machine, multilayer feedforward neural network (FNN), and random forest) and two multiple-kernel methods (multiple kernel extreme learning machine (MKELM) and multiple kernel learning (MKL)). MKL performed best, achieving an overall accuracy of 92.1%, and proved useful for the identification of crops with small sample sizes. The difference (raw or normalized) between double-bounce scattering and odd-bounce scattering helped to improve the identification of squash and yams fields.

Against the paucity of information on urban parcels in China, we propose a method to automatically identify and characterize parcels using OpenStreetMap (OSM) and points of interest (POI) data. Parcels are the basic spatial units for fine-scale urban modeling, urban studies, and spatial planning. Conventional methods for identification and characterization of parcels rely on remote sensing and field surveys, which are labor intensive and resource consuming. Poorly developed digital infrastructure, limited resources, and institutional barriers have all hampered the gathering and application of parcel data in China. Against this backdrop, we employ OSM road networks to identify parcel geometries and POI data to infer parcel characteristics. A vector-based cellular automata model is adopted to select urban parcels. The method is applied to the entire state of China and identifies 82 645 urban parcels in 297 cities. Notwithstanding all the caveats of open and/or crowd-sourced data, our approach can produce a reasonably good approximation of parcels identified using conventional methods, thus it has the potential to become a useful tool.

Modern (Multi-Purpose) Land Administration Systems have difficulty in managing up-to-date land parcel types. Managing external land use/cover information together with land parcels in an integrated manner may be a robust solution to this problem. In this study, inspired from common international land use/cover classification systems (FAO Land Cover Classification System, CORINE land cover and INSPIRE land use/cover theme) and spatial data management issues within agricultural policy implementation both in EU and in Turkey, a new land use/cover classification system was designed and land use/cover data sets was produced for three districts in Kayseri Province of Turkey. Further, rules for the integration of land use/cover data with cadastral land parcel data and accordingly updating/maintenance procedures were defined. Due to complexity of updating and maintenance procedures, use of crowd sourcing techniques with the contribution of related government agencies and also citizens was proposed. The study has been continuing as a component of a national project no 112Y027 that is financially supported by the Scientific and Technological Research Council of Turkey.

Cover; Title; Copyright; Contents; List of figures; List of tables; List of contributors; Foreword; 1 Introduction; Part I New big data sources in regional science; 2 Opportunities for retail data and their geographic integration in social science; 3 Use of probe data generated by taxis; 4 The emerging geography of globalizing Chinese cities based on web-based information services; 5 Using web-crawled data for urban housing research; 6 Examining intraurban migration in the Twin Cities metropolitan area using parcel data.

ABSTRACTAn important aspect of the growing e‐commerce sector involves the delivery of tangible goods to the end customer, the so‐called last mile. This final stage of the logistics chain remains highly inefficient due to the problem of failed deliveries. To address this problem, delivery service providers can apply data science to determine the optimal, customer‐centered location and time window for handover. In this article, we present a three‐step approach for location prediction, based on mobile location data, in order to support delivery planning. The first step is identifying a user's locations of interest through density‐based clustering. Next, the semantics (home or work) of the user's locations of interest are discovered, based on temporal assumptions. Finally, we predict future locations with a decision tree model that is trained on each user's historical location data. Though the problem of location prediction is not new, this work is the first to apply it to the field of parcel delivery with its corresponding implications. Moreover, we provide a novel and detailed evaluation on real‐world data from a parcel delivery service. The promising results indicate that our approach has the potential to help delivery service providers to gain insights into their customers' optimal delivery time and location in order to support delivery planning. Eventually this will decrease last‐mile delivery costs and boost customer satisfaction.

The Common Agricultural Policy (CAP) of the European Union (EU) has dramatically changed after 1992, and from then on the CAP focused on the management of direct income subsidies instead of production-based subsidies. For this focus, Member States (MS) are expected to establish Integrated Administration and Control System (IACS), including a Land Parcel Identification System (LPIS) as the spatial part of IACS. Different MS have chosen different solutions for their LPIS. Currently, some MS based their IACS/LPIS on data from their Land Administration Systems (LAS), and many others use purpose built special systems for their IACS/LPIS. The issue with these different IACS/LPIS is that they do not have standardized structures; rather, each represents a unique design in each MS, both in the case of LAS based or special systems. In this study, we aim at designing a core data model for those IACS/LPIS based on LAS. For this purpose, we make use of the ongoing standardization initiatives for LAS (Land Administration Domain Model: LADM) and IACS/LPIS (LPIS Core Model: LCM). The data model we propose in this study implies the collaboration between LADM and LCM and includes some extensions. Some basic issues with the collaboration model are discussed within this study: registration of farmers, land use rights and farming limitations, geometry/topology, temporal data management etc. For further explanation of the model structure, sample instance level diagrams illustrating some typical situations are also included. (C) 2010 Elsevier Ltd. All rights reserved.

It is estimated that between 80% and 90% of governmental data collections contain address information, Geocoding - the process of assigning geographic coordinates to addresses - is becoming increasingly important in application areas that involve the analysis and mining of such data. In many cases, address records are captured and/or stored in a free-form or inconsistent manner. This fact complicates the task of accurately matching such addresses to spatially-annotated reference data. In this paper we describe a geocoding system that is based on a comprehensive high-quality geocoded national address database. It uses a learning address parser based on hidden Markov models to segment free-form addresses into components, and a rule-based matching engine to determine the best matches to the reference database.

It is estimated that between 80% and 90% of governmental data collections contain address information, Geocoding - the process of assigning geographic coordinates to addresses - is becoming increasingly important in application areas that involve the analysis and mining of such data. In many cases, address records are captured and/or stored in a free-form or inconsistent manner. This fact complicates the task of accurately matching such addresses to spatially-annotated reference data. In this paper we describe a geocoding system that is based on a comprehensive high-quality geocoded national address database. It uses a learning address parser based on hidden Markov models to segment free-form addresses into components, and a rule-based matching engine to determine the best matches to the reference database.