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Lecture

Urban Planning in African Cities

Related concepts (24)

Urban heat island

An urban heat island (UHI) is an urban area that is significantly warmer than its surrounding rural areas due to human activities. The temperature difference is usually larger at night than during the day, and is most apparent when winds are weak. UHI is most noticeable during the summer and winter. The main cause of the UHI effect is from the modification of land surfaces. A study has shown that heat islands can be affected by proximity to different types of land cover, so that proximity to barren land causes urban land to become hotter and proximity to vegetation makes it cooler.

Density estimation

In statistics, probability density estimation or simply density estimation is the construction of an estimate, based on observed data, of an unobservable underlying probability density function. The unobservable density function is thought of as the density according to which a large population is distributed; the data are usually thought of as a random sample from that population. A variety of approaches to density estimation are used, including Parzen windows and a range of data clustering techniques, including vector quantization.

Urban forest

An urban forest is a forest, or a collection of trees, that grow within a city, town or a suburb. In a wider sense, it may include any kind of woody plant vegetation growing in and around human settlements. As opposed to a forest park, whose ecosystems are also inherited from wilderness leftovers, urban forests often lack amenities like public bathrooms, paved paths, or sometimes clear borders which are distinct features of parks. Care and management of urban forests is called urban forestry.

Kernel density estimation

In statistics, kernel density estimation (KDE) is the application of kernel smoothing for probability density estimation, i.e., a non-parametric method to estimate the probability density function of a random variable based on kernels as weights. KDE answers a fundamental data smoothing problem where inferences about the population are made, based on a finite data sample. In some fields such as signal processing and econometrics it is also termed the Parzen–Rosenblatt window method, after Emanuel Parzen and Murray Rosenblatt, who are usually credited with independently creating it in its current form.